# HG changeset patch
# User iuc
# Date 1528124296 14400
# Node ID 38073c236d71f4bd48a2c764c629fea9d206b3e6
# Parent  afa81ef5f1c34327cf05968c5611074da580a848
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/mageck commit 4478aabdcb10e4787450b1b23944defa7dc38ffe

diff -r afa81ef5f1c3 -r 38073c236d71 mageck_macros.xml
--- a/mageck_macros.xml	Thu Apr 19 05:35:12 2018 -0400
+++ b/mageck_macros.xml	Mon Jun 04 10:58:16 2018 -0400
@@ -6,7 +6,6 @@
     <xml name="requirements">
         <requirements>
             <requirement type="package" version="@VERSION@">mageck</requirement>
-            <requirement type="package" version="1.14.2">numpy</requirement>
             <requirement type="package" version="3.0.1">r-gplots</requirement>
             <requirement type="package" version="1.8_2">r-xtable</requirement>
             <yield/>
@@ -15,7 +14,7 @@
 
     <xml name="version">
         <version_command><![CDATA[
-            echo $(mageck -v )", numpy version" $([python -c "import numpy; numpy.version.version"])", gplots version" $(R --vanilla --slave -e "library(gplots); cat(sessionInfo()\$otherPkgs\$gplots\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", xtable version" $(R --vanilla --slave -e "library(xtable); cat(sessionInfo()\$otherPkgs\$xtable\$Version)" 2> /dev/null | grep -v -i "WARNING: ")
+            echo $(mageck -v )", gplots version" $(R --vanilla --slave -e "library(gplots); cat(sessionInfo()\$otherPkgs\$gplots\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", xtable version" $(R --vanilla --slave -e "library(xtable); cat(sessionInfo()\$otherPkgs\$xtable\$Version)" 2> /dev/null | grep -v -i "WARNING: ")
         ]]></version_command>
     </xml>
 
diff -r afa81ef5f1c3 -r 38073c236d71 mageck_test.xml
--- a/mageck_test.xml	Thu Apr 19 05:35:12 2018 -0400
+++ b/mageck_test.xml	Mon Jun 04 10:58:16 2018 -0400
@@ -160,8 +160,18 @@
             <param name="count_table" value="in.test.sample.txt" ftype="tabular" />
             <param name="treatment_id" value="HL60_final,KBM7_final" />
             <param name="control_id" value="HL60_initial,KBM7_initial" />
-            <output name="gene_summary" file="out.test.gene_summary.txt"/>
-            <output name="sgrna_summary" file="out.test.sgrna_summary.txt"/>
+            <output name="gene_summary">
+                <assert_contents>
+                    <has_text_matching expression="id\tnum\tneg|score\tneg|p-value\tneg|fdr\tneg|rank\tneg|goodsgrna\tneg|lfc\tpos|score\tpos|p-value\tpos|fdr\tpos|rank\tpos|goodsgrna\tpos|lfc" />
+                    <has_text_matching expression="ACD.*10.*0.9996.*-0.0296" />
+                </assert_contents>
+            </output>
+            <output name="sgrna_summary">
+                <assert_contents>
+                    <has_text_matching expression="sgrna\tGene\tcontrol_count\ttreatment_count\tcontrol_mean\ttreat_mean\tLFC\tcontrol_var\tadj_var\tscore\tp.low\tp.high\tp.twosided\tFDR\thigh_in_treatment" />
+                    <has_text_matching expression="ADCK3_m227149144.*ADCK3.*263.11.*189.27.*False" />
+                </assert_contents>
+            </output>
         </test>
         <!-- Ensure MAGeCK's additional outputs works -->
         <test expect_num_outputs="7">
@@ -172,13 +182,35 @@
             <param name="normcounts" value="True" />
             <param name="pdfreport" value="True" />
             <param name="rfilesOpt" value="True" />
-            <output name="gene_summary" file="out.test.gene_summary.txt"/>
-            <output name="sgrna_summary" file="out.test.sgrna_summary.txt"/>
+            <output name="gene_summary">
+                <assert_contents>
+                    <has_text_matching expression="id\tnum\tneg|score\tneg|p-value\tneg|fdr\tneg|rank\tneg|goodsgrna\tneg|lfc\tpos|score\tpos|p-value\tpos|fdr\tpos|rank\tpos|goodsgrna\tpos|lfc" />
+                    <has_text_matching expression="ACD.*10.*0.9996.*-0.0296" />
+                </assert_contents>
+            </output>
+            <output name="sgrna_summary">
+                <assert_contents>
+                    <has_text_matching expression="sgrna\tGene\tcontrol_count\ttreatment_count\tcontrol_mean\ttreat_mean\tLFC\tcontrol_var\tadj_var\tscore\tp.low\tp.high\tp.twosided\tFDR\thigh_in_treatment" />
+                    <has_text_matching expression="ADCK3_m227149144.*ADCK3.*263.11.*189.27.*False" />
+                </assert_contents>
+            </output>
+            <output name="log">
+                <assert_contents>
+                    <has_text_matching expression="Welcome to MAGeCK" />
+                </assert_contents>
+            </output>
+            <output name="rscript">
+                <assert_contents>
+                    <has_text_matching expression="output_summary.tex" />
+                </assert_contents>
+            </output>
+            <output name="rnwfile">
+                <assert_contents>
+                    <has_text_matching expression="This is a template file for Sweave used in MAGeCK" />
+                </assert_contents>
+            </output>
+            <output name="plots" file="out.test.plots.pdf" compare="sim_size"/>
             <output name="normcounts" file="out.test.normalized.txt"/>
-            <output name="log" file="out.test.log.txt" compare="sim_size"/>
-            <output name="plots" file="out.test.plots.pdf" compare="sim_size"/>
-            <output name="rscript" file="out.test.R" />
-            <output name="rnwfile" file="output_summary.Rnw" />
         </test>
     </tests>
 
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/in.mle.sgrnaeff
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/in.mle.sgrnaeff	Mon Jun 04 10:58:16 2018 -0400
@@ -0,0 +1,100 @@
+CATGGCCATGGGCACCCGCC	INO80B_m74682554	INO80B	-0.005239
+AGGAGCTGCACCGCCACGCC	NHS_p17705966	NHS	-0.018779
+AACCACCAGCTGGTCCCGCC	MED14_m40594623	MED14	-0.018132
+CAGCAACAGCCACCGCCATT	MX2_p42748920	MX2	-0.012714
+CAGGCCTCACCTGCACCGCC	SH3GLB2_p131790360	SH3GLB2	-0.072219
+ATGATTCTTCACCTGCCGCT	RECQL_m21644465	RECQL	-0.106925
+CTGCACCAGCCATATCGCGC	CCDC170_p151815274	CCDC170	-0.015925
+ACGTCTCCGCCAGCCACTCC	MEIS2_p37388538	MEIS2	-0.061877
+AAGGTGGCATGGACCCGCCA	NTRK2_p87285677	NTRK2	0.062009
+GCTTCAAAGCACCCGCCGCC	AIFM1_p129299590	AIFM1	-0.116716
+AGGACGAGGGCCGCCGTGAC	PIN1_m9949250	PIN1	0.084799
+GGATTTCAGAACCGCCTGTG	SNTG1_p51306800	SNTG1	-0.002402
+CGCTGCCGTTCCCGCCGCTC	PHLPP1_m60383095	PHLPP1	-0.065516
+TCTTCATCACGCCGCCCGCC	DIS3_p73355915	DIS3	-0.178900
+TAATCCTTGCCCGCCATGTC	APLF_p68694848	APLF	-0.125824
+CGAGGAAGGCGAGAACCGCC	HNRPLL_m38829641	HNRPLL	0.208905
+TAATGATAAGCAGACCCGCC	SCRN1_m29994913	SCRN1	-0.042915
+GGAGCCGCCGCCGCCATATC	RNF11_p51702529	RNF11	-0.022538
+AAGAGCGCACCTGCCACTGG	HIST3H3_m228612925	HIST3H3	0.041563
+ACAGATGTCCAGCAACCGCC	SETD1B_p122243019	SETD1B	-0.022566
+GAACAAAGAACCGCCGGCGC	TMEM165_p56262481	TMEM165	-0.068287
+GGAACAGAAGCTGTCCCGCC	MAP2K7_p7968846	MAP2K7	-0.001348
+ACGTTCCCGCCGCCGCCGTT	TAF5_m105127878	TAF5	-0.075037
+TTATGCCCTGAACACCCGCC	FAM184B_m17711242	FAM184B	-0.060696
+TCTCCTCCACCCGCCGGGTC	ZW10_p113644288	ZW10	-0.132037
+AAAGTTGCAGCCGCCACTGC	SNAPC3_m15422944	SNAPC3	-0.064822
+GAGCTGAGCCTGCCACGCCG	MAST3_p18218399	MAST3	0.097420
+TAGTCTTGTCCCTACCCGCC	DDX31_m135545488	DDX31	-0.182423
+GACGGCGTGCAGCTCCCGCC	EIF4EBP1_p37888163	EIF4EBP1	0.027745
+CAGGACTACCGCCATATCCC	ZNF35_m44694064	ZNF35	-0.065333
+CGGCCGCTACCGCCGCTACC	DSTYK_m205180551	DSTYK	-0.121018
+GACCTTTGGAGTTCATCAAA	TCF12_p57524508	TCF12	0.075702
+TGGGAAGGCGTCCGACCGCC	C21orf33_m45553648	C21orf33	-0.011841
+CCGGGACCGCCACTGCCGAG	SYN1_m47478971	SYN1	0.168301
+CTCGGACGAGCGGCTGGGCC	TXNRD3_p126373683	TXNRD3	-0.003583
+TCTCATTGACCGGACCCGCC	SNRNP200_m96970554	SNRNP200	-0.101923
+GGAGCGCACCCGCCGCGGAA	C9orf69_p139008710	C9orf69	0.127616
+TCGCTTCCGCCGCCACTGCC	NPAS1_m47524322	NPAS1	-0.003985
+GGATGCGACCGCCACTATCG	SMARCA1_m128657283	SMARCA1	0.063867
+CCCAAATGCGCCCGCCTGCA	WDR77_m111991712	WDR77	0.023250
+CTCACGAGCCGAGCCTCTCG	PNCK_p152938458	PNCK	0.124364
+CAACGAAGACGCTCCCGCCT	SENP3_m7466474	SENP3	0.024559
+GCCGCCGAAACCGCCACGGA	RFWD2_p176175980	RFWD2	0.003107
+GCTCACCTTCACCGCCGCTC	C9orf9_p135759399	C9orf9	-0.134392
+TGAAACCCTGGCCCGCCAGT	C4orf22_p81283909	C4orf22	-0.016684
+GGGCTGCTCCTGCTTCCTCC	ZNF827_p146859532	ZNF827	-0.094275
+GTGGTGCCACCCGCCGTGGC	EPM2A_m146056593	EPM2A	-0.112144
+GTGTATCCAGTGCCTGCTCT	TMEM175_m941547	TMEM175	-0.249848
+GCAGCGCCAGTCCCGCCAGC	DUSP23_m159750934	DUSP23	-0.134388
+AGTGGCTGCTCCCGCCATAC	YLPM1_m75230212	YLPM1	-0.007259
+AGCACCACCAGCTGTTGCTC	RAB1A_p65315689	RAB1A	-0.032893
+AGTGGTTCCCGCCGCAGGAC	GSG2_p3627326	GSG2	0.033727
+CAGAACCCGCCACTTGTCCA	RPL10L_p47120303	RPL10L	0.026564
+GACCATGAACCGCAGCCGCC	PWP1_p108079672	PWP1	-0.090761
+AAGGAGGATAGAGGCCCGCC	KIAA1755_p36874470	KIAA1755	0.005710
+TCTCCAATACCTGCCGATTC	JAZF1_m28220148	JAZF1	-0.153656
+CGTTCACCCGCCGGGCCTTT	PAXBP1_p34143951	PAXBP1	-0.074149
+GGTAGCCAGGGCACCCGCCA	BMPR2_m203242221	BMPR2	0.152337
+GCCTCCACCGCCCGCCGCTT	UXT_p47518291	UXT	-0.042766
+ACTCTCACCGCCGTAGGTGC	POU2AF1_p111229504	POU2AF1	-0.026811
+CTGGACGACCGCCACGACAG	NFKBIA_m35873755	NFKBIA	0.076979
+CTGAATCCCGCCACACTCTC	LPIN1_p11905730	LPIN1	-0.025123
+TACCGGAGCCGCGACCGCCT	ZNF746_p149194600	ZNF746	0.173225
+TTTATCTGCATTTCCATGAC	SYNPO2_p119810206	SYNPO2	-0.140214
+TCACTCCTGAACAGACTTCT	LTA4H_m96421257	LTA4H	-0.030193
+GATGGTCGCCGCCTGCCGCT	TMEM87B_p112813167	TMEM87B	0.036418
+CTGGATGGAGCCGCCGCTCC	OCRL_p128674412	OCRL	-0.150457
+AGTGGACATCCGCCATAACA	RPL18_m49121112	RPL18	0.056941
+GACAAGGCGAAACCGCCGCC	PSMD3_p38137259	PSMD3	0.030238
+AGCCACACCGAGAACCGCCG	WBP2NL_p42394837	WBP2NL	0.131323
+CGGCCTCCAGCCGCCACTTG	LUZP1_m23420706	LUZP1	-0.002771
+ATTCTCTTTGGAGCCGTGAG	STRADB_p202340406	STRADB	-0.001874
+GCCTCTTCTCCGCGCTCTCG	FOXL2_p138665341	FOXL2	-0.150694
+CTGCACCCAACCGCCGGCAC	ADRA1A_m26722428	ADRA1A	-0.096324
+TCACCCAGCCATACCAGCCG	RBBP9_p18477729	RBBP9	0.064510
+AGTCCTCCCGCCGCTGCAGC	IMP3_p75932376	IMP3	-0.091312
+TGGTGTCTCATCTCCTTGCC	RABGAP1_m125719426	RABGAP1	-0.201102
+TTTAGGAGCTTCTCCAAATT	RARS_p167915662	RARS	-0.079412
+ACAGGCCCGCCACGTCCGTC	RPRM_p154335036	RPRM	0.003556
+GGACATGAAGGAGTCCCGCC	OBFC1_m105677228	OBFC1	0.039483
+GAGCTGCGGGACCCGCCACC	TINF2_p24711504	TINF2	-0.010376
+TTCTTCCAGAGAGAACTCTA	ZNF565_p36686010	ZNF565	0.131393
+CACATTCTCCACCCGCCGAG	CCDC78_p776304	CCDC78	0.076491
+TAGGCGCCCGCCGCTCTTCC	YWHAB_p43530334	YWHAB	-0.008675
+CGCTCCCGCCGCTGCTTCCT	CRX_m48339507	CRX	-0.050130
+AGCGGCACCTACACCCGCCA	EXOSC1_m99205546	EXOSC1	0.090355
+CGCGGTGGGCAAGACGAGCC	RHOU_p228871662	RHOU	0.144042
+TTCTCAAGAAATTCACCGCC	CHMP1B_m11851692	CHMP1B	-0.112913
+CACCGCCACCGCCACGACCA	U2AF1_p44513288	U2AF1	0.080210
+TCCCAACACCCGCCAAGAGA	NET1_p5494387	NET1	0.089082
+TCTGAGCTCCAGGTGCTTCT	PIAS3_p145578085	PIAS3	-0.027557
+TTCGCCCGCCGGCTCCTGCG	CMPK2_m7005801	CMPK2	0.105524
+ACCAGCCAAGATTGCCCGCC	SATB1_m18462362	SATB1	-0.070132
+GACACACCTCGCCCGCCTCC	FOXA3_m46367770	FOXA3	-0.054864
+AAATTCCCAGGAGAAATATA	ZNF627_p11725680	ZNF627	0.090453
+GTCACGGCCGCCCGCCGACA	MLLT4_m168227813	MLLT4	0.040685
+AACTGCCTGCACCGCCTCTA	FAM120A_p96214350	FAM120A	0.010379
+TTATGAAAGTATTTCTCTCC	ADNP2_m77891006	ADNP2	-0.159761
+CGCACCCTCACCGCCGGCCT	CD3EAP_m45909967	CD3EAP	-0.028605
+GTGGACCCTCGTGAGCGACC	HSF1_p145515504	HSF1	-0.051256
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/out.count.bam.txt
--- a/test-data/out.count.bam.txt	Thu Apr 19 05:35:12 2018 -0400
+++ b/test-data/out.count.bam.txt	Mon Jun 04 10:58:16 2018 -0400
@@ -1,4 +1,4 @@
-sgRNA	Gene	test1_bam
+sgRNA	Gene	test1.bam
 s_10007	CCNA1	0
 s_10008	CCNA1	0
 s_10027	CCNC	0
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/out.count.fastq.txt
--- a/test-data/out.count.fastq.txt	Thu Apr 19 05:35:12 2018 -0400
+++ b/test-data/out.count.fastq.txt	Mon Jun 04 10:58:16 2018 -0400
@@ -1,4 +1,4 @@
-sgRNA	Gene	test1_fastq_gz
+sgRNA	Gene	test1.fastq.gz
 s_47512	RNF111	1
 s_24835	HCFC1R1	1
 s_14784	CYP4B1	4
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/out.count.txt
--- a/test-data/out.count.txt	Thu Apr 19 05:35:12 2018 -0400
+++ b/test-data/out.count.txt	Mon Jun 04 10:58:16 2018 -0400
@@ -1,4 +1,4 @@
-sgRNA	Gene	test1_fastq_gz
+sgRNA	Gene	test1.fastq.gz
 s_47512	RNF111	1
 s_24835	HCFC1R1	1
 s_14784	CYP4B1	4
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/out.count_multi.txt
--- a/test-data/out.count_multi.txt	Thu Apr 19 05:35:12 2018 -0400
+++ b/test-data/out.count_multi.txt	Mon Jun 04 10:58:16 2018 -0400
@@ -1,4 +1,4 @@
-sgRNA	Gene	test1_fastq_gz	test2_fastq_gz
+sgRNA	Gene	test1.fastq.gz	test2.fastq.gz
 s_47512	RNF111	1	0
 s_24835	HCFC1R1	1	0
 s_14784	CYP4B1	4	0
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/out.test.R
--- a/test-data/out.test.R	Thu Apr 19 05:35:12 2018 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,930 +0,0 @@
-pdf(file='output.pdf',width=4.5,height=4.5);
-gstable=read.table('output.gene_summary.txt',header=T)
-# 
-#
-# parameters
-# Do not modify the variables beginning with "__"
-
-# gstablename='__GENE_SUMMARY_FILE__'
-startindex=3
-# outputfile='__OUTPUT_FILE__'
-targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1")
-# samplelabel=sub('.\\w+.\\w+$','',colnames(gstable)[startindex]);
-samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.'
-
-
-# You need to write some codes in front of this code:
-# gstable=read.table(gstablename,header=T)
-# pdf(file=outputfile,width=6,height=6)
-
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-######
-# function definition
-
-plotrankedvalues<-function(val, tglist, ...){
-  
-  plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...)
-  if(length(tglist)>0){
-    for(i in 1:length(tglist)){
-      targetgene=tglist[i];
-      tx=which(names(val)==targetgene);ty=val[targetgene];
-      points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
-      # text(tx+50,ty,targetgene,col=colors[i])
-    }
-    legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors)
-  }
-}
-
-
-
-plotrandvalues<-function(val,targetgenelist, ...){
-  # choose the one with the best distance distribution
-  
-  mindiffvalue=0;
-  randval=val;
-  for(i in 1:20){
-    randval0=sample(val)
-    vindex=sort(which(names(randval0) %in% targetgenelist))
-    if(max(vindex)>0.9*length(val)){
-      # print('pass...')
-      next;
-    }
-    mindiffind=min(diff(vindex));
-    if (mindiffind > mindiffvalue){
-      mindiffvalue=mindiffind;
-      randval=randval0;
-      # print(paste('Diff: ',mindiffvalue))
-    }
-  }
-  plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...)
-  
-  if(length(targetgenelist)>0){
-    for(i in 1:length(targetgenelist)){
-      targetgene=targetgenelist[i];
-      tx=which(names(randval)==targetgene);ty=randval[targetgene];
-      points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
-      text(tx+50,ty,targetgene,col=colors[i])
-    }
-  }
-  
-}
-
-
-
-
-# set.seed(1235)
-
-
-
-pvec=gstable[,startindex]
-names(pvec)=gstable[,'id']
-pvec=sort(pvec);
-
-plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel))
-
-# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel))
-
-
-pvec=gstable[,startindex+1]
-names(pvec)=gstable[,'id']
-pvec=sort(pvec);
-
-plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel))
-
-# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel))
-
-
-
-# you need to write after this code:
-# dev.off()
-
-
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(561.4907165816957,824.0396348113272,428.37415340969943,579.047491896501),c(3424.79939695118,3818.2871009576584,1992.3498917052,690.0506672205338),c(846.6456878299913,985.6508562937211,335.0024675413113,415.97581680707134),c(2432.636481525409,2122.257249136931,1067.465489792653,155.6333179800872),c(1308.1851773762019,2186.1913587343615,1482.5909580453515,997.3120339679854),c(405.68439208520414,268.16807081144486,170.34023773287015,109.85881269182627),c(640.8637498157573,559.4234589775174,711.6436598617687,632.2603542941043),c(946.5969148654764,470.6260845366416,663.0651476194316,457.74505288260946),c(246.9383256170808,177.59474888175154,28.39003962214503,0.0),c(568.8400715107754,612.7018836420428,564.0154538266146,270.64176251684285))
-targetgene="ACIN1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(2484.0819660289676,2349.578527705573,2172.7843657481662,910.9126552363929),c(992.1629154257711,1005.1862786707138,743.8190381001997,200.26346063614164),c(1267.0287897733551,1156.1418152202027,251.09412821363824,42.34141739164138),c(1500.738276518092,1315.977089213779,800.5991173444897,1476.2277955464156),c(1925.5309914189038,2054.7712445618654,194.94493873872918,235.16652091844063),c(351.29916561001374,781.4168950797068,227.75120674654121,624.2498158686586),c(1719.74905340467,1006.9622261595313,356.45271970026533,222.0063506480656),c(903.9706562768137,1445.6212558974576,1482.5909580453515,1055.1023468944147),c(651.152846716469,1081.552020689867,576.0023594448536,1072.2677863775127),c(285.1549712482957,408.46792242802854,99.0496937928171,44.630142656054424))
-targetgene="ACTR8"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(301.3235520922712,657.1005708624807,228.38209651592223,137.32351586478285),c(1142.0897559789987,1099.311495578042,112.92926871919911,100.70391163417409),c(789.3207193831689,671.3081507730209,723.6305654800077,588.7745742702564),c(392.45555321286054,412.0198174056636,334.37157777193033,213.99581222261992),c(2009.3136376104133,2235.917888421252,2437.1271791188055,1937.9781176417478),c(1071.5359486598327,406.69197493921104,645.4002340767636,349.602784139093),c(61.7345814042702,218.44154112455442,614.4866353770946,452.5954210376801),c(651.152846716469,879.0940069646701,237.21455328725622,18.88198343140764),c(1625.6773103124485,1410.1023061211074,2146.286995434164,1986.613529510525),c(1053.8974968300413,882.6459019423052,106.6203710253891,105.85354347910344))
-targetgene="AHCY"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(1268.498660759171,1411.8782536099247,1136.2324746551822,603.6512884889412),c(327.78122983695846,454.642557137284,51.73296108924205,24.031615276336996),c(132.28838872343613,241.5288584791821,123.02350502929512,65.80085135187511),c(495.34652221997754,586.0626713097802,279.4841678357833,243.74924065998954),c(1009.8013672555626,1102.8633905556771,1237.174837756142,1004.7503910773278),c(877.5129785321263,715.7068379934587,538.1489732819936,594.496387431289),c(1594.8100196103135,1108.1912330221296,605.6541786057605,127.59643349102738),c(314.5523909646148,252.1845434120872,88.95545748272109,359.9020478289517),c(512.984974049769,269.94401830026237,205.67006481820619,126.45207085882086),c(761.3931706526657,475.9539270030942,559.5992254409475,596.7851126957021))
-targetgene="ACLY"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(659.9720726313648,809.832054900787,880.7221180558769,802.1982051767731),c(724.6463960072668,1086.8798631563195,695.2405258578626,307.26136674745163),c(836.3565909292796,1289.3378768815162,468.75109865008346,177.94838930811443),c(367.46774645398926,571.85509139924,300.30353022535627,116.72498848506541),c(518.8644579930328,632.2373060190355,627.7353205340956,308.9779106957614),c(405.68439208520414,259.28833336735727,324.27734146183434,166.5047629860492),c(2096.0360257735547,1960.6460276545372,1573.4390848362154,629.9716290296913),c(277.8056163192159,435.1071347602913,182.32714335110919,0.0),c(995.1026573974029,477.7298744919117,728.0467938656747,275.21921304566894),c(2185.6981559083283,1482.9161531626255,1741.8866532609427,1862.4501839161173))
-targetgene="AATF"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(640.8637498157573,602.0461987091378,307.2433176885473,192.82510352679924),c(354.23890758164566,280.5997032331675,204.4082852794442,275.79139436177223),c(779.0316224824572,932.3724316291956,778.5179754161547,905.1908420753603),c(624.6951689717818,554.0956165110648,370.96318439602834,558.4489645167836),c(1133.270530064103,1394.1187787217498,639.0913363829536,1131.2024619361487),c(423.32284391499564,412.0198174056636,224.59675789963623,426.84726181303336),c(296.91393913482335,829.3674772777797,489.5704610396565,1233.0507362025292),c(684.959879390236,546.9918265557948,394.30610586312537,566.4595029422292),c(440.96129574478715,630.461358530218,434.6830511035094,457.1728715665062),c(1108.2827233052317,1969.5257650986248,1066.2037102538911,1333.7546478367033))
-targetgene="AGBL5"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(196.96271209933826,301.9110730989776,423.9579250240324,34.33087896619571),c(1106.8128523194157,1056.6887558464218,1743.1484327997048,807.3478370217025),c(748.1643317803222,488.3855594248168,239.73811236478022,477.77139894622366),c(1095.053884432888,882.6459019423052,837.8216137379688,365.05167967388104),c(677.6105244611563,316.11865300951774,613.8557456077136,819.3636446598709),c(1078.8853035889126,1609.008424868669,348.88204246769334,193.96946615900578),c(1437.533824128006,1095.759600600407,320.4920028455483,161.35513114111984),c(845.1758168441753,660.6524658401157,541.3034221288985,640.8430740356532),c(551.2016196809839,740.570102836904,1103.42620664737,622.5332719203489),c(601.1772331987264,900.4053768304803,735.6174710982467,754.1349746240991))
-targetgene="AHCTF1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(487.9971672908978,367.6211301852257,312.2904358435953,441.15179471561487),c(358.6485205390935,394.2603425174884,593.0363832181406,268.35303725242983),c(1743.266989177725,1980.1814500315297,837.1907239685878,281.5132075228048),c(1597.7497615819454,1465.1566782744503,1065.57282048451,992.7345834391593),c(119.05954985109253,378.2768151181308,185.48159219801417,128.7407961232339),c(986.2834314825072,745.8979453033566,328.0626800781203,302.11173490252224),c(523.2740709504807,694.3954681276485,336.89513684945433,597.9294753279087),c(1562.4728579223624,763.6574201915316,422.0652557158894,220.28980669975581),c(30.8672907021351,179.37069637056908,238.47633282601822,184.81456510135357),c(339.5401977234861,447.5387671820139,310.3977665354523,205.98527379717427))
-targetgene="ABT1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(492.4067802483456,221.99343610218943,309.7668767660713,102.99263689858714),c(243.9985836454489,239.7529109903646,130.59418226186713,174.51530141149487),c(734.9354929079785,673.0840982618383,620.7955330709046,470.9052231529845),c(1074.4756906314647,950.1319065173708,1100.902647569846,743.8357109342404),c(702.5983312200275,1010.5141211371663,1291.4313579229083,1017.3383800315995),c(1647.7253750996879,760.1055252138966,685.7771793171477,608.2287390177673),c(951.0065278229242,864.8864270541301,606.9159581445226,769.0116888427839),c(435.0818118015233,435.1071347602913,275.69882921949727,339.8757017653375),c(89.66213013477338,209.56180368046682,208.8245136651112,304.4004601669353),c(1328.7633711776252,1571.7135276035012,1122.983789498181,1356.6419004808338))
-targetgene="ADIRF"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(216.0710349149457,289.479440677255,192.42137966120518,498.36992632594104),c(1127.391046120839,1198.764554951823,371.5940741654094,370.2013115188104),c(1111.2224652768637,1038.9292809582466,948.227323379644,922.3562815584581),c(1164.137820766238,1204.0923974182756,1686.9992433247955,2089.033985093009),c(48.505742531926586,248.63264843445216,665.5887066969557,248.8988725049189),c(501.2260061632414,387.1565525622184,436.5757204116524,314.69972385679404),c(1975.5066049366465,1797.2588586833258,1628.3264947723626,1289.6966864967521),c(213.13129294331378,376.5008676293133,404.4003421732214,482.921030791153),c(2012.2533795820452,1989.0611874756173,1064.3110409457481,431.9968936579627),c(264.57677744687226,353.4135502746856,442.25372833608145,191.6807408945927))
-targetgene="ABCF1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# 
-#
-# parameters
-# Do not modify the variables beginning with "__"
-
-# gstablename='__GENE_SUMMARY_FILE__'
-startindex=9
-# outputfile='__OUTPUT_FILE__'
-targetgenelist=c("ACRC","AGAP3","ADCK4","AHRR","ADRBK1","ADK","ADCK1","ADARB2","ACSS2","ADNP")
-# samplelabel=sub('.\\w+.\\w+$','',colnames(gstable)[startindex]);
-samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.'
-
-
-# You need to write some codes in front of this code:
-# gstable=read.table(gstablename,header=T)
-# pdf(file=outputfile,width=6,height=6)
-
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-######
-# function definition
-
-plotrankedvalues<-function(val, tglist, ...){
-  
-  plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...)
-  if(length(tglist)>0){
-    for(i in 1:length(tglist)){
-      targetgene=tglist[i];
-      tx=which(names(val)==targetgene);ty=val[targetgene];
-      points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
-      # text(tx+50,ty,targetgene,col=colors[i])
-    }
-    legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors)
-  }
-}
-
-
-
-plotrandvalues<-function(val,targetgenelist, ...){
-  # choose the one with the best distance distribution
-  
-  mindiffvalue=0;
-  randval=val;
-  for(i in 1:20){
-    randval0=sample(val)
-    vindex=sort(which(names(randval0) %in% targetgenelist))
-    if(max(vindex)>0.9*length(val)){
-      # print('pass...')
-      next;
-    }
-    mindiffind=min(diff(vindex));
-    if (mindiffind > mindiffvalue){
-      mindiffvalue=mindiffind;
-      randval=randval0;
-      # print(paste('Diff: ',mindiffvalue))
-    }
-  }
-  plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...)
-  
-  if(length(targetgenelist)>0){
-    for(i in 1:length(targetgenelist)){
-      targetgene=targetgenelist[i];
-      tx=which(names(randval)==targetgene);ty=randval[targetgene];
-      points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
-      text(tx+50,ty,targetgene,col=colors[i])
-    }
-  }
-  
-}
-
-
-
-
-# set.seed(1235)
-
-
-
-pvec=gstable[,startindex]
-names(pvec)=gstable[,'id']
-pvec=sort(pvec);
-
-plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel))
-
-# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \\n',samplelabel))
-
-
-pvec=gstable[,startindex+1]
-names(pvec)=gstable[,'id']
-pvec=sort(pvec);
-
-plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel))
-
-# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \\n',samplelabel))
-
-
-
-# you need to write after this code:
-# dev.off()
-
-
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(461.5394895462105,502.5931393353569,445.40817718298644,889.1697652244688),c(76.43329126242978,90.5733219296933,447.30084649112945,357.0411412484354),c(258.6972935036084,685.515730683561,533.7327448963265,560.7376897811967),c(232.23961575892122,681.9638357059259,275.69882921949727,467.47213525636494),c(1393.4376945535273,1472.2604682297203,1039.706339939889,532.7008052921368),c(2395.88970688001,2441.927797124084,2462.9936596634266,2461.5240218762324),c(495.34652221997754,605.5980936867728,1159.575396122279,1617.5565806239213),c(682.0201374186041,822.2636873225097,1572.1773052974536,1333.7546478367033),c(961.2956247236359,1097.5355480892247,959.5833392285019,905.1908420753603),c(1940.2297012770634,1289.3378768815162,942.5493154552149,1103.737758763192))
-targetgene="ACRC"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(1387.5582106102636,1120.6228654438523,1214.4628060584262,1111.1761158725344),c(388.0459402554127,509.69692929062694,933.0859689144999,750.1297054113762),c(326.3113588511425,635.7892009966705,960.8451187672639,615.6670961271097),c(1328.7633711776252,1038.9292809582466,1346.3187678590552,1596.3858719281006),c(352.7690365958297,234.42506852391205,310.3977665354523,429.1359870774464),c(693.7791053051318,678.4119407282909,784.1959833405838,895.4637597016048),c(837.8264619150956,719.2587329710938,374.74852301231437,993.8789460713658),c(365.99787546817333,369.3970776740432,333.74068800254935,746.6966175147567),c(707.0079441774753,635.7892009966705,837.1907239685878,1465.3563505404536),c(486.5272963050818,673.0840982618383,784.8268731099647,734.6808098765882))
-targetgene="AGAP3"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(830.4771069860158,864.8864270541301,1349.4732167059603,740.974804353724),c(1481.6299537024847,1994.38902994207,2044.082852794442,1810.9538654668238),c(1234.6916280854039,1299.9935618144214,1357.6747837079133,2232.6514954349277),c(224.89026082984142,188.25043381465665,700.2876440129107,81.24974688666317),c(812.8386551562243,845.3510046771374,946.334654071501,999.6007592323984),c(1978.4463469082782,1751.0842239740703,2659.2003779409174,2851.1794981425537),c(565.9003295391435,776.0890526132542,878.1985589783528,445.72924524444096),c(680.5502664327881,534.5601941340722,550.7667686696135,1025.9210997731484),c(161.68580843975528,333.87812789769293,275.0679394501163,465.18340999195186),c(2523.768482645998,2445.4796921017187,2153.226782897355,1516.8526689897471))
-targetgene="ADCK4"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(345.4196816667499,163.38716897121142,474.42910657451245,481.2044868428432),c(415.9734889859159,372.9489726516783,212.6098522813972,349.03060282298975),c(1.469870985815957,83.46953197442323,0.0,62.9399447713588),c(351.29916561001374,150.9555365494888,288.9475143764983,416.54799812317464),c(561.4907165816957,170.49095892648148,199.3611671243962,411.97054759434855),c(251.34793857452865,221.99343610218943,1564.6066280648815,1502.5481360871656),c(736.4053638937945,893.3015868752103,1114.782222496228,459.46159683091923),c(338.07032673767014,607.3740411755903,378.5338616286004,65.22867003577186),c(1230.2820151279561,525.6804566899846,837.1907239685878,945.2435342025885))
-targetgene="AHRR"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(371.87735941143717,877.3180594758527,2395.4884543396593,1564.9158995424211),c(1109.7525942910477,1138.3823403320275,970.308465307979,999.0285779162951),c(1462.5216308868773,1209.420239884728,1537.4783679814984,1519.14139425416),c(586.4785233405669,987.4268037825386,743.8190381001997,1312.0117578247794),c(1018.6205931704583,717.4827854822763,1070.619938639558,1144.3626322065236),c(1269.9685317449869,1212.9721348623632,1591.1039983788835,1624.9949377332637),c(1321.4140162485455,1795.4829111945082,1478.8056194290655,1237.056005415252),c(908.3802692342615,832.9193722554148,1639.6825106212207,1268.5259778009315),c(923.078979092421,758.3295777250792,1479.4365091984464,1275.964334910274),c(680.5502664327881,634.013253507853,318.5993335374053,631.1159916618979))
-targetgene="ADRBK1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(1472.810727787589,1829.225913482041,1263.0413183007631,1315.444845721399),c(208.7216799858659,65.71005708624807,292.1019632234033,350.17496545519623),c(1011.2712382413785,1166.7975001531076,652.9709113093356,860.5606994193058),c(557.0811036242477,685.515730683561,875.0441101314478,1019.6271052960126),c(363.0581334965414,825.8155823001447,736.8792506370087,349.602784139093),c(1505.14788947554,451.09066215964896,653.6018010787167,991.0180394908496),c(198.43258308515422,28.41515982108025,249.83234867487624,114.43626322065236),c(438.02155377315523,74.58979453033565,254.87946682992424,231.16125170571777),c(804.0194292413286,472.4020320254591,1336.2245315489592,1203.2973077651598),c(454.19013461713075,490.1615069136343,896.4943622904019,685.4732166917076))
-targetgene="ADK"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(662.9118146029966,1008.7381736483488,1101.533537339227,1694.8010582978616),c(1547.7741480642028,1965.9738701209897,1869.9572764452857,2353.9539344488194),c(1459.5818889152454,1179.2291325748304,1296.4784760779562,1222.1792911965672),c(1193.5352404825571,1355.0479339677643,1622.0175970785526,1905.9359639399652),c(868.6937526172306,701.4992580829187,720.4761166331027,603.6512884889412),c(798.1399452980647,768.9852626579842,1478.8056194290655,1756.0244591209105),c(1168.5474337236858,907.5091667857504,879.4603385171149,977.8578692204745),c(809.8989131845924,687.2916781723785,678.8373918539567,865.7103312642352),c(1246.4505959719315,753.0017352586266,1301.5255942330043,1264.5207085882087),c(826.0674940285679,797.4004224790644,977.8791425405509,2066.7189137649816))
-targetgene="ADCK1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(1863.7964100146337,1585.9211075140413,1761.4442361117538,1464.211987908247),c(742.2848478370584,598.4943037315028,943.8110949939769,820.5080072920774),c(1568.3523418656262,2083.1864043829455,1810.6536381234716,1887.6261618246608),c(1018.6205931704583,513.248824268262,679.4682816233377,824.5132765048003),c(1140.6198849931827,1191.6607649965529,880.0912282864958,977.8578692204745),c(135.22813069506805,118.98848175077354,351.40560154521734,399.95473995618005),c(665.8515565746286,701.4992580829187,986.7115993118849,746.6966175147567),c(418.9132309575478,300.1351256101601,376.6411923204574,645.4205245644794),c(561.4907165816957,543.4399315781598,881.9838975946388,580.7640358448108),c(442.4311667306031,229.0972260574595,395.5678854018874,651.142337725512))
-targetgene="ADARB2"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(734.9354929079785,358.74139274113816,541.9343118982795,378.7840312603593),c(595.2977492554626,591.3905137762326,1061.787481868224,887.4532212761591),c(1655.0747300287676,943.0281165621008,1069.358159100796,2038.1098479598186),c(626.1650399575977,884.4218494311227,517.3296108924205,858.2719741548927),c(680.5502664327881,747.673892792174,533.1018551269456,1016.194017399393),c(662.9118146029966,777.8650001020718,864.9498738213518,787.3214909580882),c(880.4527205037583,621.5816210861304,671.8976043907657,1040.7978139918332),c(94.07174309222125,447.5387671820139,711.6436598617687,927.5059134033875),c(399.80490814194036,806.280159923152,1147.58849050404,1059.1076161071376),c(698.1887182625796,531.0082991564371,504.0809257354195,347.8862401907832))
-targetgene="ACSS2"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(408.62413405683606,523.9045092011671,483.89245311522745,701.494293542599),c(1805.0015705819953,1434.9655709645526,1712.2348341000356,2152.546111180471),c(3017.64513388016,2642.609863360463,1834.6274493599499,3573.2723190648703),c(1649.1952460855039,783.1928425685244,773.4708572611067,1332.0381038883936),c(959.82575373782,1397.6706736993847,1429.5962174173474,2811.126806015325),c(495.34652221997754,301.9110730989776,336.89513684945433,555.015876620164),c(1491.9190506031964,1331.9606166131366,2087.614246881731,1983.1804416139055),c(429.2023278582595,889.7496918975753,567.8007924429005,1132.9190058844583),c(427.7324568724435,573.6310388880576,655.4944703868597,899.4690289143276),c(690.8393633334998,767.2093151691668,1040.33722970927,993.3067647552625))
-targetgene="ADNP"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-dev.off()
-Sweave("output_summary.Rnw");
-library(tools);
-
-texi2dvi("output_summary.tex",pdf=TRUE);
-
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/out.test.log.txt
--- a/test-data/out.test.log.txt	Thu Apr 19 05:35:12 2018 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,109 +0,0 @@
-INFO  @ Mon, 26 Mar 2018 08:37:53: Parameters: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/bin/mageck test -k /private/var/folders/zn/m_qvr9zd7tq0wdtsbq255f8xypj_zg/T/tmpTX65kA/files/000/dataset_4.dat -t HL60_final,KBM7_final -c HL60_initial,KBM7_initial -n output --normcounts-to-file --pdf-report --norm-method median --adjust-method fdr --sort-criteria neg --remove-zero both --gene-lfc-method median 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Welcome to MAGeCK v0.5.7. Command: test 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Loading count table from /private/var/folders/zn/m_qvr9zd7tq0wdtsbq255f8xypj_zg/T/tmpTX65kA/files/000/dataset_4.dat  
-INFO  @ Mon, 26 Mar 2018 08:37:53: Processing 1 lines.. 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Parsing error in line 1 (usually the header line). Skip this line. 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Loaded 999 records. 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Loading R template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template.RTemplate. 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Loading R template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template_indvgene.RTemplate. 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Loading Rnw template file: /Users/doylemaria/miniconda3/envs/mulled-v1-9ee130591ca78526e74a59d8d6dc03cb7db20645470975762936caeac62972dc/lib/python3.6/site-packages/mageck/plot_template.Rnw. 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Setting up the visualization module... 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Given sample labels: HL60_final,KBM7_final 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Converted index: 2 3 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Treatment samples:HL60_final,KBM7_final 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Treatment sample index:2,3 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Given sample labels: HL60_initial,KBM7_initial 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Converted index: 0 1 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Control samples:HL60_initial,KBM7_initial 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Control sample index:0,1 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Initial (total) size factor: 1.6666455325878438 2.027372749328462 0.7198064117880387 0.6589869375844738 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Median factor: 1.469870985815957 1.7759474888175155 0.6308897693810006 0.5721813161032618 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Final size factor: 1.469870985815957 1.7759474888175155 0.6308897693810006 0.5721813161032618 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Writing normalized read counts to output.normalized.txt 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Adjusted model: 1.1175084644498339	3.4299551007579927 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Raw variance calculation: 0.5 for control, 0.5 for treatment 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Adjusted variance calculation: 0.3333333333333333 for raw variance, 0.6666666666666667 for modeling 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Use qnorm to reversely calculate sgRNA scores ... 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: lower test FDR cutoff: 0.3283283283283283 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: higher test FDR cutoff: 0.34534534534534533 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Running command: RRA -i output.plow.txt -o output.gene.low.txt -p 0.3283283283283283 --skip-gene NA --skip-gene na  
-INFO  @ Mon, 26 Mar 2018 08:37:53: Command message: 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Welcome to RRA v 0.5.7. 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Skipping gene NA for permutation ... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Skipping gene na for permutation ... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Reading input file... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Summary: 999 sgRNAs, 100 genes, 1 lists; skipped sgRNAs:0 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Computing lo-values for each group... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Computing false discovery rate... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Increase the number of permutations to 1001 to get precise p values. To avoid this, use the --permutation option. 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Permuting genes with 9 sgRNAs... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Permuting genes with 10 sgRNAs... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Number of genes under FDR adjustment: 100 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Saving to output file... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   RRA completed. 
-INFO  @ Mon, 26 Mar 2018 08:37:53:    
-INFO  @ Mon, 26 Mar 2018 08:37:53: End command message. 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Running command: RRA -i output.phigh.txt -o output.gene.high.txt -p 0.34534534534534533 --skip-gene NA --skip-gene na  
-INFO  @ Mon, 26 Mar 2018 08:37:53: Command message: 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Welcome to RRA v 0.5.7. 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Skipping gene NA for permutation ... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Skipping gene na for permutation ... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Reading input file... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Summary: 999 sgRNAs, 100 genes, 1 lists; skipped sgRNAs:0 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Computing lo-values for each group... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Computing false discovery rate... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Increase the number of permutations to 1001 to get precise p values. To avoid this, use the --permutation option. 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Permuting genes with 9 sgRNAs... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Permuting genes with 10 sgRNAs... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Number of genes under FDR adjustment: 100 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   Saving to output file... 
-INFO  @ Mon, 26 Mar 2018 08:37:53:   RRA completed. 
-INFO  @ Mon, 26 Mar 2018 08:37:53:    
-INFO  @ Mon, 26 Mar 2018 08:37:53: End command message. 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Sorting the merged items by negative selection... 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Loading top 10 genes from output.gene.low.txt: ACIN1,ACTR8,AHCY,ACLY,AATF,AGBL5,AHCTF1,ABT1,ADIRF,ABCF1 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Column index:3 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Loading top 10 genes from output.gene.high.txt: ACRC,AGAP3,ADCK4,AHRR,ADRBK1,ADK,ADCK1,ADARB2,ACSS2,ADNP 
-DEBUG @ Mon, 26 Mar 2018 08:37:53: Column index:9 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Running command: rm output.plow.txt 
-INFO  @ Mon, 26 Mar 2018 08:37:53: Running command: rm output.phigh.txt 
-INFO  @ Mon, 26 Mar 2018 08:37:54: Running command: rm output.gene.low.txt 
-INFO  @ Mon, 26 Mar 2018 08:37:54: Running command: rm output.gene.high.txt 
-INFO  @ Mon, 26 Mar 2018 08:37:54: Running command: cd ./; Rscript output.R 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Command message: 
-INFO  @ Mon, 26 Mar 2018 08:37:59:   null device  
-INFO  @ Mon, 26 Mar 2018 08:37:59:             1  
-INFO  @ Mon, 26 Mar 2018 08:37:59:   Writing to file output_summary.tex 
-INFO  @ Mon, 26 Mar 2018 08:37:59:   Processing code chunks with options ... 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    1 : keep.source term verbatim (label = funcdef, output_summary.Rnw:27) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    2 : keep.source term tex (label = tab1, output_summary.Rnw:37) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    3 : keep.source term verbatim (output_summary.Rnw:77) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    4 : keep.source term verbatim pdf  (output_summary.Rnw:83) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    5 : keep.source term verbatim pdf  (output_summary.Rnw:201) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    6 : keep.source term verbatim pdf  (output_summary.Rnw:345) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    7 : keep.source term verbatim pdf  (output_summary.Rnw:489) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    8 : keep.source term verbatim (output_summary.Rnw:567) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    9 : keep.source term verbatim pdf  (output_summary.Rnw:573) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:   10 : keep.source term verbatim pdf  (output_summary.Rnw:691) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:   11 : keep.source term verbatim pdf  (output_summary.Rnw:835) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:   12 : keep.source term verbatim pdf  (output_summary.Rnw:979) 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    
-INFO  @ Mon, 26 Mar 2018 08:37:59:   You can now run (pdf)latex on ‘output_summary.tex’ 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    
-INFO  @ Mon, 26 Mar 2018 08:37:59: End command message. 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary-*.pdf 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Command message: 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    
-INFO  @ Mon, 26 Mar 2018 08:37:59: End command message. 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.aux 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Command message: 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    
-INFO  @ Mon, 26 Mar 2018 08:37:59: End command message. 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.tex 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Command message: 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    
-INFO  @ Mon, 26 Mar 2018 08:37:59: End command message. 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Running command: cd ./; rm -rf output_summary.toc 
-INFO  @ Mon, 26 Mar 2018 08:37:59: Command message: 
-INFO  @ Mon, 26 Mar 2018 08:37:59:    
-INFO  @ Mon, 26 Mar 2018 08:37:59: End command message. 
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/out.test.report.pdf
Binary file test-data/out.test.report.pdf has changed
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/out.test.sgrna_summary.txt
--- a/test-data/out.test.sgrna_summary.txt	Thu Apr 19 05:35:12 2018 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1000 +0,0 @@
-sgrna	Gene	control_count	treatment_count	control_mean	treat_mean	LFC	control_var	adj_var	score	p.low	p.high	p.twosided	FDR	high_in_treatment
-AHRR_p344008	AHRR	251.35/221.99	1564.6/1502.5	236.67	1533.6	2.6908	1178.2	5085.6	18.186	1	3.3319e-74	6.6638e-74	6.6571e-71	True
-ACAD9_m128598565	ACAD9	739.35/925.27	2528/2050.7	832.31	2289.3	1.4586	65590	35597	7.7226	1	5.7002e-15	1.14e-14	5.6945e-12	True
-ACTR8_m53916081	ACTR8	1925.5/2054.8	194.94/235.17	1990.2	215.06	-3.2041	4580.2	54357	7.6137	1.3311e-14	1	2.6622e-14	7.8608e-12	False
-ACRC_m70814198	ACRC	76.433/90.573	447.3/357.04	83.503	402.17	2.2543	2086.7	1760.4	7.5952	1	1.5737e-14	3.1475e-14	7.8608e-12	True
-ABCC1_p16101710	ABCC1	52.915/26.639	203.78/224.3	39.777	214.04	2.3987	277.85	700.66	6.5833	1	2.465e-11	4.93e-11	9.8502e-09	True
-ACTR8_m53916067	ACTR8	1267/1156.1	251.09/42.341	1211.6	146.72	-3.0372	13968	31286	6.021	8.6646e-10	1	1.7329e-09	2.3419e-07	False
-AAK1_m69870125	AAK1	402.74/621.58	1149.5/1202.2	512.16	1175.8	1.1974	12666	12223	6.0027	1	9.7009e-10	1.9402e-09	2.3419e-07	True
-ADCK1_p78285331	ADCK1	798.14/768.99	1478.8/1756	783.56	1617.4	1.0446	19425	19317	5.9996	1	9.8882e-10	1.9776e-09	2.3419e-07	True
-AHCY_m32883247	AHCY	1142.1/1099.3	112.93/100.7	1120.7	106.82	-3.379	494.86	28685	5.9891	1.0549e-09	1	2.1098e-09	2.3419e-07	False
-AHCTF1_m247070995	AHCTF1	1437.5/1095.8	320.49/161.36	1266.6	240.92	-2.3895	35534	33759	5.5826	1.1847e-08	1	2.3695e-08	2.3671e-06	False
-AHCY_p32883309	AHCY	1053.9/882.65	106.62/105.85	968.27	106.24	-3.1761	7331.9	24378	5.524	1.6565e-08	1	3.3129e-08	3.0087e-06	False
-ADCY1_p45614315	ADCY1	2.9397/72.814	210.72/187.1	37.877	198.91	2.3624	1360	895.72	5.3806	1	4.1374e-08	8.2747e-08	6.6138e-06	True
-ACTN4_p39138476	ACTN4	449.78/475.95	1056.7/977.86	462.87	1017.3	1.1344	1726.9	10724	5.3539	1	4.3033e-08	8.6065e-08	6.6138e-06	True
-ADAM12_m128076658	ADAM12	768.74/767.21	1432.1/1562.6	767.98	1497.4	0.96239	4258.6	18836	5.3145	1	5.3464e-08	1.0693e-07	7.63e-06	True
-ABT1_m26597388	ABT1	1743.3/1980.2	837.19/281.51	1861.7	559.35	-1.733	91226	64053	5.1459	1.3309e-07	1	2.6618e-07	1.7727e-05	False
-ACRC_p70814182	ACRC	682.02/822.26	1572.2/1333.8	752.14	1453	0.949	19128	18646	5.1324	1	1.4307e-07	2.8614e-07	1.7866e-05	True
-ACBD6_p180471256	ACBD6	107.3/285.93	553.29/687.76	196.61	620.53	1.6531	12498	6924.5	5.0942	1	1.7666e-07	3.5333e-07	2.0382e-05	True
-ACRC_p70811990	ACRC	495.35/605.6	1159.6/1617.6	550.47	1388.6	1.3333	55476	27162	5.0853	1	1.8362e-07	3.6724e-07	2.0382e-05	True
-ADRA1B_m159344001	ADRA1B	329.25/309.01	881.98/676.32	319.13	779.15	1.2851	10677	8286.5	5.0535	1	2.1699e-07	4.3398e-07	2.2818e-05	True
-AHCY_p32883238	AHCY	61.735/218.44	614.49/452.6	140.09	533.54	1.9217	12691	6123	5.0282	1	2.5699e-07	5.1398e-07	2.5673e-05	True
-ACTR5_m37377141	ACTR5	865.75/935.92	1595.5/1695.4	900.84	1645.4	0.86841	3723.6	22496	4.9645	1	3.4447e-07	6.8893e-07	3.2774e-05	True
-ADARB2_m1779330	ADARB2	135.23/118.99	351.41/399.95	127.11	375.68	1.556	655.19	2547.9	4.9245	1	4.2536e-07	8.5071e-07	3.863e-05	True
-ADRB1_m115804012	ADRB1	2166.6/1942.9	1093.3/705.5	2054.7	899.42	-1.191	50114	56324	4.8681	5.6346e-07	1	1.1269e-06	4.8948e-05	False
-AHCTF1_m247070906	AHCTF1	1078.9/1609	348.88/193.97	1343.9	271.43	-2.3036	76257	48827	4.8539	6.0526e-07	1	1.2105e-06	5.0388e-05	False
-ACIN1_m23538803	ACIN1	2432.6/2122.3	1067.5/155.63	2277.4	611.55	-1.8952	2.3194e+05	1.1942e+05	4.8207	7.1537e-07	1	1.4307e-06	5.7173e-05	False
-ACIN1_m23538698	ACIN1	3424.8/3818.3	1992.3/690.05	3621.5	1341.2	-1.4324	4.627e+05	2.2481e+05	4.8094	7.5677e-07	1	1.5135e-06	5.8155e-05	False
-ABCF1_p30545878	ABCF1	2012.3/1989.1	1064.3/432	2000.7	748.15	-1.4179	1.0009e+05	69814	4.7403	1.0669e-06	1	2.1338e-06	7.8951e-05	False
-ABCF1_p30539251	ABCF1	1127.4/1198.8	371.59/370.2	1163.1	370.9	-1.6462	1274	29895	4.5817	2.3062e-06	1	4.6124e-06	0.00016456	False
-ACTR1A_m104250365	ACTR1A	402.74/161.61	711.64/716.37	282.18	714.01	1.3362	14542	8970.1	4.5595	1	2.5679e-06	5.1359e-06	0.00017692	True
-ADRBK1_m67034181	ADRBK1	371.88/877.32	2395.5/1564.9	624.6	1980.2	1.6631	2.3633e+05	88755	4.5503	1	2.728e-06	5.4561e-06	0.00018042	True
-AGL_m100327135	AGL	221.95/118.99	418.91/461.75	170.47	440.33	1.3639	3109.1	3531	4.5414	1	2.7993e-06	5.5987e-06	0.00018042	True
-AHCY_p32883253	AHCY	651.15/879.09	237.21/18.882	765.12	128.05	-2.5697	24907	20808	4.4189	4.9594e-06	1	9.9187e-06	0.00030965	False
-AGTPBP1_m88296213	AGTPBP1	780.5/873.77	1431.5/1476.2	827.13	1453.9	0.81294	2675	20458	4.3817	0.99999	5.8866e-06	1.1773e-05	0.00035641	True
-ADK_p76153940	ADK	804.02/472.4	1336.2/1203.3	638.21	1269.8	0.99133	31910	20857	4.373	0.99999	6.1269e-06	1.2254e-05	0.00036004	True
-ACD_m67694254	ACD	1174.4/934.15	1644.1/1830.4	1054.3	1737.3	0.72	23111	26800	4.1719	0.99998	1.5104e-05	3.0208e-05	0.00086223	True
-ACTR1A_p104262383	ACTR1A	1240.6/836.47	215.76/358.76	1038.5	287.26	-1.8505	45936	32881	4.1431	1.7134e-05	0.99998	3.4269e-05	0.00094812	False
-AAK1_p69870105	AAK1	279.28/289.48	581.68/639.7	284.38	610.69	1.0999	867.56	6237.8	4.1316	0.99998	1.8016e-05	3.6033e-05	0.00094812	True
-ACTR8_m53916096	ACTR8	1719.7/1007	356.45/222.01	1363.4	289.23	-2.233	1.3154e+05	67630	4.1313	1.8032e-05	0.99998	3.6065e-05	0.00094812	False
-ACSS2_p33500893	ACSS2	94.072/447.54	711.64/927.51	270.81	819.57	1.5941	42884	18233	4.0641	0.99998	2.4667e-05	4.9333e-05	0.0012637	True
-ACHE_p100491821	ACHE	864.28/1113.5	382.32/331.29	988.9	356.81	-1.4681	16180	24957	4.0012	3.1512e-05	0.99997	6.3025e-05	0.001574	False
-ACSL6_m131326651	ACSL6	630.57/692.62	1066.8/1423.6	661.6	1245.2	0.91134	32780	21565	3.9742	0.99996	3.5303e-05	7.0606e-05	0.0017204	True
-ADCK4_m41220474	ADCK4	1978.4/1751.1	2659.2/2851.2	1864.8	2755.2	0.56291	22137	50558	3.9601	0.99996	3.7465e-05	7.4931e-05	0.0017823	True
-ACLY_p40070033	ACLY	1594.8/1108.2	605.65/127.6	1351.5	366.63	-1.8793	1.1633e+05	62333	3.945	3.9906e-05	0.99996	7.9812e-05	0.0018542	False
-ACTRT3_m169487204	ACTRT3	377.76/277.05	599.35/716.94	327.4	658.14	1.0051	5992.9	7296	3.8721	0.99995	5.3953e-05	0.00010791	0.00245	True
-ADRB1_m115804005	ADRB1	1346.4/1584.1	791.77/633.98	1465.3	712.87	-1.0384	20355	38658	3.8267	6.4926e-05	0.99994	0.00012985	0.0028827	False
-ABCF1_p30545181	ABCF1	1164.1/1204.1	1687/2089	1184.1	1888	0.67261	40807	33934	3.8212	0.99993	6.6413e-05	0.00013283	0.0028846	True
-ABTB1_m127395813	ABTB1	492.41/353.41	863.06/731.82	422.91	797.44	0.91342	9135.6	9699.2	3.8029	0.99993	7.1501e-05	0.000143	0.0030341	True
-ACLY_m40070008	ACLY	327.78/454.64	51.733/24.032	391.21	37.882	-3.3344	4215.3	8894	3.7981	7.2891e-05	0.99993	0.00014578	0.0030341	False
-AGAP3_p150783818	AGAP3	388.05/509.7	933.09/750.13	448.87	841.61	0.90535	12068	10932	3.7563	0.99991	8.624e-05	0.00017248	0.003464	True
-ABT1_p26597266	ABT1	986.28/745.9	328.06/302.11	866.09	315.09	-1.4559	14615	21533	3.755	8.6686e-05	0.99991	0.00017337	0.003464	False
-ACTR3_p114691894	ACTR3	2171/2250.1	1406.3/1163.8	2210.6	1285	-0.78214	16259	61099	3.7443	9.0443e-05	0.99991	0.00018089	0.0035432	False
-ADCK5_m145608343	ADCK5	936.31/790.3	1459.9/1358.4	863.3	1409.1	0.70621	7906.4	21456	3.7263	0.9999	9.7163e-05	0.00019433	0.0037333	True
-ADRBK1_p67034199	ADRBK1	923.08/758.33	1479.4/1276	840.7	1377.7	0.71192	17136	20832	3.7206	0.9999	9.9387e-05	0.00019877	0.0037467	True
-ACAT2_p160183972	ACAT2	138.17/277.05	441.62/673.46	207.61	557.54	1.4209	18259	9017	3.6851	0.99988	0.00011596	0.00023192	0.0042905	True
-ADRA1A_m26722399	ADRA1A	161.69/230.87	376.64/490.36	196.28	433.5	1.1391	4429.7	4230	3.6474	0.99987	0.00013263	0.00026527	0.0047957	True
-AGAP2_p58129171	AGAP2	508.58/907.51	1324.9/1248.5	708.04	1286.7	0.86084	41245	25220	3.6436	0.99987	0.00013441	0.00026883	0.0047957	True
-ACVR1C_m158443841	ACVR1C	474.77/515.02	895.23/869.72	494.9	882.47	0.83315	567.92	11553	3.606	0.99984	0.0001555	0.00031101	0.0054509	True
-AATF_m35306482	AATF	836.36/1289.3	468.75/177.95	1062.8	323.35	-1.7137	72440	42175	3.6011	0.00015844	0.99984	0.00031689	0.0054581	False
-ADRBK1_p67034193	ADRBK1	908.38/832.92	1639.7/1268.5	870.65	1454.1	0.7393	35863	26394	3.5913	0.99984	0.00016449	0.00032897	0.0055702	True
-ACIN1_m23538715	ACIN1	846.65/985.65	335/415.98	916.15	375.49	-1.2845	6469.8	22922	3.5711	0.00017778	0.99982	0.00035555	0.0059199	False
-ADRA1A_m26722428	ADRA1A	0/0	68.136/184.24	4.4399	126.19	4.5473	3370.2	1164.4	3.568	0.99967	0.00032598	0.00065196	0.0095886	True
-ADAP1_m994071	ADAP1	1148/719.26	262.45/345.03	933.61	303.74	-1.6168	47653	31490	3.5496	0.00019291	0.99981	0.00038582	0.0063186	False
-ADRB3_m37823829	ADRB3	314.55/280.6	690.82/507.52	297.58	599.17	1.0073	8687.9	7269.7	3.5373	0.9998	0.00020218	0.00040435	0.0065153	True
-ADNP2_p77891042	ADNP2	238.12/232.65	417.65/547.01	235.38	482.33	1.0319	4190.7	5054.9	3.4733	0.99974	0.00025717	0.00051434	0.008156	True
-ACVR1_m158637033	ACVR1	958.36/756.55	1619.5/1261.1	857.45	1440.3	0.74755	42295	28294	3.465	0.99973	0.00026516	0.00053033	0.0082203	True
-ADD1_p2877627	ADD1	236.65/310.79	502.82/804.49	273.72	653.65	1.2528	24125	12027	3.4644	0.99973	0.00026743	0.00053485	0.0082203	True
-ABCB8_p150730712	ABCB8	249.88/381.83	630.26/574.47	315.85	602.36	0.92921	5130.8	7010.3	3.4219	0.99969	0.0003109	0.00062181	0.0094119	True
-AHNAK2_m105423836	AHNAK2	293.97/816.94	1369/1079.1	555.46	1224.1	1.1385	89382	38551	3.4054	0.99967	0.00033114	0.00066227	0.0095886	True
-ACVRL1_m52306276	ACVRL1	921.61/880.87	1418.9/1405.3	901.24	1412.1	0.64725	461.12	22507	3.405	0.99967	0.00033082	0.00066164	0.0095886	True
-A1CF_m52596017	A1CF	432.14/731.69	980.4/1082	581.92	1031.2	0.82436	25013	17560	3.3905	0.99965	0.00034889	0.00069778	0.0099583	True
-ACVR1C_p158443781	ACVR1C	492.41/459.97	729.31/981.29	476.19	855.3	0.84356	16137	12757	3.3565	0.99961	0.0003947	0.00078941	0.011107	True
-AFF4_p132272844	AFF4	599.71/941.25	1211.3/1385.3	770.48	1298.3	0.75201	36727	24846	3.3485	0.99959	0.00040631	0.00081262	0.011275	True
-ACHE_m100491767	ACHE	1556.6/1758.2	970.94/942.95	1657.4	956.95	-0.79177	10356	44340	3.3264	0.00043988	0.99956	0.00087977	0.01204	False
-AATK_m79102281	AATK	683.49/816.94	1095.2/1516.9	750.21	1306	0.79901	48894	28533	3.2905	0.9995	0.00050001	0.001	0.0135	True
-AFAP1L2_p116100418	AFAP1L2	388.05/362.29	687.67/664.87	375.17	676.27	0.84835	295.7	8489.3	3.268	0.99946	0.0005416	0.0010832	0.014428	True
-ADCK5_m145608301	ADCK5	1051/703.28	1509.7/1321.2	877.12	1415.4	0.68979	39109	27595	3.2406	0.9994	0.00059631	0.0011926	0.01567	True
-ADNP_p49510899	ADNP	1491.9/1332	2087.6/1983.2	1411.9	2035.4	0.52732	9123.3	37095	3.237	0.9994	0.00060389	0.0012078	0.01567	True
-AFMID_m76187074	AFMID	898.09/880.87	506.6/310.69	889.48	408.65	-1.1202	9669.3	22181	3.2285	0.00062217	0.99938	0.0012443	0.015746	False
-ACSS2_p33500928	ACSS2	399.8/806.28	1147.6/1059.1	603.04	1103.3	0.87047	43263	24017	3.2283	0.99938	0.00062259	0.0012452	0.015746	True
-ACSS2_m33500924	ACSS2	595.3/591.39	1061.8/887.45	593.34	974.62	0.71502	7601.9	14136	3.2068	0.99933	0.0006711	0.0013422	0.016501	True
-ACTR5_p37377211	ACTR5	1021.6/738.79	476.32/281.51	880.18	378.92	-1.2137	29477	24441	3.2063	0.00067225	0.99933	0.0013445	0.016501	False
-ACVR1_m158637046	ACVR1	712.89/678.41	1296.5/986.44	695.65	1141.5	0.71364	24328	19358	3.2042	0.99932	0.00067723	0.0013545	0.016501	True
-ACTL6B_p100253066	ACTL6B	983.34/1022.9	560.86/438.86	1003.1	499.86	-1.0035	4113	25357	3.1605	0.00078736	0.99921	0.0015747	0.018954	False
-ACIN1_p23538735	ACIN1	246.94/177.59	28.39/0	212.27	14.195	-3.811	1403.6	4505.9	3.1549	0.00080283	0.9992	0.0016057	0.019092	False
-ACTR1A_p104250290	ACTR1A	605.59/738.79	2045.3/1120.9	672.19	1583.1	1.2346	2.1808e+05	83522	3.152	0.99918	0.000819	0.001638	0.019092	True
-ADH5_p100003219	ADH5	316.02/586.06	44.793/127.6	451.04	86.195	-2.3741	19945	13595	3.1481	0.00082178	0.99918	0.0016436	0.019092	False
-ACTR8_m53916121	ACTR8	285.15/408.47	99.05/44.63	346.81	71.84	-2.2555	4541.9	7778.6	3.1316	0.00086926	0.99913	0.0017385	0.019963	False
-ACTL6A_p179287878	ACTL6A	518.86/502.59	99.05/238.6	510.73	168.82	-1.5913	4934.7	11964	3.1263	0.00088515	0.99911	0.0017703	0.020097	False
-ADRB3_p37823908	ADRB3	471.83/399.59	733.09/761.57	435.71	747.33	0.77701	1507.4	10026	3.1122	0.99907	0.00092859	0.0018572	0.020846	True
-AGFG1_m228337276	AGFG1	2382.7/2260.8	1567.8/1499.1	2321.7	1533.4	-0.59811	4891.7	64529	3.1032	0.00095731	0.99904	0.0019146	0.021204	False
-ACTL7A_m111624736	ACTL7A	873.1/829.37	364.65/436.57	851.24	400.61	-1.0854	1771.3	21122	3.1006	0.00096573	0.99903	0.0019315	0.021204	False
-ADAM10_p58974401	ADAM10	968.64/864.89	590.51/256.34	916.77	423.43	-1.1126	30610	25496	3.0897	0.001002	0.999	0.0020039	0.02176	False
-AFF3_m100625363	AFF3	1159.7/761.88	505.34/304.97	960.8	405.16	-1.2437	49608	32648	3.0752	0.0010519	0.99895	0.0021038	0.022599	False
-ADCK3_p227149165	ADCK3	558.55/838.25	1063.7/1170.7	698.4	1117.2	0.67697	22420	18772	3.0566	0.99888	0.0011193	0.0022387	0.023792	True
-ABT1_p26597300	ABT1	1562.5/763.66	422.07/220.29	1163.1	321.18	-1.8532	1.697e+05	76498	3.0473	0.0011547	0.99885	0.0023093	0.02425	False
-AGBL5_m27275887	AGBL5	640.86/602.05	307.24/192.83	621.45	250.03	-1.3101	3649.6	14883	3.0445	0.0011652	0.99883	0.0023304	0.02425	False
-ADARB1_m46595721	ADARB1	229.3/735.24	1186.1/914.35	482.27	1050.2	1.1211	82453	34968	3.0372	0.9988	0.0012001	0.0024001	0.024531	True
-ABHD14B_m52004122	ABHD14B	1793.2/1836.3	983.56/1301.7	1814.8	1142.6	-0.66697	25770	49052	3.0349	0.0012032	0.9988	0.0024065	0.024531	False
-ADK_p76154015	ADK	454.19/490.16	896.49/685.47	472.18	790.98	0.74309	11456	11128	3.0222	0.99875	0.0012548	0.0025096	0.025324	True
-AGPAT5_m6566233	AGPAT5	3338.1/3873.3	2840.3/2366	3605.7	2603.1	-0.46989	1.2787e+05	1.1285e+05	2.9845	0.0014203	0.99858	0.0028405	0.028377	False
-ACRC_p70800703	ACRC	1393.4/1472.3	1039.7/532.7	1432.8	786.2	-0.86508	65817	47077	2.9803	0.0014398	0.99856	0.0028796	0.028482	False
-ADK_m75960590	ADK	208.72/65.71	292.1/350.17	137.22	321.14	1.2208	5956.2	3834.8	2.9701	0.99849	0.0015089	0.0030177	0.029556	True
-ACTR1A_p104248866	ACTR1A	975.99/665.98	467.49/172.23	820.99	319.86	-1.3572	45822	28800	2.9531	0.0015732	0.99843	0.0031463	0.030516	False
-ACSL6_m131329908	ACSL6	1011.3/866.66	557.71/419.98	938.97	488.84	-0.94029	9970	23558	2.9326	0.0016805	0.99832	0.0033609	0.032284	False
-ACTN1_m69445700	ACTN1	699.66/571.86	1036.6/953.25	635.76	994.9	0.64526	5818.1	15265	2.9069	0.99817	0.0018254	0.0036508	0.034735	True
-ADH5_m100006267	ADH5	1339.1/1610.8	2090.1/2003.8	1474.9	2047	0.47257	20324	38942	2.8988	0.99813	0.0018729	0.0037458	0.035196	True
-ACO2_p41903797	ACO2	567.37/665.98	377.27/100.13	616.68	238.7	-1.3656	21633	17048	2.895	0.0018957	0.9981	0.0037915	0.035196	False
-ACBD6_m180471338	ACBD6	1123/815.16	436.58/573.33	969.07	504.95	-0.93909	28364	25721	2.8939	0.0019025	0.9981	0.003805	0.035196	False
-ACTL6B_m100253086	ACTL6B	637.92/925.27	1187.3/1186.7	781.6	1187	0.60222	20642	19686	2.8895	0.99807	0.0019291	0.0038582	0.035361	True
-ADAR_m154574103	ADAR	132.29/133.2	278.22/658.58	132.74	468.4	1.8114	36168	13839	2.8533	0.99751	0.0024852	0.0049703	0.043556	True
-ADCK3_p227149155	ADCK3	2275.4/2228.8	2932.4/2994.2	2252.1	2963.3	0.39579	1498	62378	2.8476	0.9978	0.0022023	0.0044046	0.040002	True
-ADARB1_p46595645	ADARB1	1084.8/1005.2	1514.8/1497.4	1045	1506.1	0.52691	1658.6	26537	2.8306	0.99768	0.002323	0.004646	0.04146	True
-ACAD9_m128598636	ACAD9	570.31/527.46	308.51/144.76	548.88	226.63	-1.2724	7162.1	12963	2.8305	0.0023241	0.99768	0.0046481	0.04146	False
-ACVRL1_p52306296	ACVRL1	1331.7/1124.2	725.52/727.81	1227.9	726.67	-0.75606	10768	31756	2.8129	0.0024547	0.99755	0.0049093	0.043402	False
-ACO2_m41895810	ACO2	2229.8/1791.9	1452.9/1241.6	2010.9	1347.3	-0.5774	59094	56356	2.7953	0.0025929	0.99741	0.0051858	0.045049	False
-AHRR_m344006	AHRR	345.42/163.39	474.43/481.2	254.4	477.82	0.9067	8295.4	6439.2	2.7842	0.99731	0.0026854	0.0053709	0.045895	True
-ADD3_m111860427	ADD3	283.69/518.58	682.62/695.77	401.13	689.2	0.77934	13837	10709	2.7837	0.99731	0.0026875	0.0053751	0.045895	True
-AATF_p35306410	AATF	2096/1960.6	1573.4/629.97	2028.3	1101.7	-0.87996	2.2712e+05	1.1272e+05	2.76	0.0028899	0.99711	0.0057797	0.048932	False
-ACVR1B_m52369170	ACVR1B	411.56/831.14	970.31/1637	621.35	1303.7	1.0679	1.5513e+05	61632	2.7484	0.99699	0.0030131	0.0060263	0.050502	True
-ABI1_p27149701	ABI1	698.19/779.64	1791.1/1034.5	738.91	1412.8	0.93415	1.4477e+05	60285	2.7446	0.99697	0.0030332	0.0060664	0.050502	True
-AHCY_p32883304	AHCY	1625.7/1410.1	2146.3/1986.6	1517.9	2066.5	0.44484	17992	40206	2.7358	0.99689	0.0031119	0.0062238	0.051385	True
-AHNAK2_p105423983	AHNAK2	363.06/388.93	977.25/556.16	376	766.7	1.026	44496	20505	2.7285	0.9968	0.0031953	0.0063905	0.052329	True
-ACO2_m41895784	ACO2	824.6/866.66	1135/1341.2	845.63	1238.1	0.54947	11074	20967	2.7103	0.99664	0.0033614	0.0067228	0.054602	True
-AHNAK_p62303470	AHNAK	379.23/731.69	881.35/997.31	555.46	939.33	0.7569	34419	20230	2.6989	0.99652	0.0034785	0.0069571	0.056049	True
-ADCK1_m78285413	ADCK1	1193.5/1355	1622/1905.9	1274.3	1764	0.46882	26674	33093	2.6918	0.99645	0.003553	0.007106	0.056334	True
-ACVR2A_m148602752	ACVR2A	1403.7/996.31	745.71/644.28	1200	694.99	-0.78711	44070	35326	2.687	0.003605	0.9964	0.00721	0.056334	False
-ADCK5_p145608311	ADCK5	601.18/742.35	1225.2/901.19	671.76	1063.2	0.66158	31227	21229	2.6865	0.99639	0.0036103	0.0072206	0.056334	True
-ACHE_m100491773	ACHE	564.43/388.93	162.14/226.01	476.68	194.08	-1.292	8719.8	11080	2.685	0.0036263	0.99637	0.0072527	0.056334	False
-AATK_p79104864	AATK	877.51/1136.6	1415.7/1455.1	1007.1	1435.4	0.51086	17169	25467	2.684	0.99636	0.0036372	0.0072744	0.056334	True
-ADRB3_m37823894	ADRB3	1215.6/1864.7	2161.4/2267.6	1540.2	2214.5	0.52361	1.0817e+05	63299	2.6802	0.99632	0.0036785	0.0073569	0.056535	True
-ADI1_m3523246	ADI1	801.08/665.98	1251.7/971.56	733.53	1111.6	0.59907	24180	19992	2.674	0.99625	0.0037472	0.0074944	0.056772	True
-ADK_m76154033	ADK	557.08/685.52	875.04/1019.6	621.3	947.34	0.60779	9349.9	14879	2.6729	0.99624	0.0037603	0.0075206	0.056772	True
-ACTL6A_p179287966	ACTL6A	1478.7/728.14	548.24/173.94	1103.4	361.09	-1.6088	1.7586e+05	77415	2.6712	0.0037791	0.99622	0.0075583	0.056772	False
-AEBP1_m44144319	AEBP1	992.16/896.85	488.94/579.05	944.51	533.99	-0.82157	4300.8	23713	2.6658	0.0038398	0.99616	0.0076796	0.057253	False
-AAAS_m53714382	AAAS	1034.8/1191.7	504.08/815.93	1113.2	660.01	-0.75331	30465	29136	2.6552	0.0039636	0.99604	0.0079272	0.058661	False
-AATF_p35306466	AATF	277.81/435.11	182.33/0	356.46	91.164	-1.9555	14497	10179	2.6462	0.0040705	0.99593	0.008141	0.0598	False
-ACTR3_m114688941	ACTR3	1148/1046	811.32/186.53	1097	498.93	-1.1351	1.0019e+05	52070	2.621	0.0043835	0.99562	0.0087669	0.063928	False
-ACTL7A_p111624631	ACTL7A	659.97/717.48	377.9/325	688.73	351.45	-0.9686	1526.6	16687	2.611	0.0045142	0.99549	0.0090284	0.065358	False
-AEN_m89169489	AEN	94.072/0	175.39/122.45	47.036	148.92	1.642	2913	1533.7	2.6015	0.99476	0.0052434	0.010487	0.071756	True
-ACTL6A_m179287950	ACTL6A	380.7/387.16	191.79/90.977	383.93	141.38	-1.4348	2551.3	8710	2.6003	0.0046575	0.99534	0.0093151	0.066777	False
-AFF1_p87967338	AFF1	620.29/641.12	825.83/1159.8	630.7	992.82	0.65375	27994	19418	2.5987	0.99532	0.0046791	0.0093581	0.066777	True
-ACTL7A_p111624691	ACTL7A	335.13/497.27	601.24/779.88	416.2	690.56	0.72912	14550	11202	2.5922	0.99523	0.004768	0.0095361	0.067564	True
-ACTR8_m53916057	ACTR8	992.16/1005.2	743.82/200.26	998.67	472.04	-1.0795	73906	41456	2.5865	0.0048472	0.99515	0.0096944	0.068202	False
-AAK1_m69870056	AAK1	933.37/1255.6	1409.4/2011.2	1094.5	1710.3	0.64354	1.165e+05	57460	2.5691	0.9949	0.0050983	0.010197	0.071234	True
-ADAD1_m123301366	ADAD1	526.21/557.65	897.76/765.01	541.93	831.38	0.61648	4652.6	12780	2.5604	0.99477	0.005228	0.010456	0.071756	True
-ACAT2_p160183107	ACAT2	2034.3/2248.3	1099.6/1749.7	2141.3	1424.7	-0.58752	1.1711e+05	78351	2.5602	0.0052302	0.99477	0.01046	0.071756	False
-AGL_p100327219	AGL	196.96/287.7	41.639/78.389	242.33	60.014	-1.9957	2396.1	5221.1	2.5479	0.0054181	0.99458	0.010836	0.073642	False
-AHNAK_m62303488	AHNAK	399.8/504.37	781.04/643.13	452.09	712.09	0.65429	7488.2	10446	2.5438	0.99452	0.0054821	0.010964	0.074008	True
-ADCK3_m227149163	ADCK3	1115.6/1063.8	1343.8/1829.3	1089.7	1586.5	0.54151	59592	38400	2.5353	0.99438	0.0056173	0.011235	0.075325	True
-ACAD9_p128598545	ACAD9	829.01/1099.3	676.31/403.96	964.16	540.14	-0.83477	36810	28445	2.5141	0.0059667	0.99403	0.011933	0.079477	False
-ACAD9_p128598592	ACAD9	921.61/774.31	499.03/469.76	847.96	484.4	-0.80653	5638.3	21032	2.5069	0.0060892	0.99391	0.012178	0.080572	False
-ACTN4_p39138482	ACTN4	1362.6/1005.2	1596.2/1653.6	1183.9	1624.9	0.45648	32756	31246	2.4948	0.9937	0.0063007	0.012601	0.082247	True
-AFTPH_m64778905	AFTPH	463.01/353.41	618.9/679.18	408.21	649.04	0.66768	3911.1	9324.9	2.4939	0.99368	0.0063167	0.012633	0.082247	True
-ABT1_p26597221	ABT1	1597.7/1465.2	1065.6/992.73	1531.5	1029.2	-0.57298	5721.6	40607	2.4927	0.0063393	0.99366	0.012679	0.082247	False
-AES_m3056311	AES	756.98/616.25	892.08/1161.5	686.62	1026.8	0.57988	23102	18787	2.4819	0.99347	0.0065342	0.013068	0.083824	True
-AEBP2_p19615465	AEBP2	257.23/429.78	158.98/81.822	343.5	120.4	-1.5047	8932	8108.1	2.4813	0.0065448	0.99346	0.01309	0.083824	False
-ADAP1_m994066	ADAP1	167.57/67.486	204.41/355.9	117.53	280.15	1.2462	8241.2	4303.9	2.4789	0.99316	0.0068396	0.013679	0.085409	True
-ACTR3_m114684932	ACTR3	1024.5/1362.2	883.88/396.52	1193.3	640.2	-0.89735	87881	49801	2.4786	0.0065951	0.9934	0.01319	0.08393	False
-A1CF_p52595881	A1CF	714.36/671.31	1119.8/906.91	692.83	1013.4	0.54792	11797	16797	2.4732	0.9933	0.0066957	0.013391	0.08445	True
-ACLY_m40070097	ACLY	495.35/586.06	279.48/243.75	540.7	261.62	-1.0445	2376.6	12748	2.4719	0.0067205	0.99328	0.013441	0.08445	False
-ADCY1_m45614243	ADCY1	1011.3/1211.2	485.15/843.4	1111.2	664.27	-0.74144	42077	32969	2.4616	0.0069165	0.99308	0.013833	0.085833	False
-ACTL6A_p179287892	ACTL6A	415.97/669.53	750.76/1074.6	542.75	912.66	0.7487	42284	22629	2.459	0.99303	0.0069678	0.013936	0.085936	True
-ADAMTS5_p28338585	ADAMTS5	973.05/939.48	538.15/613.38	956.27	575.76	-0.73094	1696.7	24042	2.454	0.0070641	0.99294	0.014128	0.086472	False
-AGL_m100327128	AGL	122/248.63	318.6/366.2	185.32	342.4	0.88213	4575.4	4108.1	2.4508	0.99286	0.0071411	0.014282	0.086472	True
-AHNAK2_m105444536	AHNAK2	382.17/454.64	707.86/608.8	418.4	658.33	0.65266	3766.3	9584.3	2.4507	0.99287	0.0071284	0.014257	0.086472	True
-ADCK1_m78285328	ADCK1	662.91/1008.7	1101.5/1694.8	835.82	1398.2	0.74157	1.1789e+05	53095	2.4405	0.99266	0.0073351	0.01467	0.088286	True
-ADAP1_m994042	ADAP1	292.5/369.4	454.24/709.5	330.95	581.87	0.81221	17768	10845	2.4094	0.99201	0.0079943	0.015989	0.095645	True
-ADNP2_p77875475	ADNP2	248.41/106.56	286.42/461.18	177.48	373.8	1.0703	12665	6683.6	2.4013	0.99171	0.0082915	0.016583	0.097623	True
-AEBP1_m44144326	AEBP1	1149.4/1149	1616.3/1506.6	1149.2	1561.4	0.44187	3013.3	29499	2.4	0.9918	0.0081976	0.016395	0.097493	True
-ACVR2B_m38518828	ACVR2B	404.21/1095.8	1399.9/1221	749.99	1310.5	0.80435	1.2756e+05	54751	2.3954	0.99169	0.0083063	0.016613	0.097623	True
-ABI1_m27149751	ABI1	1233.2/868.44	720.48/531.56	1050.8	626.02	-0.74633	42189	31864	2.3798	0.0086604	0.99134	0.017321	0.10099	False
-ABCB8_m150725643	ABCB8	598.24/1035.4	1096.5/1688.5	816.81	1392.5	0.76888	1.3539e+05	58581	2.3785	0.99131	0.0086939	0.017388	0.10099	True
-AGFG1_p228337221	AGFG1	449.78/428	713.54/641.99	438.89	677.76	0.62576	1398.4	10108	2.3759	0.99125	0.0087524	0.017505	0.10108	True
-ADRB3_p37823813	ADRB3	1267/703.28	528.05/453.17	985.15	490.61	-1.0043	80857	43520	2.3707	0.0088784	0.99112	0.017757	0.10195	False
-ACTRT3_p169487206	ACTRT3	35.277/406.69	451.09/596.79	220.98	523.94	1.2417	39794	16406	2.3652	0.99059	0.0094075	0.018815	0.1068	True
-ACD_m67694376	ACD	1855/1420.8	1083.9/1183.8	1637.9	1133.9	-0.53019	49635	45718	2.3572	0.0092066	0.99079	0.018413	0.10511	False
-AFF3_p100623773	AFF3	1902/1825.7	1573.4/850.83	1863.8	1212.1	-0.62031	1.32e+05	77686	2.3382	0.0096884	0.99031	0.019377	0.10936	False
-AHNAK_m62303548	AHNAK	868.69/980.32	721.11/327.86	924.51	524.48	-0.8166	41776	29362	2.3345	0.0097848	0.99022	0.01957	0.10983	False
-ACTRT3_m169487242	ACTRT3	2171/1610.8	1054.8/1460.2	1890.9	1257.5	-0.58809	1.1954e+05	74078	2.3271	0.0099808	0.99002	0.019962	0.11141	False
-ACTR1A_m104250350	ACTR1A	417.44/223.77	119.24/110.43	320.61	114.83	-1.4732	9396.8	7884.1	2.323	0.010089	0.98991	0.020177	0.11198	False
-ADCK3_p227149104	ADCK3	1628.6/1669.4	2129.3/2142.2	1649	2135.7	0.37295	457.83	44091	2.3181	0.98978	0.010222	0.020445	0.11284	True
-AHNAK2_m105423829	AHNAK2	737.88/879.09	561.49/402.82	808.48	482.15	-0.74452	11280	19945	2.3107	0.010425	0.98957	0.020851	0.11445	False
-ACRC_p70814200	ACRC	1940.2/1289.3	942.55/1103.7	1614.8	1023.1	-0.65782	1.1241e+05	66186	2.2997	0.010732	0.98927	0.021464	0.11689	False
-AGPAT3_p45379592	AGPAT3	1078.9/451.09	300.3/251.19	764.99	275.75	-1.4688	99135	45548	2.2982	0.010774	0.98923	0.021549	0.11689	False
-ADNP_p49511011	ADNP	427.73/573.63	655.49/899.47	500.68	777.48	0.63389	20202	14536	2.2958	0.98916	0.010843	0.021685	0.11689	True
-ADCK2_m140373210	ADCK2	862.81/628.69	1499.6/940.67	745.75	1220.1	0.70954	91813	42758	2.2942	0.98911	0.010891	0.021782	0.11689	True
-ADH5_m100003154	ADH5	560.02/753	262.45/456.6	656.51	359.53	-0.86691	18734	16792	2.2919	0.010956	0.98904	0.021913	0.11689	False
-AGPAT5_m6566202	AGPAT5	286.62/570.08	193.68/129.89	428.35	161.78	-1.3992	21104	13593	2.2904	0.010999	0.989	0.021998	0.11689	False
-ADCK4_p41220480	ADCK4	2523.8/2445.5	2153.2/1516.9	2484.6	1835	-0.43701	1.0278e+05	80651	2.2873	0.011088	0.98891	0.022176	0.11722	False
-AGAP3_p150784007	AGAP3	707.01/635.79	837.19/1465.4	671.4	1151.3	0.77709	99916	44119	2.2846	0.98883	0.011175	0.02235	0.11751	True
-ACBD6_p180471279	ACBD6	792.26/1243.2	681.99/462.32	1017.7	572.16	-0.82974	62892	38142	2.2814	0.011263	0.98874	0.022525	0.11782	False
-ABL2_p179100524	ABL2	692.31/472.4	755.81/1107.2	582.36	931.49	0.67671	42954	23548	2.2752	0.98855	0.011449	0.022898	0.11914	True
-ACIN1_p23538701	ACIN1	405.68/268.17	170.34/109.86	336.93	140.1	-1.26	5642.2	7532.4	2.2695	0.011618	0.98838	0.023235	0.12027	False
-ADAR_p154574114	ADAR	99.951/195.35	38.484/20.026	147.65	29.255	-2.2967	2360.6	3009.6	2.259	0.011943	0.98806	0.023885	0.123	False
-ADAM12_p128076641	ADAM12	1128.9/1333.7	1475.7/1837.3	1231.3	1656.5	0.42762	43186	35631	2.2524	0.98785	0.012149	0.024297	0.12417	True
-ABLIM2_p8108299	ABLIM2	939.25/1484.7	1625.2/1782.3	1212	1703.8	0.49102	80553	47716	2.2514	0.98782	0.012181	0.024362	0.12417	True
-ACVR2A_p148602720	ACVR2A	1412.5/715.71	1485.1/1809.8	1064.1	1647.5	0.6301	1.4775e+05	67303	2.2485	0.98773	0.012271	0.024542	0.1243	True
-ACTN4_m39138430	ACTN4	163.16/264.62	376.01/354.75	213.89	365.38	0.76978	2686.5	4544.2	2.2474	0.98768	0.012318	0.024636	0.1243	True
-AATF_m35306509	AATF	367.47/571.86	300.3/116.72	469.66	208.51	-1.1676	18869	13556	2.2438	0.012422	0.98758	0.024844	0.12472	False
-ACAD11_p132378460	ACAD11	1621.3/1113.5	816.37/951.54	1367.4	883.95	-0.62881	69020	46870	2.233	0.012773	0.98723	0.025547	0.12761	False
-AHNAK_m62303560	AHNAK	704.07/959.01	1027.7/1504.8	831.54	1266.3	0.60614	73159	38106	2.2271	0.98703	0.012972	0.025943	0.12879	True
-ACTN1_m69392359	ACTN1	545.32/353.41	897.76/593.35	449.37	745.55	0.72914	32373	17709	2.2257	0.98698	0.013021	0.026042	0.12879	True
-ADRBK2_m26057580	ADRBK2	610/815.16	1209.4/892.6	712.58	1051	0.56	35616	23426	2.2112	0.98649	0.013512	0.027024	0.13299	True
-ABCB8_p150725612	ABCB8	368.94/190.03	700.29/391.94	279.48	546.12	0.96394	31771	14669	2.2015	0.986	0.013999	0.027998	0.13711	True
-ABCF1_p30545587	ABCF1	48.506/248.63	665.59/248.9	148.57	457.24	1.6153	53420	19827	2.1922	0.9834	0.016603	0.033206	0.15509	True
-ACVRL1_p52306281	ACVRL1	1195/896.85	1687.6/1273.1	1045.9	1480.4	0.50076	65182	39436	2.1877	0.98565	0.014347	0.028694	0.13983	True
-AHCY_m32883210	AHCY	301.32/657.1	228.38/137.32	479.21	182.85	-1.3851	33717	18670	2.1749	0.01482	0.98518	0.02964	0.14325	False
-ADAMTS5_p28338667	ADAMTS5	1115.6/923.49	1207.5/1932.8	1019.6	1570.2	0.62248	1.4075e+05	64128	2.1743	0.98516	0.014841	0.029683	0.14325	True
-ADNP_p49520498	ADNP	690.84/767.21	1040.3/993.31	729.02	1016.8	0.47947	2011.1	17776	2.1586	0.98456	0.015442	0.030884	0.14833	True
-ACSL6_p131329830	ACSL6	770.21/735.24	519.85/403.96	752.73	461.91	-0.70332	3663.5	18421	2.1428	0.016066	0.98393	0.032133	0.15359	False
-AFTPH_m64778617	AFTPH	415.97/293.03	242.89/37.192	354.5	140.04	-1.3337	14357	10099	2.1391	0.016214	0.98379	0.032429	0.15398	False
-ADCK5_p145603106	ADCK5	1483.1/1340.8	2315.4/1616.4	1412	1965.9	0.47718	1.2719e+05	67129	2.1379	0.98374	0.016261	0.032523	0.15398	True
-ACLY_m40069993	ACLY	1268.5/1411.9	1136.2/603.65	1340.2	869.94	-0.62286	76050	48686	2.1312	0.016536	0.98346	0.033072	0.15509	False
-ADNP_m49520507	ADNP	959.83/1397.7	1429.6/2811.1	1178.7	2120.4	0.84651	5.2508e+05	1.9526e+05	2.1309	0.98339	0.016611	0.033222	0.15509	True
-AAK1_m69870103	AAK1	712.89/802.73	576.63/359.9	757.81	468.27	-0.69332	13761	18559	2.1254	0.016779	0.98322	0.033557	0.15592	False
-ADCK1_p78285416	ADCK1	826.07/797.4	977.88/2066.7	811.73	1522.3	0.90634	2.966e+05	1.1222e+05	2.1211	0.98291	0.017088	0.034175	0.15806	True
-ACLY_p40070062	ACLY	512.98/269.94	205.67/126.45	391.46	166.06	-1.2322	16336	11379	2.1158	0.017179	0.98282	0.034358	0.15817	False
-AGL_m100327263	AGL	758.45/463.52	410.08/210.56	610.99	310.32	-0.9751	31698	20302	2.1104	0.017414	0.98259	0.034828	0.1596	False
-ADAM12_p128019039	ADAM12	1824.1/815.16	661.8/605.37	1319.6	633.59	-1.0573	2.5529e+05	1.0803e+05	2.0879	0.018404	0.9816	0.036807	0.16759	False
-ACBD6_p180471344	ACBD6	1040.7/1292.9	1392.4/1703.4	1166.8	1547.9	0.40746	40086	33362	2.0865	0.98153	0.018469	0.036937	0.16759	True
-ADRM1_p60878615	ADRM1	477.71/841.8	176.65/448.02	659.75	312.33	-1.0764	51551	27789	2.0849	0.018538	0.98146	0.037075	0.16759	False
-ACTL6B_m100253443	ACTL6B	1114.2/1284	1689.5/1439.6	1199.1	1564.6	0.38355	22826	30927	2.0782	0.98116	0.018844	0.037687	0.16959	True
-ADRB1_m115803922	ADRB1	230.77/223.77	388.63/354.75	227.27	371.69	0.70724	299.14	4861.5	2.0713	0.98082	0.019175	0.038351	0.1717	True
-ABCC1_m16101705	ABCC1	1283.2/1053.1	832.77/786.18	1168.2	809.48	-0.52864	13775	30041	2.0695	0.019249	0.98075	0.038498	0.1717	False
-ACTR8_m53916047	ACTR8	2484.1/2349.6	2172.8/910.91	2416.8	1541.8	-0.64812	4.026e+05	1.7919e+05	2.067	0.019366	0.98063	0.038732	0.17197	False
-AATF_m35306475	AATF	724.65/1086.9	695.24/307.26	905.76	501.25	-0.85232	70435	38567	2.0598	0.019707	0.98029	0.039415	0.17423	False
-ABLIM2_p8108286	ABLIM2	936.31/996.31	1115.4/1572.9	966.31	1344.2	0.47574	53230	33959	2.0505	0.97984	0.020158	0.040316	0.17742	True
-AGPAT3_p45379648	AGPAT3	1030.4/657.1	589.25/411.4	843.74	500.32	-0.75276	42742	28191	2.0453	0.020411	0.97959	0.040821	0.1784	False
-ADRBK1_p67034166	ADRBK1	1270/1213	1591.1/1625	1241.5	1608	0.373	1099.3	32146	2.0446	0.97955	0.020448	0.040896	0.1784	True
-ADD3_p111860505	ADD3	977.46/845.35	620.8/585.34	911.41	603.07	-0.59497	4677.7	22790	2.0425	0.020553	0.97945	0.041105	0.17854	False
-AFTPH_m64778773	AFTPH	60.265/161.61	231.54/191.68	110.94	211.61	0.9255	2964.9	2448.4	2.0345	0.97878	0.021215	0.042431	0.1835	True
-ADH5_p100002510	ADH5	443.9/241.53	451.72/726.67	342.71	589.19	0.77998	29138	14830	2.024	0.97846	0.02154	0.04308	0.1855	True
-ACTR3_p114684941	ACTR3	952.48/1094	1239.1/1457.9	1023.2	1348.5	0.39788	16980	25923	2.0202	0.97832	0.021681	0.043363	0.18592	True
-ADCY1_m45614323	ADCY1	709.95/912.84	1003.7/1190.1	811.39	1096.9	0.43455	18976	20025	2.0179	0.9782	0.021802	0.043604	0.18616	True
-ABL1_m133729449	ABL1	599.71/374.72	1384.2/601.36	487.22	992.77	1.0254	1.6585e+05	62853	2.0165	0.97754	0.022456	0.044913	0.19012	True
-AAK1_m69870137	AAK1	1330.2/522.13	323.02/456.6	926.18	389.81	-1.2464	1.6772e+05	71374	2.0126	0.022081	0.97792	0.044162	0.18773	False
-A1CF_m52603847	A1CF	0/88.797	91.479/152.2	44.399	121.84	1.4361	2893	1492.1	2.0048	0.97429	0.025711	0.051423	0.20798	True
-ADRB1_m115804047	ADRB1	637.92/545.22	385.47/322.14	591.57	353.81	-0.73996	3151.5	14089	2.0031	0.022583	0.97742	0.045166	0.19038	False
-AGPAT3_m45379563	AGPAT3	263.11/250.41	51.102/164.79	256.76	107.95	-1.2424	3271.4	5567.8	1.9995	0.022775	0.97723	0.04555	0.19119	False
-ACO2_m41865159	ACO2	1192.1/879.09	852.33/377.07	1035.6	614.7	-0.75153	80957	44500	1.9952	0.023012	0.97699	0.046024	0.19237	False
-ADK_m76153995	ADK	1011.3/1166.8	652.97/860.56	1089	756.77	-0.52455	16820	27785	1.9934	0.023111	0.97689	0.046221	0.1924	False
-ADRA1A_p26722406	ADRA1A	333.66/557.65	632.15/680.9	445.65	656.52	0.55788	13137	11233	1.9896	0.97668	0.023318	0.046635	0.19331	True
-ABCC1_m16101799	ABCC1	730.53/907.51	491.46/585.34	819.02	538.4	-0.60429	10034	20235	1.9727	0.024264	0.97574	0.048528	0.19967	False
-AFF4_m132272833	AFF4	1365.5/1925.1	1092.1/1252.5	1645.3	1172.3	-0.48869	84728	57563	1.9716	0.024328	0.97567	0.048656	0.19967	False
-ADCY1_p45614309	ADCY1	389.52/195.35	176.02/37.764	292.44	106.89	-1.4435	14203	9024.2	1.9706	0.024384	0.97562	0.048768	0.19967	False
-ADNP2_m77891006	ADNP2	0/0	15.772/168.79	4.4399	92.283	4.1	5853.9	1992.3	1.968	0.95454	0.045461	0.090922	0.29049	True
-AHCTF1_m247068886	AHCTF1	1095.1/882.65	837.82/365.05	988.85	601.44	-0.7164	67157	39023	1.9612	0.024929	0.97507	0.049858	0.20315	False
-ADI1_p3523164	ADI1	1120/1216.5	638.46/1000.2	1168.3	819.32	-0.51137	35036	31708	1.9597	0.025013	0.97499	0.050026	0.20315	False
-AGTPBP1_m88307696	AGTPBP1	0/362.29	437.84/382.79	181.15	410.31	1.1751	33572	13709	1.9573	0.97321	0.026791	0.053581	0.21411	True
-ADI1_p3517642	ADI1	261.64/399.59	406.29/1138.1	330.61	772.18	1.2213	1.3863e+05	51128	1.9529	0.97261	0.027386	0.054772	0.21627	True
-A1CF_m52596056	A1CF	618.82/653.55	357.08/434.29	636.18	395.68	-0.68371	1791.6	15276	1.9458	0.025838	0.97416	0.051677	0.20817	False
-ACAD9_p128598519	ACAD9	636.45/754.78	581.05/224.87	695.62	402.96	-0.78616	35217	22987	1.9303	0.026785	0.97322	0.053569	0.21411	False
-AGAP3_p150783825	AGAP3	326.31/635.79	960.85/615.67	481.05	788.26	0.71131	53731	25373	1.9286	0.97308	0.026923	0.053847	0.21431	True
-ADAM10_m58974490	ADAM10	612.94/699.72	426.48/401.67	656.33	414.08	-0.66324	2036.9	15816	1.9263	0.027032	0.97297	0.054065	0.21433	False
-ADPRHL2_m36554620	ADPRHL2	351.3/298.36	244.79/74.384	324.83	159.58	-1.0208	7959.8	7474.8	1.9126	0.0279	0.9721	0.055799	0.21946	False
-ACTL7A_m111624684	ACTL7A	533.56/348.09	606.92/658.58	440.82	632.75	0.52044	9267.8	10157	1.9043	0.97157	0.028434	0.056869	0.2226	True
-ACTRT3_m169487289	ACTRT3	1117.1/532.78	474.43/389.66	824.94	432.04	-0.93153	87153	42649	1.903	0.028521	0.97148	0.057042	0.2226	False
-AGL_p100327060	AGL	730.53/461.75	329.96/386.22	596.14	358.09	-0.73372	18852	15758	1.8964	0.028956	0.97104	0.057912	0.22511	False
-ADAM12_p128018991	ADAM12	51.445/69.262	83.277/222.58	60.354	152.93	1.327	4930.6	2385.8	1.8953	0.96745	0.032554	0.065108	0.24032	True
-ADCK5_p145608305	ADCK5	376.29/619.81	376.64/94.41	498.05	235.53	-1.0772	34739	19336	1.8904	0.029353	0.97065	0.058706	0.22652	False
-ADIRF_p88728321	ADIRF	1647.7/760.11	685.78/608.23	1203.9	647	-0.89486	1.9847e+05	86867	1.8899	0.029387	0.97061	0.058775	0.22652	False
-ABCF1_p30545610	ABCF1	1975.5/1797.3	1628.3/1289.7	1886.4	1459	-0.37041	36611	51211	1.8885	0.029477	0.97052	0.058955	0.22652	False
-ACBD6_p180471350	ACBD6	607.06/538.11	754.54/830.24	572.58	792.39	0.46802	2620.6	13587	1.8857	0.97033	0.029667	0.059334	0.22708	True
-ABL1_p133729492	ABL1	1105.3/1172.1	1019.5/401.1	1138.7	710.31	-0.68015	96725	51708	1.8841	0.029778	0.97022	0.059555	0.22708	False
-AGAP2_p58129148	AGAP2	762.86/1189.9	622.06/660.3	976.37	641.18	-0.60594	45952	31721	1.882	0.029916	0.97008	0.059832	0.22727	False
-ADH7_m100350762	ADH7	1856.4/1578.8	1422.7/1207.9	1717.6	1315.3	-0.38481	30802	46138	1.8732	0.030518	0.96948	0.061036	0.22984	False
-AFAP1L2_p116100409	AFAP1L2	1344.9/1133.1	2324.8/1356.6	1239	1840.7	0.57073	2.4557e+05	1.0324e+05	1.8728	0.96945	0.030551	0.061102	0.22984	True
-AES_m3061159	AES	565.9/658.88	1000.6/715.8	612.39	858.2	0.48618	22438	17241	1.872	0.9694	0.0306	0.0612	0.22984	True
-ABI1_p27112164	ABI1	1709.5/1655.2	1220.8/1354.9	1682.3	1287.8	-0.38523	5235.8	45083	1.8578	0.031596	0.9684	0.063191	0.23643	False
-ADRB1_p115803909	ADRB1	577.66/634.01	706.6/969.28	605.84	837.94	0.46725	18044	15660	1.8547	0.96818	0.031817	0.063635	0.23721	True
-ABCB8_p150725605	ABCB8	149.93/259.29	880.72/244.89	204.61	562.81	1.4553	1.0406e+05	37570	1.848	0.9622	0.037804	0.075607	0.26117	True
-ACSL6_m131326625	ACSL6	640.86/190.03	897.13/587.06	415.45	742.09	0.83541	74849	31289	1.8466	0.96729	0.032708	0.065415	0.24032	True
-ABLIM2_m8108274	ABLIM2	546.79/1392.3	1411.3/1554.6	969.57	1483	0.61255	1.8387e+05	77568	1.8434	0.96735	0.032647	0.065294	0.24032	True
-ACTRT3_m169487267	ACTRT3	634.98/676.64	847.28/927.51	655.81	887.4	0.43573	2042.6	15802	1.8423	0.96728	0.032716	0.065431	0.24032	True
-ADD1_p2877730	ADD1	194.02/24.863	238.48/209.42	109.44	223.95	1.0263	7364.8	3893.2	1.8351	0.96538	0.034618	0.069237	0.2506	True
-ADD3_p111860500	ADD3	515.92/1049.6	1134.3/1131.8	782.75	1133.1	0.53302	71200	36560	1.8321	0.96653	0.033472	0.066944	0.24497	True
-AHCTF1_m247068867	AHCTF1	748.16/488.39	239.74/477.77	618.27	358.75	-0.78357	31036	20211	1.8256	0.033958	0.96604	0.067917	0.24762	False
-ADI1_m3523251	ADI1	1561/1820.3	2134.3/2023.2	1690.7	2078.8	0.29797	19899	45333	1.8228	0.96583	0.03417	0.06834	0.24826	True
-AGFG1_m228337248	AGFG1	983.34/671.31	675.68/335.87	827.33	505.78	-0.70885	53210	31379	1.8152	0.034743	0.96526	0.069486	0.2506	False
-ABT1_p26597313	ABT1	30.867/179.37	238.48/184.81	105.12	211.65	1.0028	6233.2	3453	1.8128	0.96374	0.036264	0.072527	0.25877	True
-AATK_m79102298	AATK	1067.1/1417.2	1601.2/1531.2	1242.2	1566.2	0.33415	31865	32166	1.8066	0.96459	0.035412	0.070824	0.25451	True
-ADCK4_m41220284	ADCK4	1234.7/1300	1357.7/2232.7	1267.3	1795.2	0.50197	1.9246e+05	86082	1.799	0.96399	0.03601	0.07202	0.25788	True
-ACVR2A_p148653859	ACVR2A	248.41/621.58	605.02/783.89	434.99	694.46	0.67365	42813	20943	1.7929	0.96346	0.036544	0.073087	0.25892	True
-ADPRHL2_m36554557	ADPRHL2	1597.7/1046	759.59/1043.1	1321.9	901.34	-0.55195	96190	55045	1.7925	0.036525	0.96347	0.07305	0.25892	False
-ADD1_p2877736	ADD1	1103.9/809.83	769.69/554.44	956.85	662.06	-0.53065	33197	27105	1.7906	0.036682	0.96332	0.073364	0.25898	False
-ACTRT3_m169487273	ACTRT3	806.96/721.03	177.91/685.47	764	431.69	-0.82211	66251	34569	1.7875	0.036926	0.96307	0.073853	0.25926	False
-ACVR2A_p148653946	ACVR2A	952.48/467.07	919.21/1190.1	709.78	1054.7	0.5707	77255	37255	1.7869	0.96302	0.036982	0.073964	0.25926	True
-ADAD1_p123301335	ADAD1	790.79/408.47	375.38/290.67	599.63	333.02	-0.84652	38337	22314	1.7851	0.037122	0.96288	0.074244	0.25933	False
-ABHD14B_p52004061	ABHD14B	389.52/605.6	377.27/185.39	497.56	281.33	-0.82038	20878	14707	1.7832	0.037275	0.96273	0.074549	0.25949	False
-ACVR1_p158637068	ACVR1	689.37/1438.5	1443.5/1606.1	1063.9	1524.8	0.51878	1.4692e+05	67022	1.7801	0.96247	0.037528	0.075055	0.26035	True
-ACVR2B_m38495825	ACVR2B	2063.7/2365.6	1987.3/1531.7	2214.6	1759.5	-0.33172	74667	65705	1.7755	0.037908	0.96209	0.075815	0.26117	False
-ABCC1_m16043636	ABCC1	70.554/369.4	355.19/467.47	219.98	411.33	0.89992	25479	11618	1.7753	0.96128	0.038724	0.077448	0.26406	True
-ABCF1_p30545638	ABCF1	213.13/376.5	404.4/482.92	294.82	443.66	0.588	8213.8	7066.6	1.7706	0.96168	0.03832	0.076639	0.263	True
-ADIRF_p88728315	ADIRF	702.6/1010.5	1291.4/1017.3	856.56	1154.4	0.43007	42485	28341	1.7691	0.96156	0.038437	0.076873	0.263	True
-ACHE_m100491721	ACHE	1075.9/873.77	824.57/567.03	974.86	695.8	-0.48592	26801	25309	1.7541	0.039707	0.96029	0.079415	0.26985	False
-AEBP1_p44144347	AEBP1	1039.2/994.53	685.78/786.75	1016.9	736.26	-0.46529	3047.7	25743	1.7489	0.040157	0.95984	0.080313	0.27198	False
-ABCB8_p150725600	ABCB8	438.02/399.59	220.81/275.79	418.8	248.3	-0.75183	1125	9594.5	1.7408	0.04086	0.95914	0.081719	0.2758	False
-ADAD1_p123301358	ADAD1	1749.1/1907.4	2041.6/2387.1	1828.3	2214.3	0.27628	36115	49457	1.7361	0.95873	0.041273	0.082545	0.27703	True
-ACVR1B_m52369237	ACVR1B	1472.8/1536.2	1218.2/1098.6	1504.5	1158.4	-0.37684	4584	39812	1.7345	0.041414	0.95859	0.082828	0.27703	False
-AGBL5_m27276006	AGBL5	1133.3/1394.1	639.09/1131.2	1263.7	885.15	-0.51317	77554	47709	1.7331	0.04154	0.95846	0.083081	0.27703	False
-AGTPBP1_p88296203	AGTPBP1	438.02/340.98	458.66/679.75	389.5	569.2	0.54615	14575	10759	1.7325	0.9584	0.041596	0.083193	0.27703	True
-AGPAT3_m45379578	AGPAT3	101.42/156.28	206.93/226.01	128.85	216.47	0.74396	843.48	2586.8	1.7228	0.95729	0.042708	0.085416	0.28166	True
-ADK_m75960552	ADK	1472.8/1829.2	1263/1315.4	1651	1289.2	-0.35659	32444	44151	1.7218	0.042557	0.95744	0.085114	0.28166	False
-AFF2_m147967463	AFF2	604.12/911.06	929.93/1103.7	757.59	1016.8	0.42411	31106	22737	1.7193	0.95722	0.042784	0.085567	0.28166	True
-ACTR3_p114691883	ACTR3	542.38/776.09	418.28/466.9	659.24	442.59	-0.57375	14246	15893	1.7185	0.042856	0.95714	0.085712	0.28166	False
-AHRR_p344036	AHRR	338.07/607.37	378.53/65.229	472.72	221.88	-1.0878	42671	21542	1.7157	0.043111	0.95689	0.086221	0.28174	False
-AFF3_m100623802	AFF3	626.17/191.8	130.59/185.39	408.98	157.99	-1.3666	47918	22202	1.7153	0.043149	0.95685	0.086298	0.28174	False
-ADRB2_p148206408	ADRB2	1074.5/754.78	1548.8/1021.3	914.63	1285.1	0.49016	95113	46958	1.7096	0.95633	0.043672	0.087344	0.28422	True
-AAAS_p53714367	AAAS	474.77/582.51	411.34/268.35	528.64	339.85	-0.63589	8013.4	12432	1.6932	0.045206	0.95479	0.090411	0.29049	False
-ADIRF_p88729975	ADIRF	89.662/209.56	208.82/304.4	149.61	256.61	0.77437	5877.7	3995.3	1.6928	0.95435	0.045653	0.091307	0.29049	True
-AAK1_p69870070	AAK1	1456.6/1275.1	1129.3/962.41	1365.9	1045.9	-0.38484	15199	35751	1.6926	0.045266	0.95473	0.090532	0.29049	False
-AGAP2_m58129187	AGAP2	917.2/768.99	1056.7/1118	843.09	1087.4	0.36672	6431.3	20897	1.69	0.95448	0.045519	0.091037	0.29049	True
-ACP1_p272054	ACP1	645.27/429.78	154.57/448.59	537.53	301.58	-0.8317	33222	19517	1.6895	0.045561	0.95444	0.091122	0.29049	False
-ACTR1A_p104250304	ACTR1A	654.09/1040.7	1092.1/1155.8	847.4	1123.9	0.40703	38383	26805	1.6891	0.9544	0.045603	0.091206	0.29049	True
-ACD_p67694355	ACD	752.57/795.62	1094.6/917.21	774.1	1005.9	0.37747	8329.9	19004	1.6815	0.95367	0.046333	0.092665	0.29388	True
-ADRA1B_m159343996	ADRA1B	748.16/523.9	919.84/777.02	636.03	848.43	0.41512	17672	16072	1.6754	0.95307	0.046933	0.093865	0.296	True
-ABI1_m27149710	ABI1	636.45/1191.7	722.37/328.43	914.06	525.4	-0.7977	1.1586e+05	53863	1.675	0.046963	0.95304	0.093926	0.296	False
-AES_m3061215	AES	523.27/905.73	1206.3/844.54	714.5	1025.4	0.52056	69279	34682	1.6694	0.95248	0.04752	0.09504	0.29857	True
-ADARB2_m1779251	ADARB2	742.28/598.49	943.81/820.51	670.39	882.16	0.39552	8969.8	16193	1.6642	0.95196	0.048038	0.096076	0.30078	True
-ABLIM2_m8108316	ABLIM2	291.03/307.24	154.57/173.94	299.14	164.26	-0.86092	159.5	6598.9	1.6615	0.048306	0.95169	0.096612	0.30078	False
-ADPRHL2_p36554606	ADPRHL2	257.23/245.08	52.995/203.12	251.15	128.06	-0.96627	5671.6	5512.4	1.6613	0.048324	0.95168	0.096648	0.30078	False
-ACLY_p40069998	ACLY	877.51/715.71	538.15/594.5	796.61	566.32	-0.49151	7339.1	19619	1.6441	0.050079	0.94992	0.10016	0.31074	False
-ADAP1_m994091	ADAP1	878.98/1218.3	358.98/956.11	1048.6	657.55	-0.67254	1.1793e+05	57069	1.6372	0.050797	0.9492	0.10159	0.31351	False
-ADRBK2_p26057601	ADRBK2	1456.6/1681.8	1731.2/2079.3	1569.2	1905.2	0.27975	42978	42141	1.6368	0.94916	0.050839	0.10168	0.31351	True
-ACVR2B_p38518841	ACVR2B	587.95/495.49	733.09/717.52	541.72	725.3	0.42037	2197.8	12775	1.6243	0.94784	0.052157	0.10431	0.32065	True
-AGPAT5_p6566262	AGPAT5	696.72/486.61	950.12/681.47	591.66	815.79	0.46276	29080	19088	1.6223	0.94763	0.052374	0.10475	0.32099	True
-ADAMTS5_p28338596	ADAMTS5	840.77/571.86	373.49/573.33	706.31	473.41	-0.57622	28062	20795	1.6151	0.053144	0.94686	0.10629	0.32472	False
-ACP1_m272137	ACP1	1730/722.81	789.87/605.37	1226.4	697.62	-0.81305	2.6214e+05	1.0852e+05	1.6061	0.054128	0.94587	0.10826	0.3284	False
-ADRBK2_m25961078	ADRBK2	316.02/419.12	497.77/529.84	367.57	513.81	0.48208	2914.6	8298.3	1.6053	0.94578	0.054217	0.10843	0.3284	True
-ACVRL1_p52306245	ACVRL1	836.36/973.22	1095.2/1197	904.79	1146.1	0.34076	7272.6	22606	1.6051	0.94576	0.05424	0.10848	0.3284	True
-AGAP2_m58128454	AGAP2	1212.6/1152.6	897.76/908.62	1182.6	903.19	-0.3885	931.14	30454	1.6012	0.054667	0.94533	0.10933	0.32994	False
-ADRBK1_p67034160	ADRBK1	1018.6/717.48	1070.6/1144.4	868.05	1107.5	0.35108	24030	22402	1.5998	0.94517	0.054825	0.10965	0.32994	True
-ACVR2B_p38518817	ACVR2B	1265.6/1760	1651/2271.6	1512.8	1961.3	0.37441	1.5737e+05	79160	1.5942	0.94456	0.055445	0.11089	0.3319	True
-AGAP3_p150783857	AGAP3	1328.8/1038.9	1346.3/1596.4	1183.8	1471.4	0.31342	36634	32538	1.5939	0.94452	0.055483	0.11097	0.3319	True
-ACTN4_p39138459	ACTN4	1575.7/2017.5	1196.8/1602.1	1796.6	1399.5	-0.36017	89860	62290	1.5912	0.05578	0.94422	0.11156	0.33208	False
-ADPRHL2_m36554512	ADPRHL2	495.35/809.83	791.77/982.44	652.59	887.1	0.44234	33814	21748	1.5902	0.94411	0.055895	0.11179	0.33208	True
-AFF2_p147967406	AFF2	1043.6/1051.4	1372.8/1240.5	1047.5	1306.7	0.31867	4392.6	26607	1.5888	0.94395	0.056048	0.1121	0.33208	True
-ACTRT3_m169487247	ACTRT3	432.14/834.7	288.32/464.61	633.42	376.46	-0.7491	48282	26229	1.587	0.056259	0.94374	0.11252	0.33208	False
-ACVR1B_p52369196	ACVR1B	405.68/284.15	173.49/237.46	344.92	205.47	-0.74446	4715.3	7731.4	1.5862	0.056343	0.94366	0.11269	0.33208	False
-AFF3_m100625275	AFF3	521.8/237.98	606.92/516.11	379.89	561.51	0.56251	22201	13139	1.5845	0.94343	0.05657	0.11314	0.33243	True
-AFMID_p76183473	AFMID	383.64/428	833.41/426.85	405.82	630.13	0.63354	41814	20114	1.5816	0.94301	0.056994	0.11399	0.33371	True
-ADARB1_m46595779	ADARB1	665.85/174.04	191.79/144.76	419.95	168.28	-1.3143	61022	26756	1.5794	0.057122	0.94288	0.11424	0.33371	False
-AEN_m89169460	AEN	1606.6/1554	1376.6/1138.1	1580.3	1257.3	-0.32956	14917	42050	1.5748	0.057652	0.94235	0.1153	0.33542	False
-ACVR1_p158655928	ACVR1	288.09/307.24	536.89/335.87	297.67	436.38	0.55035	10194	7773.1	1.5733	0.94216	0.057844	0.11569	0.33542	True
-ACTN1_m69392382	ACTN1	684.96/914.61	846.65/1418.4	799.79	1132.5	0.50135	94919	44777	1.5725	0.94208	0.057918	0.11584	0.33542	True
-ADARB2_p1779233	ADARB2	442.43/229.1	395.57/651.14	335.76	523.36	0.63881	27707	14238	1.5721	0.9419	0.058104	0.11621	0.33552	True
-AFF3_m100625319	AFF3	742.28/781.42	955.8/996.17	761.85	975.98	0.35693	790.26	18669	1.5672	0.94146	0.058537	0.11707	0.33705	True
-ADPRHL2_m36554576	ADPRHL2	1099.5/1115.3	1309.1/1431.6	1107.4	1370.3	0.30714	3814.3	28306	1.563	0.94098	0.059024	0.11805	0.33855	True
-ADCK3_m227149096	ADCK3	270.46/415.57	434.05/525.83	343.01	479.94	0.4834	7370.6	7684	1.5621	0.94086	0.059137	0.11827	0.33855	True
-ACVR2B_p38495805	ACVR2B	804.02/776.09	550.14/595.07	790.05	572.6	-0.46373	699.76	19440	1.5596	0.059426	0.94057	0.11885	0.33924	False
-ACSS2_p33501184	ACSS2	698.19/531.01	504.08/347.89	614.6	425.98	-0.52781	13087	14701	1.5556	0.059898	0.9401	0.1198	0.34096	False
-AAAS_m53715169	AAAS	956.89/1113.5	502.82/966.99	1035.2	734.9	-0.49372	59996	37506	1.5506	0.060496	0.9395	0.12099	0.34299	False
-AATK_p79104850	AATK	438.02/561.2	319.86/344.45	499.61	332.16	-0.58749	3944.4	11675	1.5498	0.060597	0.9394	0.12119	0.34299	False
-ABCC1_m16043645	ABCC1	1609.5/978.55	807.54/1025.9	1294	916.73	-0.49684	1.1145e+05	59593	1.5456	0.061105	0.9389	0.12221	0.34488	False
-ACTL6A_m179287986	ACTL6A	661.44/358.74	296.52/337.59	510.09	317.05	-0.68432	23329	15741	1.5388	0.061929	0.93807	0.12386	0.3477	False
-ADRA1B_p159343912	ADRA1B	1149.4/1465.2	960.21/1086.6	1307.3	1023.4	-0.35292	28911	34048	1.5386	0.061952	0.93805	0.1239	0.3477	False
-AGAP3_p150784015	AGAP3	486.53/673.08	784.83/734.68	579.81	759.75	0.38937	9329.5	13778	1.533	0.93737	0.062632	0.12526	0.34996	True
-ACTR3_p114684915	ACTR3	895.15/824.04	700.29/571.04	859.6	635.66	-0.43481	5440.7	21353	1.5325	0.062705	0.93729	0.12541	0.34996	False
-ADAM10_m59009777	ADAM10	539.44/1021.2	1090.8/1021.3	780.31	1056.1	0.43612	59222	32523	1.5292	0.93689	0.063113	0.12623	0.35125	True
-ACAT2_p160183152	ACAT2	2185.7/1877.2	2122.9/2834.6	2031.4	2478.8	0.28699	1.5041e+05	87211	1.5148	0.93508	0.064918	0.12984	0.36029	True
-ABL2_m179100447	ABL2	1425.8/1026.5	1555.1/1463.6	1226.1	1509.4	0.29963	41949	35119	1.5115	0.93467	0.065331	0.13066	0.36158	True
-ACVR1C_m158443700	ACVR1C	992.16/637.57	876.94/1399.6	814.86	1138.2	0.48168	99717	46653	1.4972	0.93282	0.067177	0.13435	0.37018	True
-ACAT2_m160183968	ACAT2	1816.8/2846.8	1813.8/1763.5	2331.8	1788.6	-0.3824	2.659e+05	1.3186e+05	1.4958	0.067352	0.93265	0.1347	0.37018	False
-ACIN1_m23538815	ACIN1	1308.2/2186.2	1482.6/997.31	1747.2	1240	-0.49441	2.516e+05	1.1521e+05	1.4944	0.06754	0.93246	0.13508	0.37018	False
-AGPAT3_p45379552	AGPAT3	1043.6/1156.1	1405.6/1294.8	1099.9	1350.2	0.29563	6233.8	28093	1.4937	0.93237	0.067625	0.13525	0.37018	True
-ABTB1_m127395233	ABTB1	598.24/546.99	482.63/314.7	572.61	398.67	-0.52129	7706.7	13588	1.4923	0.067814	0.93219	0.13563	0.3702	False
-ACLY_m40070029	ACLY	132.29/241.53	123.02/65.801	186.91	94.412	-0.97779	3802	3911.5	1.4888	0.068272	0.93173	0.13654	0.37168	False
-ADARB1_m46591572	ADARB1	205.78/186.47	225.86/359.9	196.13	292.88	0.57609	4585.1	4279.5	1.479	0.93033	0.069666	0.13933	0.37824	True
-AHCTF1_m247068945	AHCTF1	677.61/316.12	613.86/819.36	496.86	716.61	0.52745	43227	22145	1.4767	0.93009	0.069912	0.13982	0.37855	True
-AFF2_m147924923	AFF2	601.18/328.55	370.96/181.95	464.86	276.46	-0.74764	27513	16354	1.4742	0.070213	0.92979	0.14043	0.37915	False
-ADCK1_p78285402	ADCK1	1246.5/753	1301.5/1264.5	999.73	1283	0.35962	61215	37246	1.4679	0.92894	0.071062	0.14212	0.3827	True
-ADIRF_p88729942	ADIRF	951.01/864.89	606.92/769.01	907.95	687.96	-0.39977	8422.9	22694	1.4603	0.072108	0.92789	0.14422	0.38729	False
-ACVR1B_p52369243	ACVR1B	692.31/749.45	607.55/448.02	720.88	527.78	-0.44908	7178.6	17556	1.4574	0.072508	0.92749	0.14502	0.38839	False
-ACAD9_m128598631	ACAD9	399.8/609.15	281.38/399.95	504.48	340.67	-0.56506	14472	12692	1.4541	0.072959	0.92704	0.14592	0.38977	False
-ADRA1A_p26722438	ADRA1A	1093.6/934.15	726.79/835.96	1013.9	781.37	-0.37536	9334.6	25659	1.4514	0.07333	0.92667	0.14666	0.3907	False
-AFTPH_p64778713	AFTPH	1731.5/1507.8	1543.8/999.6	1619.6	1271.7	-0.34868	86548	57661	1.449	0.073666	0.92633	0.14733	0.39145	False
-ADCK2_m140373155	ADCK2	77.903/97.677	99.681/212.28	87.79	155.98	0.82211	3267.4	2214.8	1.4489	0.92396	0.076041	0.15208	0.39793	True
-ADI1_m3523162	ADI1	592.36/710.38	362.76/578.48	651.37	470.62	-0.46807	15115	15683	1.4433	0.074462	0.92554	0.14892	0.39463	False
-ADCY1_p45614127	ADCY1	1725.6/2271.4	1786.7/1442.5	1998.5	1614.6	-0.30762	1.041e+05	71107	1.4399	0.074949	0.92505	0.1499	0.39616	False
-AFMID_p76187082	AFMID	1137.7/1028.3	803.75/885.74	1083	844.75	-0.35804	4672.8	27613	1.4337	0.075835	0.92417	0.15167	0.39793	False
-AFF1_m87967432	AFF1	173.44/602.05	147.63/215.14	387.75	181.38	-1.0918	47064	21559	1.4316	0.076124	0.92388	0.15225	0.39793	False
-ABHD14B_p52004011	ABHD14B	501.23/635.79	668.74/800.48	568.51	734.61	0.36923	8865.6	13480	1.4307	0.92374	0.07626	0.15252	0.39793	True
-ABT1_p26597361	ABT1	339.54/447.54	310.4/205.99	393.54	258.19	-0.60615	5641.4	8952.9	1.4305	0.07628	0.92372	0.15256	0.39793	False
-ACAD11_p132378566	ACAD11	679.08/777.87	449.82/627.11	728.47	538.47	-0.43532	10297	17761	1.4257	0.076978	0.92302	0.15396	0.40053	False
-ACTR5_m37377189	ACTR5	679.08/774.31	1578.5/680.32	726.7	1129.4	0.63543	2.0394e+05	79789	1.4257	0.92263	0.077373	0.15475	0.40069	True
-ADARB2_m1779337	ADARB2	665.85/701.5	986.71/746.7	683.68	866.7	0.34178	14719	16550	1.4227	0.92259	0.077411	0.15482	0.40069	True
-ACVR1C_m158401030	ACVR1C	1099.5/1435	1341.3/1834.4	1267.2	1587.8	0.32518	88938	51572	1.4119	0.92101	0.078993	0.15799	0.40783	True
-ADH7_p100349698	ADH7	1087.7/847.13	994.28/1551.8	967.42	1273	0.39569	92164	46957	1.4103	0.92077	0.079228	0.15846	0.40798	True
-ADRA1B_p159343905	ADRA1B	410.09/293.03	44.162/334.73	351.56	189.44	-0.88851	24533	13442	1.4058	0.079898	0.9201	0.1598	0.41038	False
-ADARB2_p1421336	ADARB2	561.49/543.44	881.98/580.76	552.47	731.37	0.40409	22765	16293	1.4016	0.91949	0.080514	0.16103	0.412	True
-ABTB1_p127395220	ABTB1	652.62/1113.5	814.48/296.39	883.07	555.43	-0.66795	1.2021e+05	54739	1.4009	0.080627	0.91937	0.16125	0.412	False
-ADRA1B_m159343926	ADRA1B	526.21/820.49	1157.1/708.36	673.35	932.71	0.46947	71980	34842	1.3895	0.91764	0.08236	0.16472	0.41884	True
-AEBP1_p44144306	AEBP1	971.58/944.8	922.99/509.81	958.19	716.4	-0.41904	42858	30350	1.3879	0.082582	0.91742	0.16516	0.41884	False
-ADCK1_m78285358	ADCK1	1547.8/1966	1870/2354	1756.9	2112	0.26543	1.0229e+05	65637	1.386	0.91712	0.082878	0.16576	0.41884	True
-AATK_m79102275	AATK	912.79/772.54	2881.9/634.55	842.66	1758.2	1.0602	1.2676e+06	4.3645e+05	1.3859	0.90779	0.092212	0.18442	0.44502	True
-ABI1_p27112174	ABI1	1484.6/1314.2	2124.2/1406.4	1399.4	1765.3	0.33492	1.3606e+05	69839	1.3847	0.91692	0.083076	0.16615	0.41884	True
-ACO2_p41895716	ACO2	302.79/348.09	404.4/481.78	325.44	443.09	0.44403	2009.6	7247.4	1.382	0.9165	0.083496	0.16699	0.41884	True
-ACVR1C_m158401075	ACVR1C	283.69/312.57	297.15/608.23	298.13	452.69	0.60095	24401	12516	1.3815	0.91612	0.083879	0.16776	0.41884	True
-AEBP2_m19615578	AEBP2	348.36/671.31	820.79/597.36	509.83	709.07	0.47511	38554	20812	1.3811	0.91635	0.083646	0.16729	0.41884	True
-AGFG1_m228337201	AGFG1	933.37/880.87	635.31/763.29	907.12	699.3	-0.37491	4784	22671	1.3802	0.083756	0.91624	0.16751	0.41884	False
-ACTL7A_p111624607	ACTL7A	346.89/715.71	558.34/1060.8	531.3	809.58	0.60672	97130	40711	1.3792	0.91573	0.084272	0.16854	0.41884	True
-AFF4_p132272853	AFF4	756.98/676.64	883.25/914.35	716.81	898.8	0.326	1855.7	17445	1.3778	0.91587	0.084127	0.16825	0.41884	True
-ACIN1_m23535193	ACIN1	561.49/824.04	428.37/579.05	692.77	503.71	-0.45899	22909	18833	1.3776	0.084162	0.91584	0.16832	0.41884	False
-ADARB2_m1779308	ADARB2	1140.6/1191.7	880.09/977.86	1166.1	928.97	-0.32771	3040.9	29983	1.3697	0.085394	0.91461	0.17079	0.42337	False
-ADCK5_m145603137	ADCK5	1195/1083.3	659.91/1098	1139.2	878.96	-0.37373	51102	36508	1.3618	0.086629	0.91337	0.17326	0.42843	False
-ADH5_m100003254	ADH5	1178.8/1243.2	868.1/1075.1	1211	971.62	-0.31745	11749	31269	1.3537	0.087909	0.91209	0.17582	0.43368	False
-ADARB2_p1421307	ADARB2	418.91/300.14	376.64/645.42	359.52	511.03	0.50613	21588	12593	1.3501	0.91144	0.088556	0.17711	0.4358	True
-AEN_m89169544	AEN	536.5/687.29	504.71/394.23	611.9	449.47	-0.44421	8735.7	14629	1.3429	0.089651	0.91035	0.1793	0.43999	False
-ACIN1_p23538745	ACIN1	568.84/612.7	564.02/270.64	590.77	417.33	-0.5004	21998	16711	1.3417	0.089848	0.91015	0.1797	0.43999	False
-ADCK2_m140373220	ADCK2	345.42/582.51	849.18/484.07	463.97	666.62	0.52191	47380	22961	1.3374	0.90935	0.090646	0.18129	0.44173	True
-AHCY_m32883301	AHCY	392.46/412.02	334.37/214	402.24	274.18	-0.55124	3718.3	9173.3	1.3371	0.090599	0.9094	0.1812	0.44173	False
-ADI1_m3517677	ADI1	414.5/488.39	226.49/403.96	451.44	315.22	-0.51679	9238.6	10430	1.3339	0.091125	0.90887	0.18225	0.44261	False
-ACTL7A_p111624699	ACTL7A	1214.1/776.09	893.97/543.57	995.1	718.77	-0.46875	78661	42974	1.333	0.091269	0.90873	0.18254	0.44261	False
-ADRBK1_p67034261	ADRBK1	680.55/634.01	318.6/631.12	657.28	474.86	-0.46818	24958	18880	1.3276	0.092148	0.90785	0.1843	0.44502	False
-ADAM10_m59009883	ADAM10	1197.9/1882.5	1693.9/2150.3	1540.2	1922.1	0.31936	1.6921e+05	83648	1.3204	0.90664	0.093357	0.18671	0.44946	True
-ADNP_m49518579	ADNP	1805/1435	1712.2/2152.5	1620	1932.4	0.25426	82700	56385	1.3156	0.90585	0.094147	0.18829	0.45218	True
-AGL_m100327186	AGL	138.17/159.84	400.62/132.75	149	266.68	0.83553	18056	8045.4	1.312	0.90042	0.099578	0.19916	0.46422	True
-AGL_p100327074	AGL	607.06/985.65	651.08/514.39	796.35	582.73	-0.44991	40504	26576	1.3104	0.095036	0.90496	0.19007	0.45433	False
-AFF4_p132272762	AFF4	561.49/474.18	718.58/605.94	517.83	662.26	0.3543	5078	12150	1.3103	0.90495	0.09505	0.1901	0.45433	True
-ACP1_p264954	ACP1	1267/1397.7	929.3/1247.4	1332.3	1088.3	-0.29162	29557	34775	1.3086	0.095342	0.90466	0.19068	0.45464	False
-ABL1_m133729532	ABL1	1400.8/1465.2	2174.7/1405.3	1433	1790	0.32073	1.4903e+05	74817	1.3052	0.90409	0.095914	0.19183	0.45628	True
-ADRM1_p60878642	ADRM1	94.072/124.32	70.66/451.45	109.19	261.06	1.2498	36479	13594	1.3025	0.88325	0.11675	0.2335	0.50165	True
-AEBP2_p19615498	AEBP2	936.31/907.51	624.58/824.51	921.91	724.55	-0.34712	10201	23083	1.299	0.096966	0.90303	0.19393	0.45921	False
-AFMID_p76183450	AFMID	798.14/1559.3	606.92/1007.6	1178.7	807.26	-0.54554	1.8497e+05	81886	1.2982	0.097118	0.90288	0.19424	0.45921	False
-AFF2_m147891420	AFF2	505.64/122.54	115.45/183.1	314.09	149.28	-1.0681	37834	17256	1.2976	0.09722	0.90278	0.19444	0.45921	False
-AGPAT5_m6566307	AGPAT5	288.09/250.41	232.8/107.57	269.25	170.18	-0.65875	4275.6	5869.9	1.2942	0.097799	0.9022	0.1956	0.46018	False
-ABLIM2_m8160405	ABLIM2	1336.1/1784.8	1398.7/1144.9	1560.5	1271.8	-0.29489	66433	49787	1.2937	0.097886	0.90211	0.19577	0.46018	False
-ABL1_m133729488	ABL1	621.76/651.77	688.3/35.475	636.76	361.89	-0.8135	1.0677e+05	45785	1.2922	0.098144	0.90186	0.19629	0.46031	False
-ACVRL1_m52306921	ACVRL1	740.81/525.68	1467.4/548.15	633.25	1007.8	0.66952	2.2285e+05	84415	1.2891	0.89986	0.10014	0.20028	0.46422	True
-ADCK4_p41220422	ADCK4	161.69/333.88	275.07/465.18	247.78	370.13	0.57703	16449	9050.7	1.286	0.90032	0.09968	0.19936	0.46422	True
-AHNAK2_m105423809	AHNAK2	404.21/470.63	489.57/643.13	437.42	566.35	0.37193	6997.9	10070	1.2848	0.90057	0.099429	0.19886	0.46422	True
-AATK_p79102286	AATK	1887.3/998.08	1163.4/929.22	1442.7	1046.3	-0.46311	2.1139e+05	95793	1.2808	0.10013	0.89987	0.20027	0.46422	False
-ADIRF_p88729951	ADIRF	435.08/435.11	275.7/339.88	435.09	307.79	-0.49803	1029.7	10011	1.2724	0.10161	0.89839	0.20322	0.46994	False
-ADAR_m154574219	ADAR	634.98/1269.8	1618.9/1005.9	952.39	1312.4	0.46214	1.9468e+05	80849	1.266	0.89721	0.10279	0.20558	0.47431	True
-ADH5_p100002504	ADH5	668.79/717.48	772.84/938.95	693.14	855.89	0.3039	7490.8	16805	1.2555	0.89535	0.10465	0.2093	0.48177	True
-AHNAK2_m105444541	AHNAK2	761.39/664.2	846.65/909.2	712.8	877.93	0.30022	3339.3	17337	1.2541	0.8951	0.1049	0.20981	0.48183	True
-ABCB8_p150730680	ABCB8	773.15/854.23	638.46/633.98	813.69	636.22	-0.35447	1648.5	20088	1.2522	0.10525	0.89475	0.21051	0.48234	False
-ACVR1B_m52369192	ACVR1B	632.04/475.95	1491.4/427.99	554	959.71	0.79161	2.8881e+05	1.05e+05	1.252	0.88991	0.11009	0.22017	0.49176	True
-ADRB3_m37823867	ADRB3	251.35/237.98	251.09/438.86	244.66	344.98	0.49401	8859	6470.9	1.2471	0.89369	0.10631	0.21263	0.48496	True
-ACTL6A_p179287911	ACTL6A	1281.7/1189.9	1228.3/725.53	1235.8	976.93	-0.33881	65315	43093	1.247	0.10619	0.89381	0.21238	0.48496	False
-AFF2_m147924918	AFF2	858.4/1136.6	795.55/2439.8	997.51	1617.7	0.69696	6.9522e+05	2.4854e+05	1.244	0.89076	0.10924	0.21847	0.49176	True
-ADIRF_m88728350	ADIRF	244/239.75	130.59/174.52	241.88	152.55	-0.66147	486.77	5210.1	1.2394	0.1076	0.8924	0.2152	0.48865	False
-ADIRF_m88728302	ADIRF	492.41/221.99	309.77/102.99	357.2	206.38	-0.78849	28970	15015	1.2393	0.10761	0.89239	0.21522	0.48865	False
-ACTL6A_p179287902	ACTL6A	754.04/884.42	623.32/663.16	819.23	643.24	-0.34844	4646.4	20240	1.2371	0.10803	0.89197	0.21607	0.48946	False
-AFF4_p132272771	AFF4	551.2/1387	1147/1512.3	969.11	1329.6	0.45588	2.0801e+05	85604	1.2322	0.891	0.109	0.21799	0.49176	True
-ADH5_m100006354	ADH5	1427.2/1157.9	1642.8/1393.3	1292.6	1518	0.2318	33706	33650	1.2291	0.89048	0.10952	0.21903	0.49176	True
-AHNAK_m62303528	AHNAK	266.05/797.4	677.58/839.39	531.72	758.48	0.51163	77130	34052	1.2288	0.89022	0.10978	0.21957	0.49176	True
-AGAP3_p150783972	AGAP3	366/369.4	333.74/746.7	367.7	540.22	0.55377	42636	19746	1.2277	0.88973	0.11027	0.22053	0.49176	True
-AFAP1L2_p116092995	AFAP1L2	1383.1/864.89	894.6/851.98	1124	873.29	-0.36376	67603	41721	1.2275	0.10982	0.89018	0.21963	0.49176	False
-ADIRF_m88729968	ADIRF	734.94/673.08	620.8/470.91	704.01	545.85	-0.3665	6573.2	17099	1.2095	0.11323	0.88677	0.22647	0.50165	False
-ACSL6_p131329816	ACSL6	1797.7/1074.4	1233.4/983.58	1436.1	1108.5	-0.37322	1.4636e+05	73986	1.2043	0.11424	0.88576	0.22849	0.50165	False
-ABTB1_m127395855	ABTB1	560.02/887.97	1113.5/759.28	724	936.4	0.37069	58259	31180	1.2029	0.88549	0.11451	0.22902	0.50165	True
-AHCTF1_p247076572	AHCTF1	551.2/740.57	1103.4/622.53	645.89	862.98	0.41749	66780	32617	1.2021	0.88531	0.11469	0.22938	0.50165	True
-ADRM1_m60878651	ADRM1	736.41/662.43	562.12/523.55	699.42	542.83	-0.36505	1740.2	16975	1.2018	0.11472	0.88528	0.22943	0.50165	False
-AHRR_m344069	AHRR	415.97/372.95	212.61/349.03	394.46	280.82	-0.48876	5115.4	8976.2	1.1995	0.11516	0.88484	0.23032	0.50165	False
-ACVR1B_p52369255	ACVR1B	167.57/166.94	44.162/150.48	167.25	97.323	-0.77502	2826.2	3457	1.1994	0.11518	0.88482	0.23037	0.50165	False
-ADD3_p111860553	ADD3	1881.4/1378.1	1584.8/1004.2	1629.8	1294.5	-0.33207	1.4761e+05	78215	1.1989	0.11528	0.88472	0.23056	0.50165	False
-ADCK3_p227149133	ADCK3	724.65/648.22	702.81/999.6	686.43	851.21	0.30998	23481	18910	1.1982	0.88458	0.11542	0.23083	0.50165	True
-AGPAT5_p6566257	AGPAT5	1383.1/1310.6	1145.1/1099.2	1346.9	1122.1	-0.26321	1840.8	35198	1.1981	0.11543	0.88457	0.23086	0.50165	False
-ADPRHL2_p36554501	ADPRHL2	598.24/694.4	675.68/921.21	646.32	798.45	0.30453	17383	16159	1.1968	0.8843	0.1157	0.2314	0.50165	True
-ACRC_m70811980	ACRC	461.54/502.59	445.41/889.17	482.07	667.29	0.46825	49652	24031	1.1948	0.88382	0.11618	0.23237	0.50165	True
-AGAP3_p150783903	AGAP3	693.78/678.41	784.2/895.46	686.1	839.83	0.2913	3154.2	16616	1.1926	0.8835	0.1165	0.23301	0.50165	True
-AHCTF1_m247094617	AHCTF1	845.18/660.65	541.3/640.84	752.91	591.07	-0.34862	10989	18426	1.1923	0.11658	0.88342	0.23315	0.50165	False
-AAAS_m53715212	AAAS	373.35/603.82	270.65/424.56	488.58	347.61	-0.48996	19201	13993	1.1919	0.11666	0.88334	0.23331	0.50165	False
-ACTRT3_m169487192	ACTRT3	510.05/172.27	218.29/177.38	341.16	197.83	-0.7831	28942	14739	1.1916	0.1167	0.8833	0.23341	0.50165	False
-ACD_m67694260	ACD	1189.1/1653.4	1320.5/977.29	1421.3	1148.9	-0.30672	83330	52689	1.1867	0.11767	0.88233	0.23534	0.50452	False
-ADAMTS5_p28338620	ADAMTS5	399.8/209.56	357.08/470.33	304.68	413.71	0.44006	12254	8574.9	1.1774	0.88042	0.11958	0.23917	0.51163	True
-ACVR2B_p38518810	ACVR2B	1146.5/1292.9	1507.8/1346.9	1219.7	1427.4	0.22667	11831	31519	1.1698	0.87895	0.12105	0.24209	0.51589	True
-AEN_m89169478	AEN	1381.7/1330.2	1509.1/327.29	1355.9	918.19	-0.56192	3.4983e+05	1.4025e+05	1.1695	0.1211	0.8789	0.24219	0.51589	False
-ADRM1_m60878634	ADRM1	410.09/657.1	435.31/356.47	533.6	395.89	-0.42971	16807	13977	1.1648	0.12205	0.87795	0.2441	0.51884	False
-ABL2_p179100503	ABL2	951.01/994.53	996.17/504.09	972.77	750.13	-0.37451	61010	36673	1.1626	0.1225	0.8775	0.245	0.51965	False
-ADNP2_p77875482	ADNP2	561.49/390.71	480.11/775.31	476.1	627.71	0.3981	29077	17069	1.1604	0.87704	0.12296	0.24592	0.51977	True
-ACHE_m100491735	ACHE	1519.8/1601.9	1329.3/1320.6	1560.9	1324.9	-0.23626	1702.3	41476	1.1585	0.12333	0.87667	0.24666	0.51977	False
-ACVR1B_p52369266	ACVR1B	1170/1205.9	1523/1258.2	1187.9	1390.6	0.22706	17843	30607	1.1584	0.87664	0.12336	0.24672	0.51977	True
-AFF4_m132272826	AFF4	1155.3/2191.5	2025.8/2123.4	1673.4	2074.6	0.30985	2.7081e+05	1.2015e+05	1.1573	0.87643	0.12357	0.24714	0.51977	True
-AEBP2_m19615484	AEBP2	104.36/387.16	322.38/409.68	245.76	366.03	0.57281	21899	10835	1.1555	0.87491	0.12509	0.25018	0.52376	True
-AFF1_p87967353	AFF1	1785.9/1459.8	960.21/1647.3	1622.9	1303.8	-0.31564	1.446e+05	77077	1.1494	0.1252	0.8748	0.2504	0.52376	False
-ADH7_p100350718	ADH7	171.97/321.45	229.01/65.229	246.71	147.12	-0.74188	12292	7648	1.1489	0.1253	0.8747	0.25061	0.52376	False
-AEN_m89169484	AEN	1327.3/903.96	975.99/821.65	1115.6	898.82	-0.31144	50758	35946	1.1435	0.12641	0.87359	0.25282	0.52704	False
-ADNP_m49511043	ADNP	408.62/523.9	483.89/701.49	466.26	592.69	0.34548	15160	12261	1.1418	0.87323	0.12677	0.25355	0.52704	True
-ACSL6_p131329859	ACSL6	242.53/225.55	324.91/304.97	234.04	314.94	0.42676	171.47	5022.7	1.1416	0.87312	0.12688	0.25376	0.52704	True
-A1CF_p52595870	A1CF	915.73/1035.4	948.23/639.13	975.55	793.68	-0.29733	27465	25543	1.138	0.12756	0.87244	0.25512	0.52877	False
-AFF3_m100625326	AFF3	467.42/451.09	520.48/629.97	459.25	575.23	0.32421	3063.5	10631	1.1248	0.86966	0.13034	0.26068	0.53916	True
-AGBL5_p27275870	AGBL5	684.96/546.99	394.31/566.46	615.98	480.38	-0.35803	12168	14737	1.1169	0.13201	0.86799	0.26402	0.54481	False
-AGPAT3_p45379618	AGPAT3	58.795/703.28	440.99/837.1	381.04	639.05	0.74447	1.4306e+05	53446	1.116	0.86089	0.13911	0.27822	0.56377	True
-ACTL6B_m100253081	ACTL6B	582.07/690.84	473.17/524.12	636.46	498.64	-0.35143	3607	15284	1.1148	0.13248	0.86752	0.26496	0.54481	False
-ABL1_p133729502	ABL1	774.62/529.23	675.68/228.87	651.93	452.28	-0.52653	64964	32120	1.1145	0.13252	0.86748	0.26504	0.54481	False
-AHRR_m353892	AHRR	351.3/150.96	288.95/416.55	251.13	352.75	0.48857	14105	8323.1	1.1139	0.86694	0.13306	0.26612	0.5459	True
-ADAMTS5_m28338697	ADAMTS5	1068.6/749.45	541.93/875.44	909.02	708.69	-0.35872	53270	32906	1.1044	0.13471	0.86529	0.26942	0.55154	False
-AGBL5_p27275898	AGBL5	1108.3/1969.5	1066.2/1333.8	1538.9	1200	-0.35863	2.0333e+05	94995	1.0997	0.13574	0.86426	0.27148	0.55463	False
-ACVR2B_p38518836	ACVR2B	1465.5/777.87	744.45/951.54	1121.7	847.99	-0.4031	1.2892e+05	62115	1.0981	0.13608	0.86392	0.27217	0.55489	False
-ACTR1A_p104262351	ACTR1A	973.05/1099.3	768.42/2344.2	1036.2	1556.3	0.5864	6.2477e+05	2.2578e+05	1.0947	0.86114	0.13886	0.27772	0.56377	True
-AGTPBP1_p88307700	AGTPBP1	157.28/118.99	40.377/603.08	138.13	321.73	1.2139	79525	28371	1.09	0.82636	0.17364	0.34729	0.6308	True
-ADRB1_p115803878	ADRB1	438.02/1149	1278.8/877.15	793.53	1078	0.4415	1.6672e+05	68596	1.0861	0.86111	0.13889	0.27778	0.56377	True
-ABL1_p133729441	ABL1	630.57/417.35	495.25/280.94	523.96	388.09	-0.43209	22848	15823	1.0802	0.14003	0.85997	0.28006	0.56546	False
-AFF2_m147891412	AFF2	774.62/500.82	838.45/723.24	637.72	780.84	0.2917	22061	17565	1.0799	0.85991	0.14009	0.28018	0.56546	True
-ADIRF_p88729984	ADIRF	1328.8/1571.7	1123/1356.6	1450.2	1239.8	-0.226	28405	38217	1.0764	0.14088	0.85912	0.28175	0.56748	False
-ACAD9_m128598607	ACAD9	133.76/214.89	205.67/272.36	174.32	239.01	0.4531	2757.4	3619.8	1.0752	0.85859	0.14141	0.28281	0.56847	True
-ACHE_p100491631	ACHE	540.91/587.84	672.53/703.78	564.38	688.16	0.28562	794.73	13371	1.0705	0.8578	0.1422	0.28441	0.57053	True
-ADAR_m154574098	ADAR	1572.8/1028.3	1218.2/871.43	1300.5	1044.8	-0.31553	1.0419e+05	57297	1.0681	0.14273	0.85727	0.28546	0.57108	False
-ACVR2A_p148602754	ACVR2A	514.45/252.18	461.18/539.57	383.32	500.37	0.38358	18733	12041	1.0667	0.85692	0.14308	0.28615	0.57108	True
-AFTPH_m64778841	AFTPH	746.69/456.42	1070/553.3	601.56	811.64	0.43153	87807	38838	1.066	0.85663	0.14337	0.28674	0.57108	True
-AHNAK2_m105423822	AHNAK2	316.02/735.24	709.75/664.87	525.63	687.31	0.38627	44440	23049	1.065	0.85651	0.14349	0.28697	0.57108	True
-AGBL5_p27275854	AGBL5	296.91/829.37	489.57/1233.1	563.14	861.31	0.61215	2.0907e+05	78581	1.0637	0.85298	0.14702	0.29403	0.57688	True
-ADAM12_m128019019	ADAM12	1106.8/1305.3	1081.3/955.54	1206.1	1018.4	-0.24372	13808	31127	1.0634	0.14379	0.85621	0.28758	0.57116	False
-AGPAT5_m6566240	AGPAT5	802.55/1594.8	872.52/950.39	1198.7	911.46	-0.39482	1.5843e+05	73420	1.06	0.14457	0.85543	0.28914	0.5713	False
-ABTB1_p127395242	ABTB1	842.24/1424.3	853.59/946.96	1133.3	900.28	-0.33173	86882	48323	1.0599	0.14459	0.85541	0.28918	0.5713	False
-ABT1_p26597293	ABT1	523.27/694.4	336.9/597.93	608.83	467.41	-0.38064	24355	17817	1.0595	0.14468	0.85532	0.28936	0.5713	False
-ACTR8_m53916073	ACTR8	1500.7/1316	800.6/1476.2	1408.4	1138.4	-0.30675	1.2265e+05	65545	1.0544	0.14585	0.85415	0.2917	0.57477	False
-AGBL5_m27276001	AGBL5	624.7/554.1	370.96/558.45	589.4	464.71	-0.34226	10034	14032	1.0526	0.14626	0.85374	0.29251	0.57523	False
-AEBP2_p19615557	AEBP2	1187.7/859.56	1404.4/1045.9	1023.6	1225.2	0.25907	59027	36965	1.0483	0.85275	0.14725	0.2945	0.57688	True
-ADRBK1_m67034255	ADRBK1	586.48/987.43	743.82/1312	786.95	1027.9	0.38494	1.209e+05	53204	1.0447	0.85186	0.14814	0.29627	0.57921	True
-AFMID_m76183493	AFMID	446.84/191.8	220.18/208.85	319.32	214.51	-0.57174	16293	10162	1.0425	0.14858	0.85142	0.29717	0.57983	False
-ADAR_p154574121	ADAR	0/8.8797	0/0	4.4399	0	-2.4436	19.712	61.451	1.0394	0.85069	0.14931	0.29862	0.5806	False
-ADCK4_p41220276	ADCK4	680.55/534.56	550.77/1025.9	607.56	788.34	0.37527	61771	30266	1.0392	0.85061	0.14939	0.29879	0.5806	True
-AATF_p35307505	AATF	995.1/477.73	728.05/275.22	736.42	501.63	-0.55297	1.1818e+05	51379	1.0379	0.14966	0.85034	0.29931	0.5806	False
-ADRB2_p148206432	ADRB2	467.42/1118.8	592.41/537.28	793.13	564.84	-0.48898	1.0685e+05	48633	1.0358	0.15015	0.84985	0.3003	0.5814	False
-AGFG1_m228337138	AGFG1	1049.5/1001.6	1732.4/890.89	1025.6	1311.7	0.35467	1.7762e+05	76532	1.0342	0.84945	0.15055	0.30109	0.58142	True
-ABL2_m179100597	ABL2	621.76/765.43	919.84/735.25	693.59	827.55	0.25441	13679	16818	1.0329	0.84918	0.15082	0.30165	0.58142	True
-ACTR5_p37377198	ACTR5	373.35/479.51	424.59/652.29	426.43	538.44	0.33578	15779	11786	1.0318	0.8489	0.1511	0.30219	0.58142	True
-ACTR5_p37377178	ACTR5	379.23/664.2	864.32/508.1	521.72	686.21	0.39472	52027	25510	1.0299	0.84839	0.15161	0.30322	0.58142	True
-ADRBK1_m67034242	ADRBK1	1462.5/1209.4	1537.5/1519.1	1336	1528.3	0.19391	16099	34881	1.0299	0.84846	0.15154	0.30308	0.58142	True
-ACAD11_m132378487	ACAD11	546.79/717.48	826.47/690.62	632.14	758.54	0.26262	11897	15168	1.0264	0.84764	0.15236	0.30472	0.58317	True
-AFF1_m87967920	AFF1	751.1/1630.3	1093.3/632.83	1190.7	863.08	-0.46379	2.4627e+05	1.0255e+05	1.0235	0.15304	0.84696	0.30609	0.58416	False
-AHCY_p32883227	AHCY	1071.5/406.69	645.4/349.6	739.11	497.5	-0.57015	1.3238e+05	56160	1.0228	0.1532	0.8468	0.30641	0.58416	False
-ABTB1_m127395403	ABTB1	846.65/1557.5	482.63/1248.5	1202.1	865.57	-0.47335	2.7297e+05	1.1166e+05	1.0076	0.15683	0.84317	0.31365	0.59683	False
-ACAT2_p160183940	ACAT2	22.048/0	25.866/22.315	11.024	24.091	1.0612	124.68	168.55	1.0065	0.80415	0.19585	0.3917	0.661	True
-ADCK4_m41220465	ADCK4	812.84/845.35	946.33/999.6	829.09	972.97	0.2306	973.58	20512	1.0046	0.84245	0.15755	0.31511	0.59815	True
-ACP1_p272032	ACP1	1616.9/1427.9	1369/1272.5	1522.4	1320.8	-0.20477	11258	40338	1.0037	0.15777	0.84223	0.31554	0.59815	False
-ABHD14B_p52004005	ABHD14B	186.67/415.57	333.74/472.62	301.12	403.18	0.41987	17921	10405	1.0005	0.84122	0.15878	0.31756	0.60085	True
-ADAM10_m59041696	ADAM10	499.76/458.19	409.45/338.73	478.98	374.09	-0.35573	1682	11140	0.99376	0.16017	0.83983	0.32034	0.6044	False
-ADRM1_p60878730	ADRM1	621.76/635.79	392.41/621.39	628.77	506.9	-0.31028	13157	15078	0.99248	0.16048	0.83952	0.32096	0.6044	False
-ACVR2B_m38518849	ACVR2B	646.74/737.02	715.43/378.78	691.88	547.11	-0.33815	30370	21304	0.99188	0.16063	0.83937	0.32126	0.6044	False
-ACSL6_m131326630	ACSL6	1031.8/605.6	802.49/1259.4	818.72	1030.9	0.33214	97607	46020	0.98921	0.83871	0.16129	0.32258	0.60575	True
-AES_m3056337	AES	827.54/1225.4	1143.2/1261.1	1026.5	1202.1	0.2277	43050	31693	0.98671	0.83811	0.16189	0.32378	0.60652	True
-ACVRL1_m52306910	ACVRL1	1275.8/1136.6	1464.9/1295.4	1206.2	1380.2	0.1942	12030	31132	0.98585	0.8379	0.1621	0.32421	0.60652	True
-ADCK4_m41220291	ADCK4	224.89/188.25	700.29/81.25	206.57	390.77	0.9164	96138	34960	0.98514	0.81248	0.18752	0.37505	0.64631	True
-AAAS_p53714374	AAAS	430.67/463.52	540.04/553.87	447.1	546.96	0.29025	317.6	10318	0.98307	0.83721	0.16279	0.32557	0.60794	True
-ACTN4_p39138433	ACTN4	867.22/840.02	1026.5/966.41	853.62	996.44	0.22294	1086.3	21188	0.98111	0.83673	0.16327	0.32654	0.6086	True
-ACVR2A_p148653965	ACVR2A	573.25/168.72	119.87/333.58	370.98	226.73	-0.70794	52330	23033	0.97466	0.16486	0.83514	0.32973	0.61302	False
-ACTL6B_p100253446	ACTL6B	1209.7/827.59	1085.1/472.62	1018.6	778.88	-0.38675	1.3029e+05	60627	0.97385	0.16507	0.83493	0.33013	0.61302	False
-AGPAT3_m45379590	AGPAT3	1203.8/1630.3	1392.4/997.88	1417.1	1195.1	-0.24556	84380	52957	0.96445	0.16741	0.83259	0.33482	0.62057	False
-ABLIM2_p8108320	ABLIM2	149.93/67.486	97.788/234.59	108.71	166.19	0.60785	6378.1	3553.6	0.96432	0.82664	0.17336	0.34671	0.6308	True
-ABCB8_m150730720	ABCB8	146.99/266.39	292.73/247.75	206.69	270.24	0.38516	4070.2	4374.5	0.9609	0.83155	0.16845	0.3369	0.62326	True
-ACVR2A_m148653941	ACVR2A	582.07/653.55	470.64/1227.3	617.81	848.99	0.45795	1.4442e+05	57998	0.95993	0.83058	0.16942	0.33884	0.62453	True
-ABT1_p26597240	ABT1	119.06/378.28	185.48/128.74	248.67	157.11	-0.65907	17603	9449.8	0.95907	0.16876	0.83124	0.33752	0.62326	False
-ADRBK2_p25961098	ADRBK2	2034.3/822.26	1083.2/1041.9	1428.3	1062.6	-0.42635	3.6769e+05	1.4761e+05	0.95215	0.17051	0.82949	0.34102	0.62738	False
-ACAT2_m160183956	ACAT2	796.67/1024.7	1444.1/813.64	910.7	1128.9	0.30954	1.1237e+05	52638	0.95096	0.82918	0.17082	0.34164	0.62738	True
-AATK_p79102311	AATK	107.3/326.77	531.84/160.78	217.04	346.31	0.67165	46463	18567	0.94873	0.81853	0.18147	0.36294	0.6361	True
-ABCB8_m150725669	ABCB8	1206.8/754.78	952.64/616.24	980.77	784.44	-0.32188	79365	42941	0.94744	0.17171	0.82829	0.34341	0.62949	False
-ABCC1_m16101788	ABCC1	665.85/525.68	475.69/490.36	595.77	483.03	-0.30208	4965.8	14200	0.94608	0.17205	0.82795	0.34411	0.6296	False
-ADAMTS5_p28338524	ADAMTS5	902.5/1040.7	1009.4/594.5	971.6	801.96	-0.27652	47816	32253	0.94461	0.17243	0.82757	0.34486	0.6296	False
-AGPAT5_p6566182	AGPAT5	483.59/413.8	498.4/591.06	448.69	544.73	0.27926	3364.2	10359	0.94362	0.82732	0.17268	0.34537	0.6296	True
-ABLIM2_m8108294	ABLIM2	1042.1/2282.1	1859.2/2234.4	1662.1	2046.8	0.30019	4.1955e+05	1.6951e+05	0.93436	0.82494	0.17506	0.35013	0.631	True
-ADAM12_m128018982	ADAM12	643.8/353.41	417.02/350.17	498.61	383.6	-0.37745	22199	15166	0.93401	0.17515	0.82485	0.3503	0.631	False
-AGAP3_p150783866	AGAP3	352.77/234.43	310.4/429.14	293.6	369.77	0.33177	7026	6650.8	0.934	0.82482	0.17518	0.35036	0.631	True
-ACTN1_m69445694	ACTN1	311.61/630.46	514.81/110.43	471.04	312.62	-0.58989	66296	29388	0.93365	0.17524	0.82476	0.35049	0.631	False
-ADK_p76074475	ADK	198.43/28.415	249.83/114.44	113.42	182.13	0.67851	11810	5433.1	0.93218	0.81278	0.18722	0.37443	0.64631	True
-ADAR_p154574126	ADAR	342.48/403.14	234.69/339.88	372.81	287.28	-0.37482	3685.9	8429.9	0.93159	0.17577	0.82423	0.35155	0.631	False
-ADCK4_m41220243	ADCK4	830.48/864.89	1349.5/740.97	847.68	1045.2	0.3019	92864	44971	0.93153	0.8242	0.1758	0.35159	0.631	True
-AGBL5_p27275820	AGBL5	423.32/412.02	224.6/426.85	417.67	325.72	-0.35775	10258	9796.5	0.92903	0.17644	0.82356	0.35287	0.631	False
-AGAP2_m58128438	AGAP2	817.25/896.85	1170.9/832.52	857.05	1001.7	0.22479	30214	24260	0.92887	0.82352	0.17648	0.35296	0.631	True
-AATF_m35307525	AATF	405.68/259.29	324.28/166.5	332.49	245.39	-0.43668	11581	8808.4	0.92863	0.17654	0.82346	0.35308	0.631	False
-ACTL6A_p179287976	ACTL6A	317.49/985.65	1232.8/599.07	651.57	915.92	0.49065	2.12e+05	81125	0.9281	0.82134	0.17866	0.35732	0.63178	True
-ACVRL1_p52306883	ACVRL1	1552.2/1129.5	1162.1/1157	1340.8	1159.5	-0.20944	44671	38239	0.92724	0.1769	0.8231	0.3538	0.63116	False
-AFAP1L2_p116164194	AFAP1L2	590.89/365.85	606.29/552.15	478.37	579.22	0.27547	13394	11881	0.92528	0.82259	0.17741	0.35482	0.63146	True
-ABTB1_p127395252	ABTB1	526.21/399.59	411.34/771.3	462.9	591.32	0.35256	36401	19284	0.92477	0.82238	0.17762	0.35524	0.63146	True
-ACHE_m100491729	ACHE	486.53/831.14	860.53/730.1	658.84	795.32	0.27124	33943	21903	0.92221	0.82179	0.17821	0.35642	0.63178	True
-AGBL5_m27275950	AGBL5	354.24/280.6	204.41/275.79	317.42	240.1	-0.4013	2629.6	7049	0.92117	0.17848	0.82152	0.35696	0.63178	False
-ACTN4_m39138399	ACTN4	793.73/710.38	763.38/989.87	752.05	876.63	0.22085	14562	18402	0.91829	0.82077	0.17923	0.35847	0.6327	True
-ACVR1C_m158401016	ACVR1C	448.31/458.19	456.13/637.41	453.25	546.77	0.27008	8239.7	10476	0.91368	0.81956	0.18044	0.36089	0.63576	True
-ABL1_m133729456	ABL1	1145/985.65	1194.3/478.92	1065.3	836.6	-0.34834	1.3428e+05	62837	0.91256	0.18074	0.81926	0.36147	0.63576	False
-ADRA1A_p26722419	ADRA1A	567.37/507.92	500.3/370.2	537.65	435.25	-0.30419	5114.7	12668	0.90978	0.18147	0.81853	0.36294	0.6361	False
-AHNAK2_p105423957	AHNAK2	676.14/552.32	313.55/654	614.23	483.78	-0.3438	32810	20731	0.90607	0.18245	0.81755	0.3649	0.63841	False
-ACVR1_p158655958	ACVR1	410.09/360.52	675.05/325.57	385.31	500.31	0.37596	31149	16213	0.90322	0.81657	0.18343	0.36687	0.64073	True
-ABLIM2_m8108279	ABLIM2	673.2/600.27	476.32/574.47	636.74	525.4	-0.27681	3738	15291	0.90039	0.18396	0.81604	0.36791	0.64111	False
-ADRM1_p60878704	ADRM1	273.4/671.31	363.39/323.28	472.35	343.34	-0.45909	39986	20641	0.89954	0.18418	0.81582	0.36836	0.64111	False
-ACBD6_p180471263	ACBD6	355.71/285.93	278.22/212.28	320.82	245.25	-0.38612	2304.5	7133	0.89497	0.1854	0.8146	0.37081	0.64423	False
-AFF3_m100623914	AFF3	242.53/216.67	112.3/222.01	229.6	167.15	-0.45561	3176.2	4916.9	0.89214	0.18616	0.81384	0.37232	0.64573	False
-ACBD6_p180471286	ACBD6	723.18/1007	934.98/428.56	865.07	681.77	-0.34308	84248	42419	0.89002	0.18673	0.81327	0.37346	0.64631	False
-ADRBK1_p67034172	ADRBK1	1321.4/1795.5	1478.8/1237.1	1558.4	1357.9	-0.19856	70796	51201	0.88616	0.18777	0.81223	0.37553	0.64631	False
-ADCK1_p78285311	ADCK1	868.69/701.5	720.48/603.65	785.1	662.06	-0.24556	10401	19304	0.88551	0.18794	0.81206	0.37588	0.64631	False
-ACP1_m264983	ACP1	2104.9/1738.7	1569.7/1866.5	1921.8	1718.1	-0.16156	55549	53370	0.88174	0.18896	0.81104	0.37792	0.64866	False
-ACBD6_p180471251	ACBD6	488/204.23	565.28/348.46	346.12	456.87	0.39952	31883	15802	0.88105	0.81029	0.18971	0.37941	0.64903	True
-ACD_p67694333	ACD	307.2/243.3	157.09/256.91	275.25	207	-0.40941	3511.6	6015.7	0.88058	0.18927	0.81073	0.37855	0.64866	False
-ACP1_p272042	ACP1	862.81/1184.6	890.82/876.01	1023.7	883.41	-0.21239	25934	25936	0.87101	0.19187	0.80813	0.38375	0.65417	False
-ACAT2_p160183133	ACAT2	520.33/671.31	659.91/739.26	595.82	699.58	0.23126	7272.3	14202	0.8707	0.80804	0.19196	0.38392	0.65417	True
-ADNP2_p77891013	ADNP2	742.28/607.37	525.53/601.93	674.83	563.73	-0.25909	6009.6	16312	0.86985	0.19219	0.80781	0.38438	0.65417	False
-ACTN4_p39138465	ACTN4	1705.1/1083.3	1088.9/1277.7	1394.2	1183.3	-0.23643	1.0554e+05	59565	0.86409	0.19377	0.80623	0.38754	0.65842	False
-ACTN4_p39138381	ACTN4	657.03/635.79	678.21/829.66	646.41	753.93	0.22167	5847.6	15550	0.86227	0.80573	0.19427	0.38854	0.659	True
-ADCK2_p140373137	ADCK2	194.02/53.278	121.76/25.176	123.65	73.469	-0.74318	7284.5	4075.4	0.86081	0.19467	0.80533	0.38934	0.65924	False
-A1CF_p52603829	A1CF	111.71/252.18	209.46/269.5	181.95	239.48	0.39447	5834.5	4475.7	0.85992	0.80444	0.19556	0.39111	0.661	True
-ADRB2_p148206474	ADRB2	148.46/442.21	418.91/358.19	295.33	388.55	0.39458	22495	11835	0.85682	0.80358	0.19642	0.39285	0.66181	True
-ADD3_m111860450	ADD3	1597.7/1502.5	1651/1794.9	1550.1	1723	0.15246	7446.8	41157	0.85219	0.80294	0.19706	0.39411	0.66282	True
-ABCF1_m30539291	ABCF1	216.07/289.48	192.42/498.37	252.78	345.4	0.44887	24748	11897	0.84914	0.80005	0.19995	0.3999	0.66917	True
-ABCF1_p30539272	ABCF1	1111.2/1038.9	948.23/922.36	1075.1	935.29	-0.20075	1473.9	27389	0.84464	0.19916	0.80084	0.39831	0.66838	False
-AATK_m79102320	AATK	1474.3/1163.2	1218.9/1844.1	1318.8	1531.5	0.21562	1.2192e+05	63562	0.84385	0.80062	0.19938	0.39875	0.66838	True
-ADD1_m2877771	ADD1	530.62/809.83	815.74/747.27	670.23	781.5	0.2213	20661	17680	0.83689	0.79867	0.20133	0.40265	0.67266	True
-AGL_m100327112	AGL	1142.1/957.24	1162.1/1210.2	1049.7	1186.1	0.17618	9120.3	26669	0.83566	0.79833	0.20167	0.40335	0.67269	True
-ACAD11_p132378489	ACAD11	749.63/1323.1	848.55/870.86	1036.4	859.7	-0.26933	82335	44974	0.833	0.20242	0.79758	0.40484	0.67407	False
-ADH7_m100350735	ADH7	812.84/664.2	774.73/453.74	738.52	614.24	-0.26545	31282	22450	0.82949	0.20341	0.79659	0.40683	0.67624	False
-AAAS_m53715176	AAAS	801.08/158.06	247.31/379.93	479.57	313.62	-0.61114	1.0777e+05	43359	0.82646	0.20427	0.79573	0.40854	0.67796	False
-AHNAK_p62303551	AHNAK	51.445/40.847	117.35/31.47	46.146	74.408	0.67757	1871.7	1174.8	0.82454	0.77515	0.22485	0.4497	0.70415	True
-ADRBK1_m67034219	ADRBK1	1109.8/1138.4	970.31/999.03	1124.1	984.67	-0.19084	411.13	28781	0.82169	0.20563	0.79437	0.41126	0.68051	False
-ADRB2_p148206467	ADRB2	761.39/925.27	649.82/799.34	843.33	724.58	-0.21868	12303	20904	0.82136	0.20572	0.79428	0.41144	0.68051	False
-ADNP2_p77890985	ADNP2	776.09/1280.5	1125.5/1259.4	1028.3	1192.4	0.2135	68076	40069	0.82012	0.79393	0.20607	0.41215	0.68056	True
-ACAD9_m128598614	ACAD9	676.14/966.12	1724.2/528.12	821.13	1126.2	0.45527	3.7868e+05	1.3976e+05	0.81598	0.78979	0.21021	0.42041	0.68195	True
-ADRA1A_m26722434	ADRA1A	1052.4/1303.5	984.82/1087.1	1178	1036	-0.18516	18383	30322	0.8155	0.20739	0.79261	0.41478	0.68056	False
-ADNP_p49510949	ADNP	429.2/889.75	567.8/1132.9	659.48	850.36	0.36626	1.3287e+05	54888	0.81476	0.79189	0.20811	0.41623	0.68056	True
-ADH7_p100350692	ADH7	585.01/452.87	409.45/913.2	518.94	661.32	0.3492	67807	30721	0.81236	0.79139	0.20861	0.41723	0.68056	True
-AFF4_p132272747	AFF4	527.68/113.66	72.552/331.29	320.67	201.92	-0.66466	59590	24616	0.81217	0.20835	0.79165	0.4167	0.68056	False
-ADAM12_p128076650	ADAM12	779.03/648.22	424.59/759.86	713.63	592.22	-0.26861	32379	22366	0.81178	0.20846	0.79154	0.41692	0.68056	False
-ADH5_p100002584	ADH5	2173.9/1182.8	1374.1/1439	1678.4	1406.6	-0.25472	2.4665e+05	1.1219e+05	0.81146	0.20855	0.79145	0.4171	0.68056	False
-ACTR1A_m104248862	ACTR1A	1056.8/2156	1396.2/1228.5	1606.4	1312.3	-0.29153	3.0907e+05	1.3157e+05	0.81082	0.20874	0.79126	0.41747	0.68056	False
-ADRB3_p37823958	ADRB3	2221/976.77	1393/1151.8	1598.9	1272.4	-0.32927	4.0156e+05	1.6225e+05	0.81059	0.2088	0.7912	0.4176	0.68056	False
-AHNAK_p62303510	AHNAK	473.3/451.09	598.08/493.22	462.19	545.65	0.239	2872.4	10707	0.80657	0.79004	0.20996	0.41992	0.68195	True
-ACIN1_p23538730	ACIN1	946.6/470.63	663.07/457.75	708.61	560.41	-0.33799	67176	33874	0.80541	0.21029	0.78971	0.42058	0.68195	False
-ADAD1_p123301314	ADAD1	1456.6/1408.3	1311/1241.6	1432.5	1276.3	-0.16642	1786.1	37697	0.80437	0.21059	0.78941	0.42118	0.68195	False
-AGBL5_p27275876	AGBL5	440.96/630.46	434.68/457.17	535.71	445.93	-0.2641	9104	12617	0.79931	0.21206	0.78794	0.42411	0.68531	False
-ACSS2_m33501222	ACSS2	662.91/777.87	864.95/787.32	720.39	826.14	0.19735	4810.1	17542	0.79841	0.78768	0.21232	0.42463	0.68531	True
-AATF_m35306444	AATF	659.97/809.83	880.72/802.2	734.9	841.46	0.19509	7156	17936	0.79565	0.78688	0.21312	0.42623	0.68679	True
-ADRBK2_m26000349	ADRBK2	263.11/385.38	578.53/260.34	324.24	419.43	0.37036	29048	14494	0.79066	0.78467	0.21533	0.43066	0.69033	True
-ADCK4_m41220249	ADCK4	1481.6/1994.4	2044.1/1811	1738	1927.5	0.14923	79318	57604	0.78959	0.78512	0.21488	0.42977	0.69033	True
-AGTPBP1_m88307605	AGTPBP1	727.59/445.76	502.19/471.48	586.67	486.83	-0.26863	20092	16004	0.78923	0.21499	0.78501	0.42998	0.69033	False
-ACBD6_m180471277	ACBD6	1175.9/518.58	833.41/473.19	847.24	653.3	-0.37452	1.4046e+05	60826	0.78714	0.2156	0.7844	0.4312	0.69033	False
-AATF_m35307516	AATF	518.86/632.24	627.74/308.98	575.55	468.36	-0.29676	28615	18649	0.78499	0.21623	0.78377	0.43246	0.69124	False
-AFTPH_m64778646	AFTPH	999.51/939.48	826.47/1464.2	969.49	1145.3	0.24024	1.0258e+05	50469	0.78274	0.78311	0.21689	0.43378	0.69225	True
-ABLIM2_m8108265	ABLIM2	1587.5/1193.4	1145.7/1324	1390.4	1234.9	-0.17107	46764	39900	0.77891	0.21802	0.78198	0.43603	0.69473	False
-ADRB1_m115803930	ADRB1	440.96/220.22	451.72/361.62	330.59	406.67	0.298	14211	9653.8	0.77431	0.78054	0.21946	0.43892	0.69735	True
-AFAP1L2_m116100394	AFAP1L2	665.85/989.2	859.9/518.4	827.53	689.15	-0.26364	55296	32078	0.77262	0.21987	0.78013	0.43975	0.69735	False
-AGPAT3_m45379606	AGPAT3	376.29/294.81	332.48/204.84	335.55	268.66	-0.31967	5732.6	7498.1	0.77258	0.21988	0.78012	0.43977	0.69735	False
-ACVR1_p158637019	ACVR1	668.79/1074.4	722.37/761.57	871.62	741.97	-0.23205	41524	28298	0.7707	0.22044	0.77956	0.44088	0.69801	False
-ADD1_m2877698	ADD1	477.71/403.14	471.27/246.04	440.42	358.66	-0.29555	14073	11456	0.76401	0.22243	0.77757	0.44486	0.70037	False
-AAAS_p53714441	AAAS	401.27/580.73	689.56/474.34	491	581.95	0.2447	19632	14178	0.76378	0.7775	0.2225	0.445	0.70037	True
-ACTN1_p69445745	ACTN1	868.69/744.12	765.27/632.26	806.41	698.76	-0.20643	8302.4	19888	0.76329	0.22265	0.77735	0.44529	0.70037	False
-ADAM10_p59009761	ADAM10	361.59/237.98	184.85/290.67	299.78	237.76	-0.33316	6619.3	6616.3	0.76281	0.22279	0.77721	0.44558	0.70037	False
-ADD3_m111860445	ADD3	1267/694.4	1038.4/534.42	980.71	786.43	-0.31815	1.4549e+05	64981	0.7623	0.22294	0.77706	0.44588	0.70037	False
-ACVR2B_p38495827	ACVR2B	1159.7/1097.5	1008.2/1576.9	1128.6	1292.5	0.19548	81842	46555	0.75969	0.77628	0.22372	0.44744	0.70171	True
-ADRBK2_m25961092	ADRBK2	1675.7/966.12	1316/1752	1320.9	1534	0.21567	1.7338e+05	80755	0.75004	0.77339	0.22661	0.45323	0.70857	True
-AFF4_m132272813	AFF4	953.95/749.45	755.81/1234.2	851.7	995	0.22411	67669	36646	0.74858	0.77294	0.22706	0.45411	0.70884	True
-AGL_p100327246	AGL	463.01/253.96	201.88/358.19	358.48	280.04	-0.35518	17033	11058	0.74686	0.22757	0.77243	0.45515	0.70935	False
-AFF3_p100623867	AFF3	936.31/561.2	664.33/601.36	748.75	632.84	-0.24229	36168	24264	0.74411	0.22841	0.77159	0.45681	0.71083	False
-ADD3_m111860456	ADD3	1484.6/879.09	1263/687.19	1181.8	975.12	-0.27712	1.7455e+05	78472	0.73797	0.23027	0.76973	0.46054	0.71551	False
-ADCK2_p140373194	ADCK2	1255.3/1015.8	1528/1049.4	1135.6	1288.7	0.18236	71604	43274	0.73618	0.76919	0.23081	0.46162	0.71609	True
-ACSL6_m131329874	ACSL6	795.2/1069.1	1364.6/817.07	932.16	1090.8	0.22657	93708	46815	0.7334	0.76834	0.23166	0.46332	0.7176	True
-AFAP1L2_p116100376	AFAP1L2	220.48/513.25	374.12/537.85	366.86	455.98	0.31297	28130	14897	0.73016	0.76705	0.23295	0.46591	0.71859	True
-ACTR8_m53916091	ACTR8	351.3/781.42	227.75/624.25	566.36	426	-0.41002	85553	37466	0.72943	0.23287	0.76713	0.46574	0.71859	False
-ACAT2_m160183168	ACAT2	658.5/261.06	358.35/354.18	459.78	356.26	-0.3671	39494	20261	0.72882	0.23305	0.76695	0.46611	0.71859	False
-AEBP1_m44144355	AEBP1	524.74/1969.5	1010.7/842.82	1247.1	926.75	-0.42796	5.2889e+05	1.9784e+05	0.7266	0.23374	0.76626	0.46747	0.71902	False
-ADRB1_m115803938	ADRB1	864.28/1175.7	965.26/1358.4	1020	1161.8	0.18766	62873	38178	0.72587	0.76604	0.23396	0.46792	0.71902	True
-ACO2_p41903813	ACO2	1045.1/488.39	1190.5/694.06	766.73	942.27	0.29707	1.3909e+05	58898	0.72332	0.76507	0.23493	0.46986	0.71902	True
-ACVR1B_m52369187	ACVR1B	1039.2/1157.9	1012.6/942.38	1098.6	977.48	-0.16831	4755.4	28055	0.72287	0.23488	0.76512	0.46976	0.71902	False
-ADRB2_p148206462	ADRB2	1352.3/939.48	1104.7/1499.7	1145.9	1302.2	0.18433	81608	46805	0.7225	0.76501	0.23499	0.46999	0.71902	True
-AFAP1L2_m116092987	AFAP1L2	878.98/465.3	635.94/975.57	672.14	805.75	0.26122	71621	34700	0.71726	0.76336	0.23664	0.47328	0.72295	True
-ACTL7A_m111624669	ACTL7A	554.14/381.83	603.13/481.78	467.99	542.45	0.21262	11105	10939	0.71201	0.76177	0.23823	0.47646	0.72669	True
-AFF1_p87967926	AFF1	792.26/1030	1313.5/799.34	911.16	1056.4	0.2132	80230	41932	0.70942	0.76097	0.23903	0.47807	0.72803	True
-ADAM10_m59009866	ADAM10	610/626.91	339.42/695.77	618.45	517.6	-0.25638	31819	20475	0.70487	0.24045	0.75955	0.48089	0.73	False
-ADARB1_p46595705	ADARB1	1987.3/1898.5	1793.6/2477.5	1942.9	2135.6	0.13637	1.1891e+05	74917	0.70405	0.7593	0.2407	0.4814	0.73	True
-ACTN4_p39138403	ACTN4	837.83/1486.5	1100.3/1614.7	1162.1	1357.5	0.22396	1.7134e+05	77026	0.70382	0.75923	0.24077	0.48155	0.73	True
-ACO2_p41865164	ACO2	646.74/310.79	666.22/487.5	478.77	576.86	0.26838	36201	19490	0.70263	0.75878	0.24122	0.48243	0.73023	True
-AEBP1_p44144293	AEBP1	901.03/1152.6	991.13/1306.9	1026.8	1149	0.16205	40743	30930	0.69475	0.75639	0.24361	0.48721	0.73496	True
-ADCK2_m140373177	ADCK2	582.07/804.5	745.08/427.42	693.29	586.25	-0.24156	37597	23739	0.69472	0.24362	0.75638	0.48723	0.73496	False
-ACAT2_p160183141	ACAT2	1243.5/843.58	613.86/1148.4	1043.5	881.11	-0.24384	1.1141e+05	54802	0.69387	0.24388	0.75612	0.48776	0.73496	False
-ACTR5_m37377155	ACTR5	95.542/369.4	415.76/207.7	232.47	311.73	0.42168	29571	13180	0.69038	0.74965	0.25035	0.50069	0.74006	True
-AFMID_p76183465	AFMID	158.75/598.49	317.97/236.88	378.62	277.43	-0.44726	49988	22380	0.69003	0.24509	0.75491	0.49018	0.73555	False
-ABI1_m27149743	ABI1	1412.5/703.28	808.8/964.13	1057.9	886.46	-0.25482	1.318e+05	61867	0.68931	0.24531	0.75469	0.49063	0.73555	False
-ACTR1A_p104262374	ACTR1A	1337.6/722.81	750.13/991.02	1030.2	870.57	-0.24263	1.0899e+05	53744	0.68856	0.24555	0.75445	0.4911	0.73555	False
-ACAD11_m132378578	ACAD11	526.21/593.17	497.77/463.47	559.69	480.62	-0.21931	1414.9	13247	0.687	0.24604	0.75396	0.49208	0.73555	False
-ABHD14B_m52004113	ABHD14B	579.13/651.77	926.78/517.82	615.45	722.3	0.23061	43130	24192	0.68696	0.75394	0.24606	0.49212	0.73555	True
-ABCB8_p150725675	ABCB8	943.66/756.55	615.12/1467.6	850.11	1041.4	0.29247	1.9045e+05	77545	0.68688	0.75364	0.24636	0.49271	0.73555	True
-ADD1_m2877722	ADD1	479.18/362.29	508.5/467.47	420.74	487.98	0.21345	3836.3	9643.7	0.6848	0.75326	0.24674	0.49347	0.73555	True
-AGFG1_m228337217	AGFG1	336.6/618.03	361.5/429.14	477.32	395.32	-0.2713	20944	14379	0.68388	0.24702	0.75298	0.49405	0.73555	False
-ABL2_p179100556	ABL2	605.59/502.59	842.87/465.18	554.09	654.03	0.23883	38313	21504	0.68149	0.7522	0.2478	0.4956	0.73676	True
-ABCF1_p30545605	ABCF1	501.23/387.16	436.58/314.7	444.19	375.64	-0.24125	6966.4	10244	0.67735	0.24909	0.75091	0.49819	0.73951	False
-ADCK1_p78285341	ADCK1	1168.5/907.51	879.46/977.86	1038	928.66	-0.16046	19456	26340	0.67388	0.25019	0.74981	0.50039	0.74006	False
-ACSS2_m33500940	ACSS2	1655.1/943.03	1069.4/2038.1	1299.1	1553.7	0.2581	3.6137e+05	1.43e+05	0.6735	0.74961	0.25039	0.50078	0.74006	True
-ACO2_p41895844	ACO2	264.58/525.68	376.01/576.76	395.13	476.38	0.26918	27119	15035	0.66268	0.74607	0.25393	0.50786	0.74941	True
-AFF2_m147924509	AFF2	122/81.694	31.544/115.01	101.85	73.276	-0.46951	2147.7	2043.7	0.65991	0.25466	0.74534	0.50931	0.75045	False
-AFAP1L2_m116100466	AFAP1L2	457.13/433.33	488.94/534.42	445.23	511.68	0.20027	658.65	10270	0.65568	0.74398	0.25602	0.51203	0.75335	True
-ACP1_p272099	ACP1	352.77/325	345.73/444.01	338.88	394.87	0.21999	2607.8	7581.1	0.64301	0.73988	0.26012	0.52024	0.7643	True
-AHCTF1_m247068844	AHCTF1	1106.8/1056.7	1743.1/807.35	1081.8	1275.2	0.23721	2.1956e+05	91572	0.63943	0.73868	0.26132	0.52263	0.76668	True
-ADCY1_m45614206	ADCY1	179.32/312.57	409.45/200.84	245.95	305.14	0.31001	15318	8644.5	0.63668	0.73676	0.26324	0.52648	0.7694	True
-ACTR1A_p104248833	ACTR1A	804.02/745.9	651.08/1125.5	774.96	888.28	0.19666	57109	31721	0.63626	0.7377	0.2623	0.52461	0.76845	True
-A1CF_m52603842	A1CF	402.74/431.56	198.1/489.22	417.15	343.66	-0.27886	21395	13500	0.6329	0.2634	0.7366	0.5268	0.7694	False
-ACVR1C_m158401097	ACVR1C	961.3/788.52	982.3/952.68	874.91	967.49	0.14496	7682	21777	0.62737	0.73479	0.26521	0.53042	0.77356	True
-ACVR1C_p158443795	ACVR1C	1481.6/1044.3	1328.7/1444.2	1262.9	1386.4	0.13447	51161	38897	0.62607	0.73437	0.26563	0.53127	0.77367	True
-ACTL6A_p179287920	ACTL6A	1202.4/1166.8	1205/946.39	1184.6	1075.7	-0.13898	17036	30511	0.62335	0.26653	0.73347	0.53305	0.77462	False
-AFF4_m132272866	AFF4	1199.4/1466.9	1408.1/1490.5	1333.2	1449.3	0.12044	19588	34799	0.62272	0.73326	0.26674	0.53347	0.77462	True
-ABI1_p27149733	ABI1	123.47/470.63	249.2/199.12	297.05	224.16	-0.40459	30757	14617	0.61835	0.26817	0.73183	0.53634	0.77589	False
-AGPAT3_p45379613	AGPAT3	598.24/305.46	466.86/275.22	451.85	371.04	-0.28358	30611	17164	0.61746	0.26847	0.73153	0.53693	0.77589	False
-ADAD1_m123301308	ADAD1	780.5/754.78	553.92/1272.5	767.64	913.23	0.25024	1.2927e+05	55640	0.6172	0.7313	0.2687	0.53741	0.77589	True
-ACSS2_p33470688	ACSS2	880.45/621.58	671.9/1040.8	751.02	856.35	0.18911	50775	29175	0.61667	0.73127	0.26873	0.53746	0.77589	True
-ADAMTS5_m28338537	ADAMTS5	981.87/1282.2	680.73/1288.6	1132.1	984.64	-0.20108	1.1492e+05	57645	0.61399	0.26961	0.73039	0.53923	0.77732	False
-ABL2_p179095768	ABL2	535.03/577.18	807.54/470.33	556.11	638.94	0.19997	28871	18392	0.61074	0.72931	0.27069	0.54138	0.77843	True
-ADAM12_p128076618	ADAM12	120.53/232.65	192.42/234.59	176.59	213.51	0.27249	3587.3	3672.2	0.60923	0.72833	0.27167	0.54334	0.77843	True
-AES_p3061186	AES	238.12/266.39	169.71/461.18	252.26	315.44	0.32135	21438	10786	0.60843	0.72648	0.27352	0.54704	0.77843	True
-AFTPH_p64778754	AFTPH	849.59/861.33	762.11/771.87	855.46	766.99	-0.15729	58.314	21239	0.60703	0.27191	0.72809	0.54383	0.77843	False
-ABL1_p133729469	ABL1	930.43/1090.4	1011.3/1203.3	1010.4	1107.3	0.13196	15614	25562	0.60593	0.72772	0.27228	0.54456	0.77843	True
-ABI1_p27112180	ABI1	455.66/380.05	335.63/381.64	417.86	358.64	-0.21991	1958.4	9570.3	0.60534	0.27248	0.72752	0.54495	0.77843	False
-ACTR3_p114684946	ACTR3	770.21/548.77	752.65/367.91	659.49	560.28	-0.23481	49265	27022	0.60358	0.27306	0.72694	0.54612	0.77843	False
-ADD1_p2877743	ADD1	573.25/1397.7	728.68/910.34	985.46	819.51	-0.26574	1.7817e+05	75963	0.6025	0.27342	0.72658	0.54684	0.77843	False
-ACRC_p70814187	ACRC	961.3/1097.5	959.58/905.19	1029.4	932.39	-0.14268	5380	26097	0.60062	0.27405	0.72595	0.54809	0.77843	False
-ACIN1_p23538719	ACIN1	640.86/559.42	711.64/632.26	600.14	671.95	0.16279	3233.6	14317	0.60014	0.72579	0.27421	0.54841	0.77843	True
-AFF2_m147967470	AFF2	892.21/1278.7	893.97/1599.8	1085.4	1246.9	0.19988	1.619e+05	72420	0.59993	0.72572	0.27428	0.54857	0.77843	True
-AGTPBP1_p88307705	AGTPBP1	655.56/449.31	451.72/516.68	552.44	484.2	-0.18985	11690	13056	0.59722	0.27518	0.72482	0.55036	0.7796	False
-AGFG1_p228337142	AGFG1	0/42.623	0/78.389	21.311	39.194	0.84922	1990.4	897.03	0.59709	0.63864	0.36136	0.72272	0.86416	True
-AFTPH_p64778649	AFTPH	1724.2/1500.7	1326.8/1650.7	1612.4	1488.8	-0.11505	38727	43003	0.59634	0.27547	0.72453	0.55095	0.7796	False
-AFF1_m87967374	AFF1	257.23/191.8	170.34/379.93	224.51	275.13	0.29215	12052	7214.6	0.59595	0.72326	0.27674	0.55348	0.78097	True
-AFF2_m147891407	AFF2	355.71/374.72	271.91/602.51	365.22	437.21	0.25892	27413	14631	0.5952	0.72379	0.27621	0.55241	0.78056	True
-ADRB3_p37823913	ADRB3	454.19/381.83	525.53/425.7	418.01	475.62	0.18585	3800.5	9574.2	0.58875	0.72198	0.27802	0.55604	0.78336	True
-AFMID_p76183445	AFMID	911.32/856.01	938.13/1003.6	883.66	970.87	0.13563	1836.6	22020	0.58768	0.72163	0.27837	0.55674	0.78336	True
-ABHD14B_p52004019	ABHD14B	41.156/284.15	169.08/259.77	162.65	214.42	0.39653	16818	7840.3	0.58468	0.71105	0.28895	0.5779	0.79519	True
-AGPAT5_m6566212	AGPAT5	2451.7/2452.6	2151.3/2447.8	2452.2	2299.6	-0.092657	21972	68578	0.58273	0.28004	0.71996	0.56008	0.78694	False
-ACLY_p40070039	ACLY	314.55/252.18	88.955/359.9	283.37	224.43	-0.33509	19325	10584	0.57916	0.28124	0.71876	0.56248	0.78852	False
-ABHD14B_m52004073	ABHD14B	473.3/339.21	388.63/312.41	406.25	350.52	-0.21232	5947.5	9275.2	0.57872	0.28139	0.71861	0.56278	0.78852	False
-AFF1_m87967978	AFF1	549.73/365.85	375.38/697.49	457.79	536.43	0.22826	34392	18526	0.57781	0.71819	0.28181	0.56361	0.78858	True
-ACSS2_m33470722	ACSS2	734.94/358.74	541.93/378.78	546.84	460.36	-0.24786	42035	22618	0.57532	0.28254	0.71746	0.56508	0.78884	False
-ADAR_p154574157	ADAR	1030.4/522.13	676.31/663.73	776.25	670.02	-0.21203	64619	34248	0.57406	0.28296	0.71704	0.56593	0.78884	False
-ABL2_p179100578	ABL2	623.23/570.08	549.5/506.95	596.65	528.23	-0.17541	1158.8	14224	0.57371	0.28308	0.71692	0.56616	0.78884	False
-ABHD14B_p52004076	ABHD14B	2079.9/1886.1	2073.1/1622.7	1983	1847.9	-0.10171	60105	56127	0.57007	0.28431	0.71569	0.56863	0.79112	False
-ADAMTS5_m28338702	ADAMTS5	602.65/497.27	627.1/602.51	549.96	614.81	0.16054	2927.6	12991	0.56896	0.71531	0.28469	0.56938	0.79112	True
-AGAP2_p58128423	AGAP2	421.85/511.47	400.62/414.83	466.66	407.72	-0.19434	2058.5	10822	0.56659	0.2855	0.7145	0.571	0.7914	False
-ADI1_m3523239	ADI1	579.13/316.12	406.29/636.84	447.62	521.57	0.22011	30581	17082	0.56575	0.71413	0.28587	0.57174	0.7914	True
-ACTR5_m37377161	ACTR5	342.48/548.77	531.21/203.12	445.62	367.17	-0.2787	37549	19370	0.56516	0.28598	0.71402	0.57196	0.7914	False
-ADRBK2_m26000370	ADRBK2	711.42/431.56	571.59/423.41	571.49	497.5	-0.19965	25069	17395	0.56098	0.28741	0.71259	0.57481	0.79333	False
-ADAM10_p58974481	ADAM10	1339.1/1152.6	1468.1/1225	1245.8	1346.6	0.1121	23459	32271	0.56078	0.71253	0.28747	0.57495	0.79333	True
-ACTL6B_p100253094	ACTL6B	945.13/1408.3	1386.1/645.99	1176.7	1016	-0.21164	1.9057e+05	83712	0.55546	0.28929	0.71071	0.57858	0.79519	False
-AHCY_m32883267	AHCY	789.32/671.31	723.63/588.77	730.31	656.2	-0.15415	8028.3	17811	0.55531	0.28934	0.71066	0.57868	0.79519	False
-AGTPBP1_p88307675	AGTPBP1	1175.9/1147.3	909.11/1223.3	1161.6	1066.2	-0.12347	24887	29852	0.55193	0.2905	0.7095	0.58099	0.79727	False
-ADCK3_p227149079	ADCK3	2312.1/1088.7	1301.5/1652.5	1700.4	1477	-0.20307	4.05e+05	1.6541e+05	0.54929	0.2914	0.7086	0.58281	0.79866	False
-AEBP1_m44144289	AEBP1	1960.8/2106.3	1629/2164	2033.5	1896.5	-0.10062	76855	62737	0.54723	0.29211	0.70789	0.58422	0.79877	False
-ADRBK2_p26040592	ADRBK2	426.26/607.37	258.66/615.09	516.82	436.88	-0.24191	39961	21402	0.54684	0.29224	0.70776	0.58449	0.79877	False
-ACTN4_m39138527	ACTN4	214.6/159.84	251.09/38.336	187.22	144.72	-0.36926	12066	6634.6	0.54379	0.29329	0.70671	0.58658	0.80054	False
-AHRR_m353878	AHRR	1.4699/83.47	0/62.94	42.47	31.47	-0.4209	2671.3	1392.8	0.54162	0.29404	0.70596	0.58808	0.80117	False
-ABCC1_m16101777	ABCC1	699.66/728.14	938.13/639.13	713.9	788.63	0.14344	22554	19096	0.5408	0.70568	0.29432	0.58865	0.80117	True
-AHNAK_p62303477	AHNAK	655.56/500.82	554.55/475.48	578.19	515.02	-0.16662	7549.5	13735	0.53903	0.29493	0.70507	0.58987	0.80117	False
-A1CF_p52601638	A1CF	1093.6/815.16	777.89/1377.2	954.37	1077.6	0.17498	1.0919e+05	52388	0.53823	0.70479	0.29521	0.59043	0.80117	True
-ACTRT3_p169487280	ACTRT3	1469.9/1001.6	1264.9/975	1235.8	1120	-0.14182	75827	46596	0.53639	0.29584	0.70416	0.59169	0.80117	False
-ACVR2A_p148653997	ACVR2A	1603.6/1811.5	1557.7/1627.9	1707.5	1592.8	-0.10034	12031	45836	0.53615	0.29593	0.70407	0.59186	0.80117	False
-AFF3_p100623873	AFF3	318.96/438.66	435.31/210.56	378.81	322.94	-0.22956	16210	11124	0.53008	0.29803	0.70197	0.59606	0.80226	False
-ACVRL1_p52306287	ACVRL1	1687.4/1525.5	1391.1/2109.1	1606.5	1750.1	0.12345	1.3541e+05	73689	0.52904	0.70161	0.29839	0.59678	0.80226	True
-ADIRF_p88728292	ADIRF	1074.5/950.13	1100.9/743.84	1012.3	922.37	-0.13409	35740	28990	0.52821	0.29868	0.70132	0.59736	0.80226	False
-ADD3_m111860540	ADD3	1167.1/1316	1367.1/901.19	1241.5	1134.2	-0.13038	59821	41372	0.52785	0.2988	0.7012	0.5976	0.80226	False
-ADRA1A_p26722329	ADRA1A	1571.3/1456.3	1434/1382.4	1513.8	1408.2	-0.10423	3973.3	40085	0.52735	0.29897	0.70103	0.59795	0.80226	False
-AAAS_p53714405	AAAS	1644.8/1520.2	1395.5/1553.5	1582.5	1474.5	-0.10191	10116	42116	0.52625	0.29936	0.70064	0.59871	0.80226	False
-ACRC_p70800697	ACRC	232.24/681.96	275.7/467.47	457.1	371.59	-0.2981	59757	26969	0.52615	0.29939	0.70061	0.59879	0.80226	False
-AGFG1_p228337168	AGFG1	667.32/667.76	492.09/709.5	667.54	600.8	-0.15173	11817	16116	0.52571	0.29954	0.70046	0.59909	0.80226	False
-ADRBK2_p25960997	ADRBK2	1311.1/429.78	1237.2/823.37	870.45	1030.3	0.24293	2.37e+05	93436	0.52285	0.6988	0.3012	0.60241	0.80403	True
-AEN_m89169507	AEN	567.37/737.02	333.11/790.18	652.19	561.65	-0.21528	59424	30278	0.52055	0.30134	0.69866	0.60268	0.80403	False
-ADARB2_m1779246	ADARB2	1863.8/1585.9	1761.4/1464.2	1724.9	1612.8	-0.096827	41390	46354	0.52035	0.30141	0.69859	0.60282	0.80403	False
-AGAP2_p58129143	AGAP2	756.98/500.82	792.4/260.91	628.9	526.66	-0.25553	87024	39063	0.51878	0.30196	0.69804	0.60391	0.80441	False
-ABT1_m26597238	ABT1	488/367.62	312.29/441.15	427.81	376.72	-0.18301	7773.9	9824.3	0.51545	0.30312	0.69688	0.60624	0.80644	False
-ADH7_p100349706	ADH7	464.48/394.26	171.6/547.01	429.37	359.3	-0.25636	36465	18731	0.51365	0.30375	0.69625	0.6075	0.80704	False
-ADRA1A_p26722412	ADRA1A	1012.7/983.87	1008.8/825.66	998.31	917.23	-0.12208	8592.9	25221	0.51056	0.30483	0.69517	0.60966	0.80883	False
-AGAP3_m150784003	AGAP3	1387.6/1120.6	1214.5/1111.2	1254.1	1162.8	-0.10892	20481	32510	0.50621	0.30636	0.69364	0.61271	0.81147	False
-ADCK5_p145597774	ADCK5	576.19/541.66	670.64/303.83	558.93	487.23	-0.19767	33935	20130	0.5054	0.30664	0.69336	0.61328	0.81147	False
-ACAD11_p132378547	ACAD11	51.445/465.3	251.09/143.05	258.37	197.07	-0.38901	45737	18984	0.50398	0.30714	0.69286	0.61428	0.81172	False
-AGL_p100327114	AGL	47.036/147.4	239.74/30.898	97.22	135.32	0.47289	13422	5734.9	0.50309	0.65854	0.34146	0.68293	0.84021	True
-ACVR1_m158637054	ACVR1	291.03/866.66	570.32/389.08	578.85	479.7	-0.27053	91049	39518	0.50227	0.30774	0.69226	0.61548	0.81224	False
-AFF4_m132272841	AFF4	241.06/637.57	282.64/802.2	439.31	542.42	0.30354	1.0679e+05	42342	0.50107	0.68671	0.31329	0.62659	0.81924	True
-ACRC_p70800656	ACRC	258.7/685.52	533.73/560.74	472.11	547.24	0.21263	45726	22550	0.5003	0.69131	0.30869	0.61738	0.8126	True
-ADCY1_p45614284	ADCY1	435.08/504.37	371.59/463.47	469.73	417.53	-0.16955	3310.3	10901	0.49993	0.30856	0.69144	0.61713	0.8126	False
-ADD3_p111860545	ADD3	1381.7/1209.4	1247.3/1161	1295.5	1204.1	-0.10551	9280.7	33708	0.49803	0.30923	0.69077	0.61846	0.81295	False
-AGFG1_m228337208	AGFG1	671.73/781.42	554.55/766.72	726.57	660.64	-0.13705	14262	17710	0.49547	0.31013	0.68987	0.62027	0.81426	False
-ADNP_m49520451	ADNP	1649.2/783.19	773.47/1332	1216.2	1052.8	-0.20802	2.6549e+05	1.0944e+05	0.49427	0.31056	0.68944	0.62111	0.81429	False
-ADRM1_p60878682	ADRM1	1011.3/713.93	792.4/1100.3	862.6	946.35	0.13353	45805	29559	0.48712	0.68691	0.31309	0.62617	0.81924	True
-ADAP1_m975116	ADAP1	586.48/335.65	379.8/433.71	461.07	406.75	-0.1804	16455	12603	0.48382	0.31426	0.68574	0.62851	0.81924	False
-AFTPH_p64778890	AFTPH	620.29/529.23	366.55/657.44	574.76	511.99	-0.16653	23227	16839	0.48371	0.31429	0.68571	0.62859	0.81924	False
-AES_m3057717	AES	999.51/1026.5	938.13/1242.2	1013	1090.2	0.10581	23297	25635	0.48195	0.68508	0.31492	0.62984	0.81924	True
-ADRB3_p37823900	ADRB3	180.79/239.75	279.48/205.41	210.27	242.45	0.20451	2240.7	4458.9	0.48184	0.68478	0.31522	0.63043	0.81924	True
-ADCY1_m45614270	ADCY1	667.32/614.48	543.2/873.15	640.9	708.17	0.14379	27915	19573	0.48085	0.68469	0.31531	0.63063	0.81924	True
-ACTRT3_m169487252	ACTRT3	283.69/854.23	348.88/597.93	568.96	473.41	-0.26473	96887	41290	0.47513	0.31735	0.68265	0.6347	0.82346	False
-ABCC1_p16101667	ABCC1	320.43/248.63	247.31/397.67	284.53	322.49	0.18006	6940.6	6474.6	0.4717	0.68136	0.31864	0.63727	0.82573	True
-ABCC1_p16101721	ABCC1	1480.2/1214.7	882.61/1568.3	1347.5	1225.5	-0.13678	1.3517e+05	68533	0.46592	0.32064	0.67936	0.64127	0.82911	False
-AFMID_m76183481	AFMID	701.13/978.55	617.01/908.62	839.84	762.82	-0.1386	40500	27372	0.46554	0.32077	0.67923	0.64155	0.82911	False
-ABL2_m179100554	ABL2	1055.4/722.81	915.42/1007.6	889.09	961.52	0.11286	29773	24704	0.4608	0.67753	0.32247	0.64494	0.8294	True
-AAK1_m69870049	AAK1	535.03/825.82	437.21/1147.8	680.42	792.5	0.21968	1.4737e+05	60100	0.45717	0.67533	0.32467	0.64934	0.8294	True
-ADK_p76074446	ADK	1505.1/451.09	653.6/991.02	978.12	822.31	-0.25005	3.0622e+05	1.1851e+05	0.45682	0.3239	0.6761	0.6478	0.8294	False
-ACP1_m264989	ACP1	1456.6/1333.7	1753.9/1236.5	1395.2	1495.2	0.099788	70699	47970	0.45653	0.67599	0.32401	0.64801	0.8294	True
-ADNP2_p77875511	ADNP2	1293.5/1303.5	1006.9/1416.1	1298.5	1211.5	-0.099961	41896	36495	0.45537	0.32442	0.67558	0.64885	0.8294	False
-ADAD1_m123301345	ADAD1	514.45/468.85	733.09/374.21	491.65	553.65	0.17101	32720	18552	0.45517	0.67546	0.32454	0.64908	0.8294	True
-ACTR5_p37377204	ACTR5	291.03/305.46	359.61/310.69	298.25	335.15	0.16776	650.16	6577.1	0.45502	0.67542	0.32458	0.64917	0.8294	True
-A1CF_m52595977	A1CF	313.08/486.61	557.08/100.13	399.85	328.6	-0.28232	59727	25984	0.45424	0.32483	0.67517	0.64966	0.8294	False
-ACSL6_p131329878	ACSL6	908.38/1435	998.7/1151.8	1171.7	1075.2	-0.12379	75183	45155	0.45376	0.325	0.675	0.65	0.8294	False
-ACD_p67694194	ACD	1439/1046	1161.5/1155.8	1242.5	1158.6	-0.10075	38614	34322	0.45277	0.32536	0.67464	0.65071	0.8294	False
-ABCC1_p16043597	ABCC1	307.2/412.02	305.35/332.44	359.61	318.89	-0.17285	2930.1	8098.6	0.45251	0.32545	0.67455	0.6509	0.8294	False
-ADPRHL2_m36554563	ADPRHL2	1209.7/420.9	786.72/609.95	815.3	698.33	-0.22312	1.6337e+05	67877	0.4506	0.32614	0.67386	0.65228	0.8301	False
-ABT1_m26597359	ABT1	358.65/394.26	593.04/268.35	376.45	430.69	0.19371	26672	14572	0.44933	0.67311	0.32689	0.65379	0.83096	True
-AEBP2_m19615553	AEBP2	1371.4/534.56	865.58/796.48	952.97	831.03	-0.19732	1.7626e+05	74721	0.44657	0.32759	0.67241	0.65519	0.83141	False
-ADRA1B_p159343940	ADRA1B	1074.5/1252	1269.4/1211.3	1163.3	1240.3	0.092473	8724.8	29900	0.4457	0.67209	0.32791	0.65581	0.83141	True
-ACVR2B_p38518854	ACVR2B	2146/1630.3	1712.9/1851	1888.2	1781.9	-0.083494	71255	57928	0.44137	0.32947	0.67053	0.65895	0.83325	False
-ACD_p67694350	ACD	543.85/245.08	295.89/388.51	394.47	342.2	-0.20451	24461	14138	0.44042	0.32982	0.67018	0.65963	0.83325	False
-AGAP3_p150783915	AGAP3	837.83/719.26	374.75/993.88	778.54	684.31	-0.18586	99345	45865	0.44025	0.32988	0.67012	0.65976	0.83325	False
-ADAM12_m128019025	ADAM12	560.02/861.33	956.43/613.95	710.68	785.19	0.14365	52020	28860	0.43861	0.66952	0.33048	0.66095	0.8337	True
-ACTN1_p69392370	ACTN1	2125.4/1632.1	1962.7/1996.9	1878.8	1979.8	0.075535	61138	54367	0.43334	0.66762	0.33238	0.66477	0.83695	True
-ACVR1B_m52369211	ACVR1B	1512.5/1339.1	1062.4/1594.1	1425.8	1328.3	-0.10214	78190	51064	0.43157	0.33303	0.66697	0.66605	0.83695	False
-ADCY1_p45614294	ADCY1	149.93/312.57	287.69/239.74	231.25	263.71	0.18878	7187.5	5700	0.43005	0.66606	0.33394	0.66789	0.83695	True
-ADRB3_p37823952	ADRB3	1233.2/1458.1	1519.8/1332.6	1345.6	1426.2	0.083838	21399	35162	0.4297	0.66629	0.33371	0.66742	0.83695	True
-ACTN1_p69445708	ACTN1	179.32/252.18	249.2/240.32	215.75	244.76	0.18118	1346.9	4588.3	0.42819	0.6655	0.3345	0.669	0.83695	True
-ACSL6_p131329824	ACSL6	1027.4/978.55	1338.1/414.83	1003	876.47	-0.19432	2.1371e+05	88139	0.42682	0.33475	0.66525	0.66951	0.83695	False
-ACTL6B_p100253037	ACTL6B	1394.9/816.94	642.25/1321.2	1105.9	981.71	-0.17172	1.9875e+05	85092	0.42596	0.33507	0.66493	0.67014	0.83695	False
-AAK1_m69870119	AAK1	595.3/488.39	508.5/478.92	541.84	493.71	-0.13396	3076.3	12778	0.42583	0.33512	0.66488	0.67023	0.83695	False
-ACBD6_m180471378	ACBD6	592.36/577.18	782.93/494.36	584.77	638.65	0.12695	20876	16231	0.4229	0.66382	0.33618	0.67237	0.83796	True
-ABI1_m27149764	ABI1	204.31/332.1	203.15/268.93	268.21	236.04	-0.18361	5164.3	5844.6	0.42122	0.3368	0.6632	0.67359	0.83796	False
-ADRA1B_m159343934	ADRA1B	748.16/1200.5	1029.6/751.27	974.35	890.44	-0.12978	70529	39875	0.4202	0.33717	0.66283	0.67434	0.83796	False
-AEBP2_p19615440	AEBP2	1030.4/1092.2	878.2/1106.6	1061.3	992.4	-0.096738	13997	26998	0.4193	0.3375	0.6625	0.675	0.83796	False
-AHRR_m353898	AHRR	561.49/170.49	199.36/411.97	365.99	305.67	-0.25908	49521	22013	0.41892	0.33764	0.66236	0.67528	0.83796	False
-ADPRHL2_p36554568	ADPRHL2	492.41/793.85	555.81/863.99	643.13	709.9	0.14231	46460	25795	0.41577	0.6612	0.3388	0.6776	0.83796	True
-AFMID_p76187060	AFMID	454.19/1129.5	697.13/699.21	791.85	698.17	-0.1814	1.1401e+05	50997	0.41523	0.33899	0.66101	0.67797	0.83796	False
-ADAP1_m975132	ADAP1	956.89/941.25	1210.7/826.8	949.07	1018.7	0.1021	36901	28194	0.41493	0.6609	0.3391	0.6782	0.83796	True
-ADAD1_m123301332	ADAD1	1045.1/1118.8	1071.9/954.4	1082	1013.1	-0.094726	4811	27584	0.41438	0.3393	0.6607	0.67859	0.83796	False
-ADNP_p49510837	ADNP	495.35/301.91	336.9/555.02	398.63	445.96	0.16147	21248	13137	0.41291	0.66008	0.33992	0.67985	0.83796	True
-ADAM10_p59009767	ADAM10	886.33/751.23	969.05/521.83	818.78	745.44	-0.13521	54564	31673	0.4121	0.34013	0.65987	0.68027	0.83796	False
-ACSS2_m33500955	ACSS2	626.17/884.42	517.33/858.27	755.29	687.8	-0.13486	45735	27572	0.40647	0.3422	0.6578	0.6844	0.84098	False
-AFAP1L2_p116100433	AFAP1L2	333.66/296.58	346.36/216.28	315.12	281.32	-0.16314	4573.5	6992.3	0.40436	0.34297	0.65703	0.68595	0.84101	False
-AHNAK2_m105423801	AHNAK2	1215.6/1655.2	1491.4/1543.7	1435.4	1517.6	0.080286	48996	41520	0.40341	0.65668	0.34332	0.68664	0.84101	True
-ACTL6B_m100253076	ACTL6B	924.55/1115.3	1301.5/888.03	1019.9	1094.8	0.10208	51842	34500	0.403	0.65652	0.34348	0.68695	0.84101	True
-ADRB2_m148206447	ADRB2	826.07/822.26	880.09/654	824.17	767.05	-0.10349	12783	20376	0.40014	0.34453	0.65547	0.68905	0.84176	False
-AGTPBP1_m88307687	AGTPBP1	1988.7/1243.2	1431.5/1579.2	1615.9	1505.4	-0.10221	1.4443e+05	76881	0.39887	0.345	0.655	0.68999	0.84176	False
-ADRBK2_m25961108	ADRBK2	1031.8/392.48	904.7/696.34	712.17	800.52	0.1685	1.1305e+05	49230	0.39821	0.65453	0.34547	0.69094	0.84176	True
-ADH7_m100350690	ADH7	170.51/715.71	366.55/382.22	443.11	374.38	-0.24254	74373	31601	0.39783	0.34538	0.65462	0.69076	0.84176	False
-ADD1_p2877644	ADD1	820.19/1019.4	1227.7/763.29	919.79	995.5	0.114	63843	36630	0.39558	0.65379	0.34621	0.69242	0.84235	True
-ACLY_m40070116	ACLY	1009.8/1102.9	1237.2/1004.8	1056.3	1121	0.085596	15670	26858	0.39437	0.65335	0.34665	0.69331	0.84235	True
-ADD1_m2877776	ADD1	565.9/438.66	539.41/550.44	502.28	544.92	0.11734	4078	11744	0.39351	0.65303	0.34697	0.69395	0.84235	True
-ADAR_m154574108	ADAR	1594.8/1092.2	1489.5/1000.2	1343.5	1244.9	-0.10995	1.2302e+05	64407	0.38874	0.34873	0.65127	0.69747	0.84559	False
-ACTR8_m53916103	ACTR8	903.97/1445.6	1482.6/1055.1	1174.8	1268.8	0.11102	1.1903e+05	59831	0.3845	0.6497	0.3503	0.70061	0.84801	True
-AATK_m79102325	AATK	930.43/1282.2	1182.3/1161.5	1106.3	1171.9	0.083003	31050	29201	0.38375	0.64942	0.35058	0.70116	0.84801	True
-ADPRHL2_p36554580	ADPRHL2	849.59/935.92	934.35/739.83	892.75	837.09	-0.092776	11323	22272	0.373	0.35457	0.64543	0.70915	0.85664	False
-ADAM10_m59041689	ADAM10	89.662/99.453	49.84/176.8	94.558	113.32	0.25866	4053.9	2573.9	0.36987	0.63281	0.36719	0.73438	0.86924	True
-ACVR1_m158655997	ACVR1	1349.3/2315.8	1698.4/1722.3	1832.6	1710.3	-0.099568	2.3367e+05	1.1095e+05	0.3671	0.35677	0.64323	0.71354	0.8609	False
-ADCK3_p227149124	ADCK3	1208.2/1010.5	1032.8/1063.1	1109.4	1047.9	-0.082114	10004	28363	0.36479	0.35764	0.64236	0.71527	0.86195	False
-ACP1_m272095	ACP1	511.52/669.53	495.25/783.89	590.52	639.57	0.11492	27071	18398	0.36158	0.64117	0.35883	0.71767	0.8638	True
-ADAP1_m975089	ADAP1	471.83/632.24	523.64/662.59	552.03	593.11	0.10337	11259	13046	0.35966	0.64045	0.35955	0.7191	0.86416	True
-AEN_p89169516	AEN	1444.9/1069.1	1717.3/994.45	1257	1355.9	0.10914	1.6592e+05	77036	0.3562	0.63915	0.36085	0.72169	0.86416	True
-AES_m3057678	AES	868.69/792.07	746.34/1016.2	830.38	881.27	0.085704	19673	20547	0.35499	0.6387	0.3613	0.7226	0.86416	True
-ADI1_m3523156	ADI1	768.74/722.81	643.51/948.68	745.78	796.09	0.09407	23809	20091	0.35498	0.6387	0.3613	0.72261	0.86416	True
-ACVR1_p158637035	ACVR1	2013.7/1378.1	1688.3/1887.1	1695.9	1787.7	0.075951	1.1087e+05	67284	0.35363	0.63819	0.36181	0.72362	0.86416	True
-ACD_m67694316	ACD	279.28/451.09	408.82/385.65	365.18	397.23	0.12105	7514.3	8238.3	0.35311	0.63799	0.36201	0.72402	0.86416	True
-ACVR1C_m158401113	ACVR1C	329.25/328.55	306.61/410.25	328.9	358.43	0.12369	2685.5	7333.1	0.34487	0.63488	0.36512	0.73024	0.86924	True
-ADCK3_m227149153	ADCK3	1240.6/2438.4	1870/2081.6	1839.5	1975.8	0.10307	3.6988e+05	1.5649e+05	0.34456	0.63479	0.36521	0.73043	0.86924	True
-ACSS2_m33501214	ACSS2	680.55/747.67	533.1/1016.2	714.11	774.65	0.11723	59471	31405	0.3416	0.63366	0.36634	0.73268	0.86924	True
-ACD_p67694285	ACD	608.53/495.49	551.4/630.54	552.01	590.97	0.098226	4760.4	13045	0.34114	0.6335	0.3665	0.733	0.86924	True
-ADI1_p3517660	ADI1	980.4/834.7	943.81/769.01	907.55	856.41	-0.083578	12946	22683	0.33954	0.3671	0.6329	0.7342	0.86924	False
-A1CF_p52596023	A1CF	286.62/761.88	493.99/442.87	524.25	468.43	-0.16211	57120	27252	0.33943	0.36714	0.63286	0.73429	0.86924	False
-ADARB1_p46595724	ADARB1	343.95/129.64	305.98/227.16	236.8	266.57	0.17018	13035	7737.4	0.33846	0.63118	0.36882	0.73764	0.87104	True
-ADRB2_m148206406	ADRB2	829.01/861.33	992.39/795.33	845.17	893.86	0.080714	9969.2	20955	0.33635	0.6317	0.3683	0.7366	0.87085	True
-AES_p3061179	AES	268.99/310.79	112.93/397.67	289.89	255.3	-0.18265	20706	11150	0.33263	0.36971	0.63029	0.73942	0.87211	False
-ABTB1_p127395834	ABTB1	502.7/861.33	413.86/825.09	682.02	619.47	-0.13855	74431	35814	0.33073	0.37042	0.62958	0.74085	0.87233	False
-ADPRHL2_p36554520	ADPRHL2	1102.4/1054.9	1372.8/910.34	1078.7	1141.6	0.081719	54035	36338	0.33007	0.62933	0.37067	0.74135	0.87233	True
-ACTN1_p69445679	ACTN1	257.23/131.42	240.37/191.11	194.32	215.74	0.15009	4563.5	4244.2	0.32872	0.62828	0.37172	0.74343	0.87279	True
-ADARB1_p46595700	ADARB1	739.35/914.61	992.39/755.85	826.98	874.12	0.079888	21667	20858	0.32641	0.62794	0.37206	0.74411	0.87279	True
-ADAM12_m128076635	ADAM12	355.71/708.6	520.48/635.69	532.16	578.09	0.11923	34452	19833	0.32616	0.62782	0.37218	0.74437	0.87279	True
-ADCK1_m78285376	ADCK1	1459.6/1179.2	1296.5/1222.2	1319.4	1259.3	-0.067181	21030	34400	0.32391	0.373	0.627	0.746	0.87369	False
-AAK1_p69870063	AAK1	485.06/296.58	229.64/651.71	390.82	440.68	0.17281	53417	23728	0.32368	0.62481	0.37519	0.75038	0.87676	True
-ACAD9_p128598551	ACAD9	1159.7/1335.5	1120.5/1258.8	1247.6	1189.6	-0.06861	12509	32323	0.32255	0.37352	0.62648	0.74703	0.87387	False
-AES_p3061195	AES	539.44/355.19	834.67/193.97	447.32	514.32	0.20095	1.1111e+05	43919	0.31971	0.61916	0.38084	0.76168	0.88069	True
-ADCK2_p140373122	ADCK2	579.13/932.37	760.85/653.43	755.75	707.14	-0.095779	34080	23695	0.31578	0.37609	0.62391	0.75217	0.87782	False
-AFF1_p87967869	AFF1	718.77/1246.7	1205.6/897.18	982.74	1051.4	0.09734	93468	47678	0.31446	0.62341	0.37659	0.75317	0.87797	True
-ACLY_p40070105	ACLY	761.39/475.95	559.6/596.79	618.67	578.19	-0.097466	20715	16778	0.31253	0.37732	0.62268	0.75464	0.8784	False
-ADRM1_p60878671	ADRM1	420.38/134.97	307.87/312.41	277.68	310.14	0.15898	20370	10840	0.31182	0.62096	0.37904	0.75808	0.87899	True
-ACP1_p272185	ACP1	1625.7/797.4	951.38/1287.4	1211.5	1119.4	-0.11402	1.9974e+05	87436	0.31165	0.37765	0.62235	0.75531	0.8784	False
-ABHD14B_p52004124	ABHD14B	858.4/969.67	791.14/1137.5	914.04	964.32	0.077173	33086	26271	0.31021	0.6218	0.3782	0.7564	0.87866	True
-ADCK5_m145603095	ADCK5	906.91/1017.6	848.55/1175.3	962.26	1011.9	0.072493	29749	26056	0.30752	0.62078	0.37922	0.75845	0.87899	True
-AHNAK2_m105444555	AHNAK2	504.17/277.05	162.14/738.11	390.61	450.13	0.20413	95833	37863	0.30588	0.61147	0.38853	0.77707	0.88952	True
-ACTL6A_p179291144	ACTL6A	185.2/449.31	146.37/412.54	317.26	279.45	-0.18244	35151	16414	0.30587	0.37985	0.62015	0.7597	0.87942	False
-ACTL7A_p111624686	ACTL7A	341.01/118.99	105.99/296.96	230	201.48	-0.19014	21441	10431	0.29891	0.38251	0.61749	0.76501	0.88352	False
-ACVR2A_m148653961	ACVR2A	862.81/431.56	651.08/545.86	647.18	598.47	-0.11272	49264	26802	0.29763	0.38299	0.61701	0.76599	0.88363	False
-AEBP2_p19615492	AEBP2	730.53/387.16	403.77/822.22	558.84	613	0.13321	73252	33234	0.29707	0.61638	0.38362	0.76725	0.88386	True
-AES_p3056318	AES	1078.9/658.88	797.44/839.39	868.88	818.42	-0.08622	44542	29254	0.29504	0.38398	0.61602	0.76796	0.88386	False
-ADAP1_m994106	ADAP1	440.96/660.65	675.05/498.94	550.81	587	0.091646	19820	15282	0.29275	0.61514	0.38486	0.76972	0.88486	True
-ADRA1B_m159344006	ADRA1B	501.23/221.99	217.66/434.86	361.61	326.26	-0.14799	31287	15861	0.28397	0.38822	0.61178	0.77644	0.88952	False
-AFMID_m76187079	AFMID	395.4/470.63	546.35/251.76	433.01	399.06	-0.11753	23111	14342	0.28378	0.38829	0.61171	0.77658	0.88952	False
-ADNP2_p77891053	ADNP2	354.24/321.45	338.16/288.38	337.84	313.27	-0.10862	888.29	7555.2	0.2828	0.38866	0.61134	0.77733	0.88952	False
-ADRA1A_p26722465	ADRA1A	555.61/497.27	957.69/225.44	526.44	591.57	0.16797	1.349e+05	53216	0.28232	0.60673	0.39327	0.78654	0.89485	True
-ADAR_m154574165	ADAR	673.2/518.58	545.72/579.05	595.89	562.38	-0.083345	6254.9	14204	0.28113	0.3893	0.6107	0.77861	0.88997	False
-ACTN1_m69445718	ACTN1	1086.2/632.24	1101.5/731.25	859.24	916.39	0.092804	85806	42831	0.27617	0.60878	0.39122	0.78243	0.8914	True
-ACAT2_p160183121	ACAT2	1668.3/1926.9	2248.5/1506.6	1797.6	1877.5	0.062721	1.5434e+05	83802	0.27607	0.60875	0.39125	0.78249	0.8914	True
-ACVRL1_m52306307	ACVRL1	818.72/976.77	1041.6/836.53	897.74	939.06	0.064848	16759	22410	0.27601	0.60873	0.39127	0.78254	0.8914	True
-AGAP2_m58129192	AGAP2	795.2/726.36	1025.2/378.78	760.78	701.99	-0.11587	1.0565e+05	47642	0.26974	0.39368	0.60632	0.78736	0.89485	False
-ACTR3_p114674498	ACTR3	1317/1179.2	1340.6/1059.1	1248.1	1199.9	-0.056823	24561	32337	0.26827	0.39424	0.60576	0.78849	0.89511	False
-AHNAK_m62303507	AHNAK	116.12/236.2	292.73/19.454	176.16	156.09	-0.17343	22275	9866.6	0.26114	0.39699	0.60301	0.79398	0.89922	False
-ADH7_m100349694	ADH7	811.37/1031.8	1026.5/896.04	921.6	961.25	0.060706	16403	23074	0.26102	0.60296	0.39704	0.79407	0.89922	True
-ADAP1_p975103	ADAP1	909.85/710.38	528.05/1215.3	810.11	871.68	0.10555	1.2803e+05	56003	0.26017	0.60251	0.39749	0.79498	0.89922	True
-ADK_p75960554	ADK	363.06/825.82	736.88/349.6	594.44	543.24	-0.1297	91032	39787	0.25891	0.39785	0.60215	0.7957	0.89922	False
-AHCY_m32883330	AHCY	2009.3/2235.9	2437.1/1938	2122.6	2187.6	0.043454	75125	63974	0.25674	0.60131	0.39869	0.79738	0.9001	True
-AGFG1_m228337178	AGFG1	549.73/1021.2	786.72/874.87	785.45	830.79	0.080867	57506	32045	0.25329	0.59998	0.40002	0.80005	0.90139	True
-ADAR_p154574043	ADAR	765.8/1046	1073.8/821.08	905.92	947.43	0.064565	35596	26957	0.25282	0.5998	0.4002	0.80041	0.90139	True
-ADH5_p100003224	ADH5	555.61/362.29	341.94/643.13	458.95	492.54	0.10167	32022	17756	0.25204	0.59938	0.40062	0.80124	0.90139	True
-ACTL6B_m100253971	ACTL6B	483.59/458.19	227.75/640.84	470.89	434.3	-0.11645	42822	21561	0.25025	0.4012	0.5988	0.80239	0.90168	False
-ADAMTS5_p28338612	ADAMTS5	114.65/294.81	175.39/274.07	204.73	224.73	0.13386	10549	6401.9	0.24999	0.59658	0.40342	0.80683	0.90361	True
-ADARB1_m46595675	ADARB1	568.84/470.63	704.7/398.81	519.73	551.76	0.086102	25804	16734	0.24756	0.59775	0.40225	0.8045	0.90303	True
-AEBP1_m44144302	AEBP1	590.89/848.9	495.88/861.13	719.9	678.51	-0.085304	49995	28351	0.24583	0.40291	0.59709	0.80582	0.90349	False
-AGTPBP1_m88296222	AGTPBP1	1672.7/903.96	1204.4/1508.8	1288.3	1356.6	0.074437	1.7092e+05	79307	0.24242	0.59577	0.40423	0.80845	0.90386	True
-AEN_p89169534	AEN	1030.4/1065.6	1486.4/452.02	1048	969.2	-0.11263	2.6778e+05	1.0701e+05	0.24185	0.40445	0.59555	0.80889	0.90386	False
-AGPAT5_m6566254	AGPAT5	764.33/475.95	369.7/785.6	620.14	577.65	-0.10223	64035	31244	0.24073	0.40488	0.59512	0.80976	0.90386	False
-ADAD1_p123301373	ADAD1	923.08/836.47	918.58/770.16	879.78	844.37	-0.059198	7382.3	21912	0.23921	0.40547	0.59453	0.81094	0.90416	False
-ADH5_m100009841	ADH5	1943.2/2260.8	2097.1/1992.9	2102	2045	-0.039631	27932	57767	0.23708	0.4063	0.5937	0.81259	0.90499	False
-ADRB3_m37823842	ADRB3	142.58/0	0/138.47	71.289	69.234	-0.041603	9875.4	4185	0.23486	0.40716	0.59284	0.81431	0.90538	False
-AFF2_p147891422	AFF2	122/626.91	374.12/303.26	374.45	338.69	-0.14443	64989	27310	0.23431	0.40737	0.59263	0.81475	0.90538	False
-ADAP1_m994101	ADAP1	1148/797.4	909.11/1116.3	972.68	1012.7	0.058131	41459	30154	0.23055	0.59117	0.40883	0.81767	0.90609	True
-ADH7_p100350724	ADH7	342.48/385.38	449.19/320.42	363.93	384.81	0.08026	4605.7	8206.9	0.23045	0.59112	0.40888	0.81776	0.90609	True
-ACVR1C_p158443786	ACVR1C	1409.6/918.16	1267.5/1152.9	1163.9	1210.2	0.056251	63657	41164	0.22828	0.59029	0.40971	0.81943	0.90609	True
-ABI1_p27149695	ABI1	232.24/76.366	152.68/185.39	154.3	169.03	0.13072	6341.7	4221	0.2267	0.58604	0.41396	0.82792	0.9069	True
-ADCK2_m140373231	ADCK2	1314.1/1149	1178.5/1203.9	1231.6	1191.2	-0.048039	6969.3	31860	0.22615	0.41054	0.58946	0.82109	0.90609	False
-ACAD11_p132378525	ACAD11	818.72/1150.8	718.58/1158.7	984.77	938.63	-0.06916	75990	41891	0.22544	0.41082	0.58918	0.82164	0.90609	False
-ADARB1_m46595697	ADARB1	712.89/566.53	600.61/623.11	639.71	611.86	-0.064117	5481.9	15371	0.22465	0.41113	0.58887	0.82226	0.90609	False
-ADAMTS5_p28338607	ADAMTS5	1114.2/941.25	1267.5/690.05	1027.7	978.75	-0.070341	90824	47641	0.22428	0.41127	0.58873	0.82254	0.90609	False
-ADNP_m49518587	ADNP	3017.6/2642.6	1834.6/3573.3	2830.1	2703.9	-0.065775	7.9088e+05	3.1726e+05	0.22401	0.41137	0.58863	0.82275	0.90609	False
-ACAD9_p128598525	ACAD9	574.72/531.01	271.28/929.22	552.86	600.25	0.11844	1.087e+05	44945	0.22353	0.58655	0.41345	0.82689	0.9069	True
-ABL2_m179100544	ABL2	649.68/268.17	547.61/301.54	458.93	424.58	-0.11198	51526	24257	0.22298	0.41178	0.58822	0.82355	0.90609	False
-ADH7_p100349757	ADH7	605.59/575.41	513.54/719.23	590.5	616.39	0.061807	10805	14061	0.21835	0.58642	0.41358	0.82716	0.9069	True
-AFF3_p100623814	AFF3	949.54/1074.4	610.7/1307.4	1012	959.07	-0.077415	1.2526e+05	58824	0.21824	0.41362	0.58638	0.82725	0.9069	False
-ACTR5_p37377163	ACTR5	532.09/822.26	991.13/451.45	677.18	721.29	0.090912	93862	42205	0.21472	0.5848	0.4152	0.83039	0.90861	True
-ABCB8_p150730705	ABCB8	1092.1/927.04	883.25/1204.4	1009.6	1043.8	0.048104	32604	27893	0.20516	0.58128	0.41872	0.83745	0.91533	True
-ADRB1_m115803996	ADRB1	645.27/515.02	705.97/400.53	580.15	553.25	-0.068381	27564	18379	0.19845	0.42134	0.57866	0.84269	0.92005	False
-AHCTF1_m247067252	AHCTF1	196.96/301.91	423.96/34.331	249.44	229.14	-0.12191	40706	17163	0.19677	0.422	0.578	0.84401	0.92049	False
-ACTR3_p114684953	ACTR3	85.253/635.79	181.07/624.25	360.52	402.66	0.15905	1.2488e+05	47040	0.19428	0.55559	0.44441	0.88882	0.94234	True
-ADRM1_p60878687	ADRM1	202.84/319.67	429.01/51.496	261.26	240.25	-0.12044	39040	16798	0.19427	0.42298	0.57702	0.84596	0.92161	False
-ABLIM2_p8108331	ABLIM2	1300.8/1157.9	1311.6/1078.6	1229.4	1195.1	-0.040773	18685	31797	0.19227	0.42376	0.57624	0.84753	0.92205	False
-ACTL7A_m111624726	ACTL7A	405.68/303.69	417.02/257.48	354.69	337.25	-0.072514	8963.8	8304.8	0.1914	0.42411	0.57589	0.84821	0.92205	False
-AFF1_m87967382	AFF1	371.88/348.09	144.47/526.41	359.98	335.44	-0.10157	36610	17608	0.18984	0.42472	0.57528	0.84944	0.92238	False
-ACTR8_m53916111	ACTR8	651.15/1081.6	576/1072.3	866.35	824.14	-0.071988	1.0788e+05	50320	0.18828	0.42533	0.57467	0.85066	0.9227	False
-ACD_p67694362	ACD	1590.4/1097.5	1333.1/1433.3	1344	1383.2	0.041472	63241	44489	0.18596	0.57376	0.42624	0.85247	0.92307	True
-ADRA1B_m159344028	ADRA1B	1074.5/527.46	930.56/746.12	800.97	838.34	0.06572	83312	40930	0.18475	0.57327	0.42673	0.85346	0.92307	True
-ADNP2_p77875445	ADNP2	655.56/619.81	562.12/667.74	637.68	614.93	-0.052338	3108.2	15316	0.18386	0.42706	0.57294	0.85412	0.92307	False
-AGPAT5_m6566223	AGPAT5	1149.4/2033.5	1704/1361.2	1591.4	1532.6	-0.054301	2.2475e+05	1.0317e+05	0.18313	0.42735	0.57265	0.85469	0.92307	False
-ADCK2_m140373168	ADCK2	689.37/548.77	447.93/740.97	619.07	594.45	-0.05844	26411	18683	0.18009	0.42854	0.57146	0.85708	0.92465	False
-ADCK1_p78285386	ADCK1	809.9/687.29	678.84/865.71	748.6	772.27	0.044868	12489	18308	0.175	0.56946	0.43054	0.86108	0.92796	True
-AEBP1_m44144338	AEBP1	580.6/351.64	693.35/293.53	466.12	493.44	0.082003	53070	24895	0.17315	0.56806	0.43194	0.86389	0.92909	True
-ADD1_p2877761	ADD1	1411.1/648.22	1070/1076.8	1029.6	1073.4	0.060002	1.455e+05	65902	0.1705	0.56768	0.43232	0.86465	0.92909	True
-ADCY1_p45614255	ADCY1	451.25/825.82	820.16/515.54	638.53	667.85	0.064655	58273	29651	0.17023	0.56754	0.43246	0.86492	0.92909	True
-ACRC_p70800708	ACRC	2395.9/2441.9	2463/2461.5	2418.9	2462.3	0.025616	530.42	67543	0.1668	0.56624	0.43376	0.86753	0.93089	True
-ADAD1_p123301349	ADAD1	956.89/285.93	505.97/664.87	621.41	585.42	-0.085913	1.1886e+05	49541	0.16543	0.4343	0.5657	0.86861	0.93105	False
-ADRB2_p148206418	ADRB2	443.9/412.02	461.81/361.62	427.96	411.71	-0.055699	2763.7	9828.1	0.16388	0.43491	0.56509	0.86983	0.93136	False
-AGAP2_p58129153	AGAP2	305.73/204.23	254.25/279.8	254.98	267.02	0.066304	2738.7	5525.1	0.16197	0.5642	0.4358	0.8716	0.93225	True
-ACVR2A_m148602767	ACVR2A	1593.3/1419	1124.2/1805.2	1506.2	1464.7	-0.040206	1.2354e+05	67752	0.15914	0.43678	0.56322	0.87356	0.93336	False
-ADAD1_m123301295	ADAD1	595.3/887.97	957.69/578.48	741.64	768.08	0.050485	57366	31201	0.14973	0.5595	0.4405	0.88099	0.94029	True
-ACO2_m41895824	ACO2	590.89/333.88	292.1/679.75	462.38	485.93	0.071499	54082	25168	0.1484	0.5582	0.4418	0.8836	0.94106	True
-ACTL7A_m111624641	ACTL7A	1931.4/1555.7	1583.5/1838.4	1743.6	1711	-0.027209	51526	48451	0.14808	0.44114	0.55886	0.88228	0.94066	False
-ABL2_p179100489	ABL2	2303.3/1491.8	1720.4/1986.6	1897.5	1853.5	-0.033842	1.8234e+05	95146	0.1427	0.44326	0.55674	0.88653	0.94234	False
-ADCK2_p140373158	ADCK2	837.83/953.68	861.16/972.71	895.76	916.94	0.03368	6466.2	22355	0.14167	0.55633	0.44367	0.88734	0.94234	True
-ABL1_m133729594	ABL1	780.5/502.59	1136.9/78.389	641.55	607.63	-0.078246	2.994e+05	1.1008e+05	0.13952	0.44452	0.55548	0.88904	0.94234	False
-ACVR1_m158637066	ACVR1	709.95/658.88	675.05/658.01	684.41	666.53	-0.038138	724.69	16570	0.13891	0.44476	0.55524	0.88952	0.94234	False
-AHNAK_m62303523	AHNAK	179.32/298.36	150.15/305.54	238.84	227.85	-0.067691	9579.1	6618	0.13747	0.44533	0.55467	0.89066	0.94255	False
-ACTN1_m69445724	ACTN1	1471.3/1317.8	1537.5/1199.9	1394.5	1368.7	-0.027001	34393	36587	0.13528	0.4462	0.5538	0.89239	0.94308	False
-ADK_p76074481	ADK	438.02/74.59	254.88/231.16	256.31	243.02	-0.076482	33161	14758	0.13372	0.44681	0.55319	0.89362	0.94308	False
-AAK1_m69870131	AAK1	1108.3/1355	890.19/1508.8	1231.7	1199.5	-0.038129	1.1091e+05	58211	0.13326	0.44699	0.55301	0.89399	0.94308	False
-AFAP1L2_m116093045	AFAP1L2	708.48/847.13	752.65/767.87	777.8	760.26	-0.032869	4863.8	19105	0.12692	0.4495	0.5505	0.899	0.94737	False
-ACAD11_p132378501	ACAD11	1065.7/742.35	903.43/865.71	904	884.57	-0.03131	26488	23886	0.12571	0.44998	0.55002	0.89996	0.94737	False
-AHRR_p344030	AHRR	736.41/893.3	1114.8/459.46	814.85	787.12	-0.049891	1.1352e+05	51252	0.12272	0.45117	0.54883	0.90233	0.94887	False
-AATK_p79104858	AATK	417.44/614.48	570.96/487.5	515.96	529.23	0.036555	11447	12101	0.1206	0.54799	0.45201	0.90401	0.94964	True
-AAAS_m53715238	AAAS	47.036/239.75	265.6/44.058	143.39	154.83	0.10997	21556	9127.4	0.11971	0.51532	0.48468	0.96936	0.97895	True
-ADRB2_p148206455	ADRB2	4950.5/3555.4	3992.9/4392.1	4253	4192.5	-0.020666	5.2639e+05	2.5987e+05	0.11869	0.45276	0.54724	0.90552	0.95023	False
-AATF_p35307527	AATF	2185.7/1482.9	1741.9/1862.5	1834.3	1802.2	-0.025487	1.2711e+05	75463	0.11699	0.45343	0.54657	0.90687	0.95064	False
-ADCK5_m145603170	ADCK5	858.4/802.73	1068.7/548.15	830.57	808.44	-0.03891	68525	36543	0.11576	0.45392	0.54608	0.90784	0.95066	False
-ACAD11_m132378519	ACAD11	401.27/440.43	453.61/365.62	420.85	409.62	-0.038954	2318.8	9646.8	0.11443	0.45445	0.54555	0.9089	0.95077	False
-ADRM1_m60878679	ADRM1	1584.5/1610.8	1700.9/1448.8	1597.7	1574.8	-0.020753	16063	42565	0.11067	0.45594	0.54406	0.91188	0.9529	False
-ACVR1B_m52369226	ACVR1B	51.445/364.07	324.91/117.3	207.76	221.1	0.0894	35209	14669	0.11019	0.5233	0.4767	0.95339	0.97432	True
-ADCK5_p145603142	ADCK5	1280.3/1479.4	1263/1536.3	1379.8	1399.7	0.020605	28579	36157	0.10446	0.5416	0.4584	0.9168	0.95704	True
-ADRA1A_m26722473	ADRA1A	811.37/580.73	739.4/625.97	696.05	682.68	-0.027935	16515	16884	0.10287	0.45903	0.54097	0.91806	0.95726	False
-ABL1_m133729467	ABL1	984.81/797.4	1328/394.23	891.11	861.13	-0.049315	2.2677e+05	90408	0.10177	0.45947	0.54053	0.91894	0.95726	False
-ADARB2_m1779266	ADARB2	1568.4/2083.2	1810.7/1887.6	1825.8	1849.1	0.01834	67745	55503	0.099199	0.53951	0.46049	0.92098	0.95824	True
-AEN_m89169495	AEN	662.91/594.94	834.04/391.94	628.93	612.99	-0.036969	50016	26727	0.097564	0.46114	0.53886	0.92228	0.95824	False
-AGTPBP1_p88307644	AGTPBP1	302.79/335.65	274.44/347.89	319.22	311.16	-0.036787	1618.7	7093.6	0.095825	0.46183	0.53817	0.92366	0.95824	False
-AGBL5_m27275975	AGBL5	779.03/932.37	778.52/905.19	855.7	841.85	-0.02351	9889.9	21246	0.095004	0.46216	0.53784	0.92431	0.95824	False
-ABHD14B_m52004106	ABHD14B	446.84/394.26	519.22/340.45	420.55	429.84	0.03143	8681.2	9639	0.094568	0.53767	0.46233	0.92467	0.95824	True
-ABTB1_m127395808	ABTB1	1184.7/650	888.29/910.91	917.36	899.6	-0.028163	71609	39174	0.089702	0.46426	0.53574	0.92852	0.96124	False
-ADRB1_m115804041	ADRB1	414.5/1147.3	604.39/1002.5	780.88	803.43	0.041009	1.7385e+05	70742	0.084761	0.533	0.467	0.934	0.96456	True
-ACO2_m41903807	ACO2	474.77/1126	864.95/773.02	800.36	818.98	0.033145	1.0812e+05	49189	0.083972	0.53339	0.46661	0.93322	0.96456	True
-AFTPH_m64778724	AFTPH	49.976/127.87	105.99/66.945	88.922	86.467	-0.039928	1897.9	1774.5	0.081538	0.46751	0.53249	0.93501	0.96456	False
-ADCK3_m227149144	ADCK3	263.11/168.72	189.27/231.73	215.91	210.5	-0.036444	2678.3	4592	0.080811	0.4678	0.5322	0.93559	0.96456	False
-ABCF1_p30545888	ABCF1	264.58/353.41	442.25/191.68	309	316.97	0.036632	17670	10451	0.077983	0.53049	0.46951	0.93902	0.9661	True
-ADD3_p111860411	ADD3	593.83/834.7	523.01/932.66	714.26	727.83	0.027114	56457	30403	0.077825	0.53101	0.46899	0.93799	0.96603	True
-ADARB2_m1779272	ADARB2	1018.6/513.25	679.47/824.51	765.93	751.99	-0.026472	69110	35557	0.07398	0.47051	0.52949	0.94103	0.96717	False
-ADRA1B_m159343921	ADRA1B	67.614/245.08	196.21/110.43	156.35	153.32	-0.028039	9713	5375.9	0.063038	0.47487	0.52513	0.94974	0.97432	False
-ACTR3_p114688931	ACTR3	721.71/768.99	584.83/888.6	745.35	736.72	-0.016779	23627	20022	0.060988	0.47568	0.52432	0.95137	0.97432	False
-AAAS_p53714391	AAAS	415.97/708.6	601.87/537.85	562.29	569.86	0.019263	22433	16355	0.059205	0.5236	0.4764	0.95279	0.97432	True
-ADARB1_m46595715	ADARB1	749.63/378.28	472.54/673.46	563.96	573	0.022906	44569	23763	0.058653	0.52333	0.47667	0.95335	0.97432	True
-ADI1_p3523171	ADI1	636.45/156.28	564.02/250.04	396.37	407.03	0.038194	82286	33445	0.058293	0.51593	0.48407	0.96814	0.97895	True
-AGPAT3_m45379570	AGPAT3	127.88/230.87	191.16/160.78	179.38	175.97	-0.027492	2882.6	3736.7	0.057892	0.47692	0.52308	0.95383	0.97432	False
-AHRR_p344042	AHRR	1230.3/525.68	837.19/945.24	877.98	891.22	0.021562	1.2703e+05	56920	0.055478	0.52207	0.47793	0.95587	0.9754	True
-AEN_m89169500	AEN	679.08/568.3	741.3/492.65	623.69	616.97	-0.015604	18524	16137	0.052901	0.47891	0.52109	0.95781	0.976	False
-ADCK4_m41220523	ADCK4	565.9/776.09	878.2/445.73	670.99	661.96	-0.01952	57802	30074	0.052147	0.47921	0.52079	0.95841	0.976	False
-AEBP2_p19615544	AEBP2	745.22/751.23	736.25/773.59	748.23	754.92	0.012832	357.59	18298	0.049483	0.51973	0.48027	0.96053	0.97716	True
-AEBP1_p44144359	AEBP1	770.21/880.87	577.9/1056.8	825.54	817.36	-0.014356	60403	33744	0.044558	0.48223	0.51777	0.96446	0.97895	False
-AHCTF1_p247079429	AHCTF1	601.18/900.41	735.62/754.13	750.79	744.88	-0.011396	22470	19735	0.042105	0.48321	0.51679	0.96641	0.97895	False
-ADNP2_p77875499	ADNP2	745.22/614.48	453.61/920.64	679.85	687.12	0.015331	58803	30566	0.041603	0.51657	0.48343	0.96686	0.97895	True
-ACTR5_m37377171	ACTR5	438.02/443.99	482/391.94	441	436.97	-0.013222	2036.4	10162	0.040007	0.48404	0.51596	0.96809	0.97895	False
-ABTB1_m127395860	ABTB1	945.13/1102.9	885.77/1174.7	1024	1030.2	0.0087472	27089	26326	0.038419	0.51532	0.48468	0.96935	0.97895	True
-ACTL6B_m100253168	ACTL6B	751.1/939.48	471.91/1238.2	845.29	855.05	0.016548	1.5567e+05	65863	0.038041	0.51493	0.48507	0.97013	0.97895	True
-AEBP2_m19615572	AEBP2	1023/468.85	729.31/774.73	745.94	752.02	0.011697	77295	37922	0.031226	0.51242	0.48758	0.97515	0.98302	True
-AFF1_p87967890	AFF1	824.6/1007	526.79/1316.6	915.78	921.69	0.0092723	1.6426e+05	70028	0.022338	0.50878	0.49122	0.98244	0.98938	True
-ACHE_p100491656	ACHE	2626.7/1706.7	1749.5/2570.2	2166.7	2159.8	-0.0045493	3.8001e+05	1.665e+05	0.016725	0.49333	0.50667	0.98666	0.9925	False
-ACHE_m100491816	ACHE	295.44/420.9	380.43/338.73	358.17	359.58	0.005641	4369.4	8062.6	0.015671	0.50624	0.49376	0.98753	0.9925	True
-ACHE_m100491779	ACHE	280.75/525.68	298.41/505.24	403.21	401.82	-0.0049676	25692	14696	0.012018	0.49521	0.50479	0.99041	0.99439	False
-AGAP2_m58128444	AGAP2	2459.1/1609	1964.6/2109.6	2034.1	2037.1	0.0021679	1.8592e+05	99102	0.0097214	0.50388	0.49612	0.99224	0.99523	True
-ADRB2_p148206450	ADRB2	608.53/877.32	955.17/533.27	742.92	744.22	0.0025145	62561	32956	0.0071485	0.50284	0.49716	0.99432	0.99631	True
-ACVRL1_m52306881	ACVRL1	1665.4/1102.9	1593/1178.1	1384.1	1385.6	0.0015047	1.2213e+05	64899	0.0056737	0.50226	0.49774	0.99547	0.99647	True
-ACAD11_m132378564	ACAD11	989.22/1545.1	1045.4/1487.1	1267.1	1266.2	-0.0010322	1.2602e+05	63931	0.0035873	0.49857	0.50143	0.99714	0.99714	False
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/output.count_normalized.txt
--- a/test-data/output.count_normalized.txt	Thu Apr 19 05:35:12 2018 -0400
+++ b/test-data/output.count_normalized.txt	Mon Jun 04 10:58:16 2018 -0400
@@ -1,4 +1,4 @@
-sgRNA	Gene	test1_fastq_gz
+sgRNA	Gene	test1.fastq.gz
 s_47512	RNF111	2.0
 s_24835	HCFC1R1	2.0
 s_14784	CYP4B1	8.0
diff -r afa81ef5f1c3 -r 38073c236d71 test-data/output_summary.Rnw
--- a/test-data/output_summary.Rnw	Thu Apr 19 05:35:12 2018 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,1063 +0,0 @@
-% This is a template file for Sweave used in MAGeCK
-% Author: Wei Li, Shirley Liu lab
-% Do not modify lines beginning with "#__".
-\documentclass{article}
-
-\usepackage{amsmath}
-\usepackage{amscd}
-\usepackage[tableposition=top]{caption}
-\usepackage{ifthen}
-\usepackage{fullpage}
-\usepackage[utf8]{inputenc}
-
-\begin{document}
-\setkeys{Gin}{width=0.9\textwidth}
-
-\title{MAGeCK Comparison Report}
-\author{Wei Li}
-
-\maketitle
-
-
-\tableofcontents
-
-\section{Summary}
-
-%Function definition
-<<label=funcdef,include=FALSE,echo=FALSE>>=
-genreporttable<-function(comparisons,ngenes,direction,fdr1,fdr5,fdr25){
-  xtb=data.frame(Comparison=comparisons,Genes=ngenes,Selection=direction,FDR1=fdr1,FDR5=fdr5,FDR25=fdr25);
-  colnames(xtb)=c("Comparison","Genes","Selection","FDR1%","FDR5%","FDR25%");
-  return (xtb);
-}
-@
-
-The statistics of comparisons is as indicated in the following table. 
-
-<<label=tab1,echo=FALSE,results=tex>>=
-library(xtable)
-comparisons=c("HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.","HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.");
-ngenes=c(100,100);
-direction=c("negative","positive");
-fdr1=c(0,0);
-fdr5=c(2,0);
-fdr25=c(9,1);
-
-cptable=genreporttable(comparisons,ngenes,direction,fdr1,fdr5,fdr25);
-print(xtable(cptable, caption = "Summary of comparisons", label = "tab:one",
-    digits = c(0, 0, 0, 0, 0, 0, 0),
-    table.placement = "tbp",
-    caption.placement = "top"))
-@
-
-The meanings of the columns are as follows.
-
-\begin{itemize}
-\item \textbf{Comparison}: The label for comparisons;
-\item \textbf{Genes}: The number of genes in the library;
-\item \textbf{Selection}: The direction of selection, either positive selection or negative selection;
-\item \textbf{FDR1\%}: The number of genes with FDR $<$ 1\%;
-\item \textbf{FDR5\%}: The number of genes with FDR $<$ 5\%;
-\item \textbf{FDR25\%}: The number of genes with FDR $<$ 25\%;
-\end{itemize}
-
-The following figures show:
-
-\begin{itemize}
-\item Individual sgRNA read counts of selected genes in selected samples; 
-\item The distribution of RRA scores and p values of all genes; and
-\item The RRA scores and p values of selected genes.
-\end{itemize}
-
-
-\newpage\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg.}
-
-The following figure shows the distribution of RRA score in the comparison HL60 final,KBM7 final vs HL60 initial,KBM7 initial neg., and the RRA scores of 10 genes.
-
-<<echo=FALSE>>=
-gstable=read.table('output.gene_summary.txt',header=T)
-@
-%
-
-
-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=# 
-#
-# parameters
-# Do not modify the variables beginning with "__"
-
-# gstablename='__GENE_SUMMARY_FILE__'
-startindex=3
-# outputfile='__OUTPUT_FILE__'
-targetgenelist=c("ACIN1","ACTR8","AHCY","ACLY","AATF","AGBL5","AHCTF1","ABT1","ADIRF","ABCF1")
-# samplelabel=sub('.\w+.\w+$','',colnames(gstable)[startindex]);
-samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial neg.'
-
-
-# You need to write some codes in front of this code:
-# gstable=read.table(gstablename,header=T)
-# pdf(file=outputfile,width=6,height=6)
-
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-######
-# function definition
-
-plotrankedvalues<-function(val, tglist, ...){
-  
-  plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...)
-  if(length(tglist)>0){
-    for(i in 1:length(tglist)){
-      targetgene=tglist[i];
-      tx=which(names(val)==targetgene);ty=val[targetgene];
-      points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
-      # text(tx+50,ty,targetgene,col=colors[i])
-    }
-    legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors)
-  }
-}
-
-
-
-plotrandvalues<-function(val,targetgenelist, ...){
-  # choose the one with the best distance distribution
-  
-  mindiffvalue=0;
-  randval=val;
-  for(i in 1:20){
-    randval0=sample(val)
-    vindex=sort(which(names(randval0) %in% targetgenelist))
-    if(max(vindex)>0.9*length(val)){
-      # print('pass...')
-      next;
-    }
-    mindiffind=min(diff(vindex));
-    if (mindiffind > mindiffvalue){
-      mindiffvalue=mindiffind;
-      randval=randval0;
-      # print(paste('Diff: ',mindiffvalue))
-    }
-  }
-  plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...)
-  
-  if(length(targetgenelist)>0){
-    for(i in 1:length(targetgenelist)){
-      targetgene=targetgenelist[i];
-      tx=which(names(randval)==targetgene);ty=randval[targetgene];
-      points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
-      text(tx+50,ty,targetgene,col=colors[i])
-    }
-  }
-  
-}
-
-
-
-
-# set.seed(1235)
-
-
-
-pvec=gstable[,startindex]
-names(pvec)=gstable[,'id']
-pvec=sort(pvec);
-
-plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel))
-
-# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel))
-
-
-pvec=gstable[,startindex+1]
-names(pvec)=gstable[,'id']
-pvec=sort(pvec);
-
-plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel))
-
-# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel))
-
-
-
-# you need to write after this code:
-# dev.off()
-
-
-
-
-@
-%%
-\clearpage
-\newpage
-The following figures show the distribution of sgRNA read counts (normalized) of selected genes in selected samples.
-%
-
-
-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
-par(mfrow=c(2,2));
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(561.4907165816957,824.0396348113272,428.37415340969943,579.047491896501),c(3424.79939695118,3818.2871009576584,1992.3498917052,690.0506672205338),c(846.6456878299913,985.6508562937211,335.0024675413113,415.97581680707134),c(2432.636481525409,2122.257249136931,1067.465489792653,155.6333179800872),c(1308.1851773762019,2186.1913587343615,1482.5909580453515,997.3120339679854),c(405.68439208520414,268.16807081144486,170.34023773287015,109.85881269182627),c(640.8637498157573,559.4234589775174,711.6436598617687,632.2603542941043),c(946.5969148654764,470.6260845366416,663.0651476194316,457.74505288260946),c(246.9383256170808,177.59474888175154,28.39003962214503,0.0),c(568.8400715107754,612.7018836420428,564.0154538266146,270.64176251684285))
-targetgene="ACIN1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(2484.0819660289676,2349.578527705573,2172.7843657481662,910.9126552363929),c(992.1629154257711,1005.1862786707138,743.8190381001997,200.26346063614164),c(1267.0287897733551,1156.1418152202027,251.09412821363824,42.34141739164138),c(1500.738276518092,1315.977089213779,800.5991173444897,1476.2277955464156),c(1925.5309914189038,2054.7712445618654,194.94493873872918,235.16652091844063),c(351.29916561001374,781.4168950797068,227.75120674654121,624.2498158686586),c(1719.74905340467,1006.9622261595313,356.45271970026533,222.0063506480656),c(903.9706562768137,1445.6212558974576,1482.5909580453515,1055.1023468944147),c(651.152846716469,1081.552020689867,576.0023594448536,1072.2677863775127),c(285.1549712482957,408.46792242802854,99.0496937928171,44.630142656054424))
-targetgene="ACTR8"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(301.3235520922712,657.1005708624807,228.38209651592223,137.32351586478285),c(1142.0897559789987,1099.311495578042,112.92926871919911,100.70391163417409),c(789.3207193831689,671.3081507730209,723.6305654800077,588.7745742702564),c(392.45555321286054,412.0198174056636,334.37157777193033,213.99581222261992),c(2009.3136376104133,2235.917888421252,2437.1271791188055,1937.9781176417478),c(1071.5359486598327,406.69197493921104,645.4002340767636,349.602784139093),c(61.7345814042702,218.44154112455442,614.4866353770946,452.5954210376801),c(651.152846716469,879.0940069646701,237.21455328725622,18.88198343140764),c(1625.6773103124485,1410.1023061211074,2146.286995434164,1986.613529510525),c(1053.8974968300413,882.6459019423052,106.6203710253891,105.85354347910344))
-targetgene="AHCY"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(1268.498660759171,1411.8782536099247,1136.2324746551822,603.6512884889412),c(327.78122983695846,454.642557137284,51.73296108924205,24.031615276336996),c(132.28838872343613,241.5288584791821,123.02350502929512,65.80085135187511),c(495.34652221997754,586.0626713097802,279.4841678357833,243.74924065998954),c(1009.8013672555626,1102.8633905556771,1237.174837756142,1004.7503910773278),c(877.5129785321263,715.7068379934587,538.1489732819936,594.496387431289),c(1594.8100196103135,1108.1912330221296,605.6541786057605,127.59643349102738),c(314.5523909646148,252.1845434120872,88.95545748272109,359.9020478289517),c(512.984974049769,269.94401830026237,205.67006481820619,126.45207085882086),c(761.3931706526657,475.9539270030942,559.5992254409475,596.7851126957021))
-targetgene="ACLY"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-par(mfrow=c(1,1));
-@
-%
-
-
-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
-par(mfrow=c(2,2));
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(659.9720726313648,809.832054900787,880.7221180558769,802.1982051767731),c(724.6463960072668,1086.8798631563195,695.2405258578626,307.26136674745163),c(836.3565909292796,1289.3378768815162,468.75109865008346,177.94838930811443),c(367.46774645398926,571.85509139924,300.30353022535627,116.72498848506541),c(518.8644579930328,632.2373060190355,627.7353205340956,308.9779106957614),c(405.68439208520414,259.28833336735727,324.27734146183434,166.5047629860492),c(2096.0360257735547,1960.6460276545372,1573.4390848362154,629.9716290296913),c(277.8056163192159,435.1071347602913,182.32714335110919,0.0),c(995.1026573974029,477.7298744919117,728.0467938656747,275.21921304566894),c(2185.6981559083283,1482.9161531626255,1741.8866532609427,1862.4501839161173))
-targetgene="AATF"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(640.8637498157573,602.0461987091378,307.2433176885473,192.82510352679924),c(354.23890758164566,280.5997032331675,204.4082852794442,275.79139436177223),c(779.0316224824572,932.3724316291956,778.5179754161547,905.1908420753603),c(624.6951689717818,554.0956165110648,370.96318439602834,558.4489645167836),c(1133.270530064103,1394.1187787217498,639.0913363829536,1131.2024619361487),c(423.32284391499564,412.0198174056636,224.59675789963623,426.84726181303336),c(296.91393913482335,829.3674772777797,489.5704610396565,1233.0507362025292),c(684.959879390236,546.9918265557948,394.30610586312537,566.4595029422292),c(440.96129574478715,630.461358530218,434.6830511035094,457.1728715665062),c(1108.2827233052317,1969.5257650986248,1066.2037102538911,1333.7546478367033))
-targetgene="AGBL5"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(196.96271209933826,301.9110730989776,423.9579250240324,34.33087896619571),c(1106.8128523194157,1056.6887558464218,1743.1484327997048,807.3478370217025),c(748.1643317803222,488.3855594248168,239.73811236478022,477.77139894622366),c(1095.053884432888,882.6459019423052,837.8216137379688,365.05167967388104),c(677.6105244611563,316.11865300951774,613.8557456077136,819.3636446598709),c(1078.8853035889126,1609.008424868669,348.88204246769334,193.96946615900578),c(1437.533824128006,1095.759600600407,320.4920028455483,161.35513114111984),c(845.1758168441753,660.6524658401157,541.3034221288985,640.8430740356532),c(551.2016196809839,740.570102836904,1103.42620664737,622.5332719203489),c(601.1772331987264,900.4053768304803,735.6174710982467,754.1349746240991))
-targetgene="AHCTF1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(487.9971672908978,367.6211301852257,312.2904358435953,441.15179471561487),c(358.6485205390935,394.2603425174884,593.0363832181406,268.35303725242983),c(1743.266989177725,1980.1814500315297,837.1907239685878,281.5132075228048),c(1597.7497615819454,1465.1566782744503,1065.57282048451,992.7345834391593),c(119.05954985109253,378.2768151181308,185.48159219801417,128.7407961232339),c(986.2834314825072,745.8979453033566,328.0626800781203,302.11173490252224),c(523.2740709504807,694.3954681276485,336.89513684945433,597.9294753279087),c(1562.4728579223624,763.6574201915316,422.0652557158894,220.28980669975581),c(30.8672907021351,179.37069637056908,238.47633282601822,184.81456510135357),c(339.5401977234861,447.5387671820139,310.3977665354523,205.98527379717427))
-targetgene="ABT1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-par(mfrow=c(1,1));
-@
-%
-
-
-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
-par(mfrow=c(2,2));
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(492.4067802483456,221.99343610218943,309.7668767660713,102.99263689858714),c(243.9985836454489,239.7529109903646,130.59418226186713,174.51530141149487),c(734.9354929079785,673.0840982618383,620.7955330709046,470.9052231529845),c(1074.4756906314647,950.1319065173708,1100.902647569846,743.8357109342404),c(702.5983312200275,1010.5141211371663,1291.4313579229083,1017.3383800315995),c(1647.7253750996879,760.1055252138966,685.7771793171477,608.2287390177673),c(951.0065278229242,864.8864270541301,606.9159581445226,769.0116888427839),c(435.0818118015233,435.1071347602913,275.69882921949727,339.8757017653375),c(89.66213013477338,209.56180368046682,208.8245136651112,304.4004601669353),c(1328.7633711776252,1571.7135276035012,1122.983789498181,1356.6419004808338))
-targetgene="ADIRF"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(216.0710349149457,289.479440677255,192.42137966120518,498.36992632594104),c(1127.391046120839,1198.764554951823,371.5940741654094,370.2013115188104),c(1111.2224652768637,1038.9292809582466,948.227323379644,922.3562815584581),c(1164.137820766238,1204.0923974182756,1686.9992433247955,2089.033985093009),c(48.505742531926586,248.63264843445216,665.5887066969557,248.8988725049189),c(501.2260061632414,387.1565525622184,436.5757204116524,314.69972385679404),c(1975.5066049366465,1797.2588586833258,1628.3264947723626,1289.6966864967521),c(213.13129294331378,376.5008676293133,404.4003421732214,482.921030791153),c(2012.2533795820452,1989.0611874756173,1064.3110409457481,431.9968936579627),c(264.57677744687226,353.4135502746856,442.25372833608145,191.6807408945927))
-targetgene="ABCF1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-par(mfrow=c(1,1));
-@
-
-\newpage\section{Comparison results of HL60 final,KBM7 final vs HL60 initial,KBM7 initial pos.}
-
-The following figure shows the distribution of RRA score in the comparison HL60 final,KBM7 final vs HL60 initial,KBM7 initial pos., and the RRA scores of 10 genes.
-
-<<echo=FALSE>>=
-gstable=read.table('output.gene_summary.txt',header=T)
-@
-%
-
-
-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=# 
-#
-# parameters
-# Do not modify the variables beginning with "__"
-
-# gstablename='__GENE_SUMMARY_FILE__'
-startindex=9
-# outputfile='__OUTPUT_FILE__'
-targetgenelist=c("ACRC","AGAP3","ADCK4","AHRR","ADRBK1","ADK","ADCK1","ADARB2","ACSS2","ADNP")
-# samplelabel=sub('.\w+.\w+$','',colnames(gstable)[startindex]);
-samplelabel='HL60_final,KBM7_final_vs_HL60_initial,KBM7_initial pos.'
-
-
-# You need to write some codes in front of this code:
-# gstable=read.table(gstablename,header=T)
-# pdf(file=outputfile,width=6,height=6)
-
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-######
-# function definition
-
-plotrankedvalues<-function(val, tglist, ...){
-  
-  plot(val,log='y',ylim=c(max(val),min(val)),type='l',lwd=2, ...)
-  if(length(tglist)>0){
-    for(i in 1:length(tglist)){
-      targetgene=tglist[i];
-      tx=which(names(val)==targetgene);ty=val[targetgene];
-      points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
-      # text(tx+50,ty,targetgene,col=colors[i])
-    }
-    legend('topright',tglist,pch=20,pt.cex = 2,cex=1,col=colors)
-  }
-}
-
-
-
-plotrandvalues<-function(val,targetgenelist, ...){
-  # choose the one with the best distance distribution
-  
-  mindiffvalue=0;
-  randval=val;
-  for(i in 1:20){
-    randval0=sample(val)
-    vindex=sort(which(names(randval0) %in% targetgenelist))
-    if(max(vindex)>0.9*length(val)){
-      # print('pass...')
-      next;
-    }
-    mindiffind=min(diff(vindex));
-    if (mindiffind > mindiffvalue){
-      mindiffvalue=mindiffind;
-      randval=randval0;
-      # print(paste('Diff: ',mindiffvalue))
-    }
-  }
-  plot(randval,log='y',ylim=c(max(randval),min(randval)),pch=20,col='grey', ...)
-  
-  if(length(targetgenelist)>0){
-    for(i in 1:length(targetgenelist)){
-      targetgene=targetgenelist[i];
-      tx=which(names(randval)==targetgene);ty=randval[targetgene];
-      points(tx,ty,col=colors[(i %% length(colors)) ],cex=2,pch=20)
-      text(tx+50,ty,targetgene,col=colors[i])
-    }
-  }
-  
-}
-
-
-
-
-# set.seed(1235)
-
-
-
-pvec=gstable[,startindex]
-names(pvec)=gstable[,'id']
-pvec=sort(pvec);
-
-plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel))
-
-# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='RRA score',main=paste('Distribution of RRA scores in \n',samplelabel))
-
-
-pvec=gstable[,startindex+1]
-names(pvec)=gstable[,'id']
-pvec=sort(pvec);
-
-plotrankedvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel))
-
-# plotrandvalues(pvec,targetgenelist,xlab='Genes',ylab='p value',main=paste('Distribution of p values in \n',samplelabel))
-
-
-
-# you need to write after this code:
-# dev.off()
-
-
-
-
-@
-%%
-\clearpage
-\newpage
-The following figures show the distribution of sgRNA read counts (normalized) of selected genes in selected samples.
-%
-
-
-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
-par(mfrow=c(2,2));
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(461.5394895462105,502.5931393353569,445.40817718298644,889.1697652244688),c(76.43329126242978,90.5733219296933,447.30084649112945,357.0411412484354),c(258.6972935036084,685.515730683561,533.7327448963265,560.7376897811967),c(232.23961575892122,681.9638357059259,275.69882921949727,467.47213525636494),c(1393.4376945535273,1472.2604682297203,1039.706339939889,532.7008052921368),c(2395.88970688001,2441.927797124084,2462.9936596634266,2461.5240218762324),c(495.34652221997754,605.5980936867728,1159.575396122279,1617.5565806239213),c(682.0201374186041,822.2636873225097,1572.1773052974536,1333.7546478367033),c(961.2956247236359,1097.5355480892247,959.5833392285019,905.1908420753603),c(1940.2297012770634,1289.3378768815162,942.5493154552149,1103.737758763192))
-targetgene="ACRC"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(1387.5582106102636,1120.6228654438523,1214.4628060584262,1111.1761158725344),c(388.0459402554127,509.69692929062694,933.0859689144999,750.1297054113762),c(326.3113588511425,635.7892009966705,960.8451187672639,615.6670961271097),c(1328.7633711776252,1038.9292809582466,1346.3187678590552,1596.3858719281006),c(352.7690365958297,234.42506852391205,310.3977665354523,429.1359870774464),c(693.7791053051318,678.4119407282909,784.1959833405838,895.4637597016048),c(837.8264619150956,719.2587329710938,374.74852301231437,993.8789460713658),c(365.99787546817333,369.3970776740432,333.74068800254935,746.6966175147567),c(707.0079441774753,635.7892009966705,837.1907239685878,1465.3563505404536),c(486.5272963050818,673.0840982618383,784.8268731099647,734.6808098765882))
-targetgene="AGAP3"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(830.4771069860158,864.8864270541301,1349.4732167059603,740.974804353724),c(1481.6299537024847,1994.38902994207,2044.082852794442,1810.9538654668238),c(1234.6916280854039,1299.9935618144214,1357.6747837079133,2232.6514954349277),c(224.89026082984142,188.25043381465665,700.2876440129107,81.24974688666317),c(812.8386551562243,845.3510046771374,946.334654071501,999.6007592323984),c(1978.4463469082782,1751.0842239740703,2659.2003779409174,2851.1794981425537),c(565.9003295391435,776.0890526132542,878.1985589783528,445.72924524444096),c(680.5502664327881,534.5601941340722,550.7667686696135,1025.9210997731484),c(161.68580843975528,333.87812789769293,275.0679394501163,465.18340999195186),c(2523.768482645998,2445.4796921017187,2153.226782897355,1516.8526689897471))
-targetgene="ADCK4"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(345.4196816667499,163.38716897121142,474.42910657451245,481.2044868428432),c(415.9734889859159,372.9489726516783,212.6098522813972,349.03060282298975),c(1.469870985815957,83.46953197442323,0.0,62.9399447713588),c(351.29916561001374,150.9555365494888,288.9475143764983,416.54799812317464),c(561.4907165816957,170.49095892648148,199.3611671243962,411.97054759434855),c(251.34793857452865,221.99343610218943,1564.6066280648815,1502.5481360871656),c(736.4053638937945,893.3015868752103,1114.782222496228,459.46159683091923),c(338.07032673767014,607.3740411755903,378.5338616286004,65.22867003577186),c(1230.2820151279561,525.6804566899846,837.1907239685878,945.2435342025885))
-targetgene="AHRR"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-par(mfrow=c(1,1));
-@
-%
-
-
-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
-par(mfrow=c(2,2));
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(371.87735941143717,877.3180594758527,2395.4884543396593,1564.9158995424211),c(1109.7525942910477,1138.3823403320275,970.308465307979,999.0285779162951),c(1462.5216308868773,1209.420239884728,1537.4783679814984,1519.14139425416),c(586.4785233405669,987.4268037825386,743.8190381001997,1312.0117578247794),c(1018.6205931704583,717.4827854822763,1070.619938639558,1144.3626322065236),c(1269.9685317449869,1212.9721348623632,1591.1039983788835,1624.9949377332637),c(1321.4140162485455,1795.4829111945082,1478.8056194290655,1237.056005415252),c(908.3802692342615,832.9193722554148,1639.6825106212207,1268.5259778009315),c(923.078979092421,758.3295777250792,1479.4365091984464,1275.964334910274),c(680.5502664327881,634.013253507853,318.5993335374053,631.1159916618979))
-targetgene="ADRBK1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(1472.810727787589,1829.225913482041,1263.0413183007631,1315.444845721399),c(208.7216799858659,65.71005708624807,292.1019632234033,350.17496545519623),c(1011.2712382413785,1166.7975001531076,652.9709113093356,860.5606994193058),c(557.0811036242477,685.515730683561,875.0441101314478,1019.6271052960126),c(363.0581334965414,825.8155823001447,736.8792506370087,349.602784139093),c(1505.14788947554,451.09066215964896,653.6018010787167,991.0180394908496),c(198.43258308515422,28.41515982108025,249.83234867487624,114.43626322065236),c(438.02155377315523,74.58979453033565,254.87946682992424,231.16125170571777),c(804.0194292413286,472.4020320254591,1336.2245315489592,1203.2973077651598),c(454.19013461713075,490.1615069136343,896.4943622904019,685.4732166917076))
-targetgene="ADK"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(662.9118146029966,1008.7381736483488,1101.533537339227,1694.8010582978616),c(1547.7741480642028,1965.9738701209897,1869.9572764452857,2353.9539344488194),c(1459.5818889152454,1179.2291325748304,1296.4784760779562,1222.1792911965672),c(1193.5352404825571,1355.0479339677643,1622.0175970785526,1905.9359639399652),c(868.6937526172306,701.4992580829187,720.4761166331027,603.6512884889412),c(798.1399452980647,768.9852626579842,1478.8056194290655,1756.0244591209105),c(1168.5474337236858,907.5091667857504,879.4603385171149,977.8578692204745),c(809.8989131845924,687.2916781723785,678.8373918539567,865.7103312642352),c(1246.4505959719315,753.0017352586266,1301.5255942330043,1264.5207085882087),c(826.0674940285679,797.4004224790644,977.8791425405509,2066.7189137649816))
-targetgene="ADCK1"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(1863.7964100146337,1585.9211075140413,1761.4442361117538,1464.211987908247),c(742.2848478370584,598.4943037315028,943.8110949939769,820.5080072920774),c(1568.3523418656262,2083.1864043829455,1810.6536381234716,1887.6261618246608),c(1018.6205931704583,513.248824268262,679.4682816233377,824.5132765048003),c(1140.6198849931827,1191.6607649965529,880.0912282864958,977.8578692204745),c(135.22813069506805,118.98848175077354,351.40560154521734,399.95473995618005),c(665.8515565746286,701.4992580829187,986.7115993118849,746.6966175147567),c(418.9132309575478,300.1351256101601,376.6411923204574,645.4205245644794),c(561.4907165816957,543.4399315781598,881.9838975946388,580.7640358448108),c(442.4311667306031,229.0972260574595,395.5678854018874,651.142337725512))
-targetgene="ADARB2"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-par(mfrow=c(1,1));
-@
-%
-
-
-<<fig=TRUE,echo=FALSE,width=4.5,height=4.5>>=
-par(mfrow=c(2,2));
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(734.9354929079785,358.74139274113816,541.9343118982795,378.7840312603593),c(595.2977492554626,591.3905137762326,1061.787481868224,887.4532212761591),c(1655.0747300287676,943.0281165621008,1069.358159100796,2038.1098479598186),c(626.1650399575977,884.4218494311227,517.3296108924205,858.2719741548927),c(680.5502664327881,747.673892792174,533.1018551269456,1016.194017399393),c(662.9118146029966,777.8650001020718,864.9498738213518,787.3214909580882),c(880.4527205037583,621.5816210861304,671.8976043907657,1040.7978139918332),c(94.07174309222125,447.5387671820139,711.6436598617687,927.5059134033875),c(399.80490814194036,806.280159923152,1147.58849050404,1059.1076161071376),c(698.1887182625796,531.0082991564371,504.0809257354195,347.8862401907832))
-targetgene="ACSS2"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-# parameters
-# Do not modify the variables beginning with "__"
-targetmat=list(c(408.62413405683606,523.9045092011671,483.89245311522745,701.494293542599),c(1805.0015705819953,1434.9655709645526,1712.2348341000356,2152.546111180471),c(3017.64513388016,2642.609863360463,1834.6274493599499,3573.2723190648703),c(1649.1952460855039,783.1928425685244,773.4708572611067,1332.0381038883936),c(959.82575373782,1397.6706736993847,1429.5962174173474,2811.126806015325),c(495.34652221997754,301.9110730989776,336.89513684945433,555.015876620164),c(1491.9190506031964,1331.9606166131366,2087.614246881731,1983.1804416139055),c(429.2023278582595,889.7496918975753,567.8007924429005,1132.9190058844583),c(427.7324568724435,573.6310388880576,655.4944703868597,899.4690289143276),c(690.8393633334998,767.2093151691668,1040.33722970927,993.3067647552625))
-targetgene="ADNP"
-collabel=c("HL60_initial","KBM7_initial","HL60_final","KBM7_final")
-
-# set up color using RColorBrewer
-#library(RColorBrewer)
-#colors <- brewer.pal(length(targetgenelist), "Set1")
-
-colors=c( "#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00",  "#A65628", "#F781BF",
-          "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", 
-          "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69", "#FCCDE5",
-          "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
-
-
-## code
-
-targetmatvec=unlist(targetmat)+1
-yrange=range(targetmatvec[targetmatvec>0]);
-# yrange[1]=1; # set the minimum value to 1
-for(i in 1:length(targetmat)){
-  vali=targetmat[[i]]+1;
-  if(i==1){
-    plot(1:length(vali),vali,type='b',las=1,pch=20,main=paste('sgRNAs in',targetgene),ylab='Read counts',xlab='Samples',xlim=c(0.7,length(vali)+0.3),ylim = yrange,col=colors[(i %% length(colors))],xaxt='n',log='y')
-    axis(1,at=1:length(vali),labels=(collabel),las=2)
-    # lines(0:100,rep(1,101),col='black');
-  }else{
-    lines(1:length(vali),vali,type='b',pch=20,col=colors[(i %% length(colors))])
-  }
-}
-
-
-
-par(mfrow=c(1,1));
-@
-%__INDIVIDUAL_PAGE__
-
-
-
-
-
-
-
-
-
-\end{document}
-