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+# computeGC.py
+
+Takes .genomecov files produced by BEDtools genomeCov (with -d option).
+
+```
+bedtools genomecov 
+	-d                          \
+	-ibam normal.bam            \
+        -g reference.fa             
+```
+
+```
+python computeGC.py                 \
+        -r reference.fa             \
+        -i genomecovfile            \
+        -w [sliding window length]  \
+        -o /path/to/model.p
+```
+
+# computeFraglen.py
+
+Takes SAM file via stdin:
+
+./samtools view toy.bam | python computeFraglen.py
+
+and creates fraglen.p model in working directory.
+
+
+# genMutModel.py
+
+Takes references genome and TSV file to generate mutation models:
+
+```
+python genMutModel.py               \
+        -r hg19.fa                  \
+        -m inputVariants.tsv        \
+        -o /home/me/models.p
+```
+
+Trinucleotides are identified in the reference genome and the variant file. Frequencies of each trinucleotide transition are calculated and output as a pickle (.p) file.
+
+# genSeqErrorModel.py
+
+Generates sequence error model for genReads.py -e option.
+
+```
+python genSeqErrorModel.py                            \
+        -i input_read1.fq (.gz) / input_read1.sam     \
+        -o output.p                                   \
+        -i2 input_read2.fq (.gz) / input_read2.sam    \
+        -p input_alignment.pileup                     \
+        -q quality score offset [33]                  \
+        -Q maximum quality score [41]                 \
+        -n maximum number of reads to process [all]   \
+        -s number of simulation iterations [1000000]  \
+        --plot perform some optional plotting
+```
+
+# plotMutModel.py
+
+Performs plotting and comparison of mutation models generated from genMutModel.py.
+
+```
+python plotMutModel.py                                        \
+        -i model1.p [model2.p] [model3.p]...                  \
+        -l legend_label1 [legend_label2] [legend_label3]...   \
+        -o path/to/pdf_plot_prefix
+```
+
+# vcf_compare_OLD.py
+
+Tool for comparing VCF files.
+
+```
+python vcf_compare_OLD.py
+        --version          show program's version number and exit      \
+        -h, --help         show this help message and exit             \
+        -r <ref.fa>        * Reference Fasta                           \
+        -g <golden.vcf>    * Golden VCF                                \
+        -w <workflow.vcf>  * Workflow VCF                              \
+        -o <prefix>        * Output Prefix                             \
+        -m <track.bed>     Mappability Track                           \
+        -M <int>           Maptrack Min Len                            \
+        -t <regions.bed>   Targetted Regions                           \
+        -T <int>           Min Region Len                              \
+        -c <int>           Coverage Filter Threshold [15]              \
+        -a <float>         Allele Freq Filter Threshold [0.3]          \
+        --vcf-out          Output Match/FN/FP variants [False]         \
+        --no-plot          No plotting [False]                         \
+        --incl-homs        Include homozygous ref calls [False]        \
+        --incl-fail        Include calls that failed filters [False]   \
+        --fast             No equivalent variant detection [False]     
+```
+Mappability track examples: https://github.com/zstephens/neat-repeat/tree/master/example_mappabilityTracks
+
+## Controlled Data and Germline-Reference Allele Mismatch Information
+ICGC's "Access Controlled Data" documention can be found at http://docs.icgc.org/access-controlled-data. To have access to controlled germline data, a DACO must be
+submitted. Open tier data can be obtained without a DACO, but germline alleles that do not match the reference genome are masked and replaced with the reference
+allele. Controlled data includes unmasked germline alleles.
+