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Changeset 0:9845dc9c7323 (2018-12-18)

Next changeset 1:6f389729a30b (2019-01-31)

Commit message:
planemo upload commit ad5f1c5a1a71d7fa2bc8bac408856aa80b0fc2a3

added:
README.rst
kegg_maps_visualization.R
kegg_maps_visualization.xml
kegg_pathways_list_index.loc.sample
test-data/Lacombe_et_al_2017_OK.txt
test-data/hsa00010.pathview.png
test-data/hsa04514.pathview.png
test-data/hsa05167.pathview.png
tool-data/hsa_pathways.loc
tool-data/hsa_pathways.loc.sample
tool-data/mmu_pathways.loc
tool-data/mmu_pathways.loc.sample
tool-data/rno_pathways.loc
tool-data/rno_pathways.loc.sample
tool_data_table_conf.xml.sample

diff -r 000000000000 -r 9845dc9c7323 README.rst
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/README.rst Tue Dec 18 10:02:54 2018 -0500

@@ -0,0 +1,94 @@
+Wrapper for Pathview tool
+=============================
+
+**Authors**
+
+David Christiany, Florence Combes, Yves Vandenbrouck CEA, INSERM, CNRS, Grenoble-Alpes University, BIG Institute, FR
+
+Sandra Dérozier, Olivier Rué, Christophe Caron, Valentin Loux INRA, Paris-Saclay University, MAIAGE Unit, Migale Bioinformatics platform
+
+This work has been partially funded through the French National Agency for Research (ANR) IFB project.
+
+Contact support@proteore.org for any questions or concerns about the Galaxy implementation of this tool.
+
+=============================
+
+This tool map a list of Uniprot Accession number or Entrez gene ID to KEGG pathway with pathview R package.
+
+You can map Entrez gene IDs / Uniprot accession number from three species : human, mouse and rat.
+
+If your input have another type of IDs, please use the ID_Converter tool.
+
+**Input:**
+
+
+- KEGG Pathways IDs to be used for mapping can be set by:
+    - chosing from the KEGG pathways name list
+    - giving a list (copy/paste)
+    - importing a list from a dataset (column) - output of KEGG pathways identification and coverage can be used (1st column)
+- Genes/proteins ids to map can be either a list of Entrez genes IDs / Uniprot accession number or a file (tabular, tsv, txt) containing at least one column of Entrez genes IDs / Uniprot accession number.
+- fold change values (up to three columns) from a dataset (same dataset as for Genes/proteins ids to map)
+
+You can see below an example of an input file with identifiers (uniprot_AC) and fold_change values.
+
+.. csv-table:: Simulated data
+   :header: "Uniprot_AC","Protein.name","Number_of_peptides","fc_values 1","fc_values 2","fc_values 3"
+
+   "P15924","Desmoplakin","69","0.172302292051025","-0.757435966487116","0.0411240398990759"
+   "P02538","Keratin, type II cytoskeletal 6A","53","-0.988842456122076","0.654626325100182","-0.219153396366064"
+   "P02768","Serum albumin","44","-0.983493243315454","0.113752002761474","-0.645886132600729"
+   "P08779","Keratin, type I cytoskeletal 16","29","0.552302597284443","-0.329045605110646","2.10616106806788"
+
+|
+
+
+
+.. class:: warningmark
+
+If there's more than one id per line in the defined column, it will be split to have only one id per line. For example this table:
+
+.. csv-table:: Simulated data
+   :header: "Ensembl_Gene","UniProt.AC","UniProt.ID"
+
+    "ENSG00000136881","Q14032","BAAT_HUMAN"
+    "ENSG00000170099","P08185;G3V350;G3V4V7","CBG_HUMAN;G3V350_HUMAN;G3V4V7_HUMAN"
+    "ENSG00000244731","P0C0L4","CO4A_HUMAN"
+
+|
+
+will become:
+
+.. csv-table:: Simulated data
+   :header: "Ensembl_Gene","UniProt.AC","UniProt.ID"
+
+    "ENSG00000136881","Q14032","BAAT_HUMAN"
+    "ENSG00000170099","P08185","CBG_HUMAN;G3V350_HUMAN;G3V4V7_HUMAN"
+    "ENSG00000170099","G3V350","CBG_HUMAN;G3V350_HUMAN;G3V4V7_HUMAN"
+    "ENSG00000170099","G3V4V7","CBG_HUMAN;G3V350_HUMAN;G3V4V7_HUMAN"
+    "ENSG00000244731","P0C0L4","CO4A_HUMAN"
+
+|
+
+**Output:**
+
+- a **collection dataset** named 'KEGG maps visualization from <dataset>', one file (png or pdf) for each given pathway.
+- a **summary text file** (.tsv) of the mapping(s) with the following columns
+    - **KEGG pathway ID**: KEGG pathway(s) used to map given genes/proteins ids
+    - **pathway name**: name(s) of KEGG pathway(s) used for mapping
+    - **nb of Uniprot_AC used** (only when Uniprot accession number is given): number of Uniprot accession number which will be converted to Entrez genes IDs
+    - **nb of Entrez gene ID used**: number of Entrez gene IDs used for mapping
+    - **nb of Entrez gene ID mapped**: number of Entrez gene IDs mapped on a given pathway
+    - **nb of Entrez gene ID in the pathway**: number total of Entrez gene IDs in a given pathway
+    - **ratio of Entrez gene ID mapped**: number of Entrez gene IDs mapped / number total of Entrez gene IDs
+    - **Entrez gene ID mapped**: list of mapped Entrez gene IDs
+    - **uniprot_AC mapped** (only when Uniprot accession number is given): list of Uniprot accession number corresponding to the mapped Entrez gene IDs
+
+-----
+
+.. class:: infomark
+
+**Database:**
+
+KEGG Pathways names list are from  http://rest.kegg.jp/list/pathway/
+
+User manual / Documentation: http://www.bioconductor.org/packages/release/bioc/html/pathview.html

diff -r 000000000000 -r 9845dc9c7323 kegg_maps_visualization.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/kegg_maps_visualization.R Tue Dec 18 10:02:54 2018 -0500

[

b'@@ -0,0 +1,354 @@\n+#!/usr/bin/Rscript\n+#Rscript made for mapping genesID on KEGG pathway with Pathview package\n+#input : csv file containing ids (uniprot or geneID) to map, plus parameters\n+#output : KEGG pathway : jpeg or pdf file.\n+\n+options(warn=-1) #TURN OFF WARNINGS !!!!!!\n+suppressMessages(library("pathview"))\n+suppressMessages(library(KEGGREST))\n+\n+read_file <- function(path,header){\n+ file <- try(read.csv(path,header=header, sep="\\t",stringsAsFactors = FALSE, quote="\\"", check.names = F, comment.char = "#"),silent=TRUE)\n+ if (inherits(file,"try-error")){\n+ stop("File not found !")\n+ }else{\n+ return(file)\n+ }\n+}\n+\n+##### fuction to clean and concatenate pathway name (allow more flexibility for user input) \n+concat_string <- function(x){\n+ x <- gsub(" - .*","",x)\n+ x <- gsub(" ","",x)\n+ x <- gsub("-","",x)\n+ x <- gsub("_","",x)\n+ x <- gsub(",","",x)\n+ x <- gsub("\\\\\'","",x)\n+ x <- gsub("\\\\(.*)","",x)\n+ x <- gsub("\\\\/","",x)\n+ x <- tolower(x)\n+ return(x)\n+}\n+\n+#return output suffix (pathway name) from id kegg (ex : hsa:00010)\n+get_suffix <- function(pathways_list,species,id){\n+ suffix = gsub("/","or",pathways_list[pathways_list[,1]==paste(species,id,sep=""),2])\n+ suffix = gsub(" ","_",suffix)\n+ if (nchar(suffix) > 50){\n+ suffix = substr(suffix,1,50)\n+ }\n+ return(suffix)\n+}\n+\n+str2bool <- function(x){\n+ if (any(is.element(c("t","true"),tolower(x)))){\n+ return (TRUE)\n+ }else if (any(is.element(c("f","false"),tolower(x)))){\n+ return (FALSE)\n+ }else{\n+ return(NULL)\n+ }\n+}\n+\n+is.letter <- function(x) grepl("[[:alpha:]]", x)\n+\n+#### hsa00010 -> 00010\n+remove_kegg_prefix <- function(x){\n+ x = gsub(":","",x)\n+ if (substr(x,1,4) == \'path\'){\n+ x=substr(x,5,nchar(x))\n+ }\n+ if (is.letter(substr(x,1,3))){\n+ x <- substr(x,4,nchar(x))\n+ }\n+ return(x)\n+}\n+\n+kegg_to_geneID <- function(vector){\n+ vector <- sapply(vector, function(x) unlist(strsplit(x,":"))[2],USE.NAMES = F)\n+ return (vector)\n+}\n+\n+clean_bad_character <- function(string) {\n+ string <- gsub("X","",string)\n+ return(string)\n+}\n+\n+get_list_from_cp <-function(list){\n+ list = strsplit(list, "[ \\t\\n]+")[[1]]\n+ list = list[list != ""] #remove empty entry\n+ list = gsub("-.+", "", list) #Remove isoform accession number (e.g. "-2")\n+ return(list)\n+}\n+\n+get_ref_pathways <- function(species){\n+ ##all available pathways for the species\n+ pathways <-keggLink("pathway", species)\n+ tot_path<-unique(pathways)\n+ \n+ ##formating the dat into a list object\n+ ##key= pathway ID, value = genes of the pathway in the kegg format\n+ pathways_list <- sapply(tot_path, function(pathway) names(which(pathways==pathway)))\n+ return (pathways_list)\n+}\n+\n+mapping_summary <- function(pv.out,species,id,id_type,pathways_list,geneID,uniprotID,mapped2geneID){\n+ ref_pathways = get_ref_pathways(species)\n+ names(ref_pathways) <- sapply(names(ref_pathways), function(x) gsub("path:[a-z]{3}","",x),USE.NAMES = F)\n+ \n+ #genes present in pathway\n+ genes = ref_pathways[id][[1]]\n+ nb_genes = length(genes)\n+ \n+ #genes mapped on pathway genes\n+ mapped <- unlist(sapply(pv.out$plot.data.gene$all.mapped, function(x) strsplit(x,",")),use.names = F)\n+ mapped = unique(mapped[mapped!=""])\n+ nb_mapped <- length(mapped)\n+ \n+ #compue ratio of mapping\n+ ratio = round((nb_mapped/nb_genes)*100, 2)\n+ if (is.nan(ratio)) { ratio = ""}\n+ pathway_id = paste(species,id,sep="")\n+ pathway_name = as.character(pathways_list[pathways_list[,1]==pathway_id,][2])\n+ \n+ if (id_type=="geneid" || id_type=="keggid") {\n+ row <- c(pathway_id,pathway_name,length(unique(geneID)),nb_mapped,nb_genes,ratio,paste(mapped,collapse=";"))\n+ names(row) <- c("KEGG pathway ID","pathway name","nb of Entrez gene ID used","nb of Entrez gene ID mapped",\n+ "nb of Entrez gene ID in the pathway", "ratio of Entrez gene ID mapped (%)","Entrez gene ID mapped")\n+ } else if (id_type=="uniprotid") {\n+ row <- c(pathway_id,pathway_name,length(unique(uniprot'..b')\n+ ncol <- as.numeric(gsub("c", "" ,args$id_column))\n+ header <- str2bool(args$header)\n+ native_kegg <- str2bool(args$native_kegg)\n+ species=args$species\n+ fold_change_data = str2bool(args$fold_change_data)\n+ \n+ #org list used in mapped2geneID\n+ org <- c(\'Hs\',\'Mm\',\'Rn\')\n+ names(org) <- c(\'hsa\',\'mmu\',\'rno\')\n+ \n+ #read input file or list\n+ if (!is.null(args$input)){\n+ tab <- read_file(args$input,header)\n+ tab <- data.frame(tab[which(tab[ncol]!=""),],stringsAsFactors = F)\n+ tab = one_id_one_line(tab,ncol)\n+ } else {\n+ id_list = gsub("[[:blank:]]|\\u00A0|NA","",id_list)\n+ id_list = unique(id_list[id_list!=""])\n+ tab <- data.frame(id_list,stringsAsFactors = F)\n+ ncol=1\n+ }\n+ \n+ \n+ ##### map uniprotID to entrez geneID and kegg to geneID\n+ uniprotID=""\n+ mapped2geneID=""\n+ if (id_type == "uniprotid") {\n+ uniprotID=tab[,ncol]\n+ mapped2geneID = id2eg(ids = uniprotID, category = "uniprot", org = org[[species]], pkg.name = NULL)\n+ geneID = mapped2geneID[,2]\n+ tab = cbind(tab,geneID)\n+ ncol=ncol(tab)\n+ }else if (id_type == "keggid"){\n+ keggID = tab[,ncol] \n+ geneID = kegg_to_geneID(keggID)\n+ tab = cbind(tab,geneID)\n+ ncol=ncol(tab)\n+ }else if (id_type == "geneid"){\n+ colnames(tab)[ncol] <- "geneID"\n+ }\n+ \n+ ##### build matrix to map on KEGG pathway (kgml : KEGG xml)\n+ geneID_indices = which(!is.na(tab$geneID))\n+ if (fold_change_data) {\n+ fold_change <- as.integer(unlist(strsplit(gsub("c","",args$fold_change_col),",")))\n+ if (length(fold_change) > 3) { fold_change= fold_change[1:3] }\n+ if (length(fold_change)==1){\n+ tab[,fold_change] <- as.double(gsub(",",".",as.character(tab[,fold_change]) ))\n+ } else {\n+ tab[,fold_change] <- apply(tab[,fold_change],2,function(x) as.double(gsub(",",".",as.character(x))))\n+ }\n+ mat = tab[geneID_indices,c(ncol,fold_change)]\n+ mat = mat[(!duplicated(mat$geneID)),]\n+ geneID=mat$geneID\n+ mat = as.data.frame(mat[,-1])\n+ row.names(mat)=geneID\n+ limit = get_limit(mat)\n+ } else {\n+ mat = unique(as.character(tab$geneID[!is.na(tab$geneID[tab$geneID!=""])]))\n+ geneID=mat\n+ limit=1\n+ }\n+ \n+ #####mapping geneID (with or without expression values) on KEGG pathway\n+ plot.col.key= TRUE\n+ low_color = "green"\n+ mid_color = "#F3F781" #yellow\n+ high_color = "red"\n+ if (!fold_change_data) {\n+ plot.col.key= FALSE #if there\'s no exrepession data, we don\'t show the color key\n+ high_color = "#81BEF7" #blue\n+ } \n+ \n+ #create graph(s) and text output\n+ for (id in ids) {\n+ suffix= get_suffix(pathways_list,species,id)\n+ pv.out <- suppressMessages(pathview(gene.data = mat,\n+ gene.idtype = "entrez", \n+ pathway.id = id,\n+ species = species, \n+ kegg.dir = ".", \n+ out.suffix=suffix,\n+ kegg.native = native_kegg,\n+ low = list(gene = low_color, cpd = "blue"), \n+ mid = list(gene = mid_color, cpd = "transparent"), \n+ high = list(gene = high_color, cpd = "yellow"), \n+ na.col="#D8D8D8", #gray\n+ cpd.data=NULL,\n+ plot.col.key = plot.col.key,\n+ pdf.size=c(9,9),\n+ limit=list(gene=limit, cpd=limit)))\n+ \n+ if (is.list(pv.out)){\n+ \n+ #creating text file\n+ if (!exists("DF")) { \n+ DF <- data.frame(t(mapping_summary(pv.out,species,id,id_type,pathways_list,geneID,uniprotID,mapped2geneID)),stringsAsFactors = F,check.names = F)\n+ } else {\n+ #print (mapping_summary(pv.out,species,id))\n+ DF <- rbind(DF,data.frame(t(mapping_summary(pv.out,species,id,id_type,pathways_list,geneID,uniprotID,mapped2geneID)),stringsAsFactors = F,check.names = F))\n+ }\n+ }\n+ }\n+ \n+ DF <- as.data.frame(apply(DF, c(1,2), function(x) gsub("^$|^ $", NA, x))) #convert "" et " " to NA\n+ \n+ #text file output\n+ write.table(DF,file=args$output,quote=FALSE, sep=\'\\t\',row.names = FALSE, col.names = TRUE)\n+}\n+\n+main()\n'

diff -r 000000000000 -r 9845dc9c7323 kegg_maps_visualization.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/kegg_maps_visualization.xml Tue Dec 18 10:02:54 2018 -0500

[

b'@@ -0,0 +1,347 @@\n+<tool id="kegg_maps_visualization" name="KEGG maps visualization" version="2018.12.18">\n+ <description>of (differentially expressed) genes/proteins</description>\n+ <requirements>\n+ <requirement type="package" version="1.18.0">bioconductor-pathview</requirement>\n+ <requirement type="package" version="1.18.0">bioconductor-keggrest</requirement>\n+ </requirements>\n+ <command detect_errors="exit_code"><![CDATA[\n+ Rscript $__tool_directory__/kegg_maps_visualization.R \n+ #if $input.ids == "text"\n+ --id_list="$input.txt"\n+ #else\n+ --input="$input.file"\n+ --id_column="$input.ncol"\n+ --header="$input.header" \n+ #end if\n+ #if $species.pathways.pathways_id != "pathways_file"\n+ --pathways_id="$species.pathways.pids" \n+ #else \n+ --pathways_input="$species.pathways.file"\n+ --header2="$species.pathways.header2"\n+ --pathway_col="$species.pathways.ncol2"\n+ #end if\n+ --id_type="$id_type" \n+ --native_kegg="$native"\n+ \n+\n+ #if $input.ids=="file" and $input.foldchange.fc=="true"\n+ --fold_change_data="$input.foldchange.fc"\n+ --fold_change_col="$input.foldchange.fc_col"\n+ #else \n+ --fold_change_data="false"\n+ #end if\n+\n+ --species=${species.ref_file} \n+ --pathways_list=$__tool_directory__/${ filter( lambda x: str( x[0] ) == str( $species.ref_file ), $__app__.tool_data_tables[\'kegg_pathways_list_index\'].get_fields() )[0][-1] } \n+ --output="$text_output"\n+\n+ ]]></command>\n+ <inputs>\n+  \n+ <conditional name="species">\n+ <param name="ref_file" type="select" label="Species" >\n+ <option value="hsa">Human (Homo sapiens)</option>\n+ <option value="mmu">Mouse (Mus musculus)</option>\n+ <option value="rno">Rat (Rattus norvegicus)</option>\n+ </param>\n+ <when value="hsa">\n+ <conditional name="pathways">\n+ <param name="pathways_id" type="select" label="Enter your pathway(s)" help="Enter KEGG pathway name(s) or KEGG pathway id(s)">\n+ <option value="pathways_names">KEGG pathway name(s)</option>\n+ <option value="pathways_ids">KEGG pathway IDs</option>\n+ <option value="pathways_file">KEGG pathway IDs from file</option>\n+ </param> \n+ <when value="pathways_names">\n+ <param name="pids" type="select" label="Select pathway(s) (by clicking in the box)" multiple="true" help=\'You can select one or several pathway(s), you can write the beginning of your pathways to search using autocomplete\'>\n+ <options from_data_table="hsa_pathways">\n+ <filter type="sort_by" column="1"/>\n+ <validator type="no_options" message="No indexes are available for the selected input dataset"/>\n+ </options>\n+ </param>\n+ </when>\n+ <when value="pathways_ids">\n+ <param name="pids" type="text" label="Copy/paste your pathway IDs" help=\'IDs must be separated by tab, space or carriage return into the form field, for example: "hsa00010 hsa05412"\'>\n+ <sanitizer invalid_char=\'\'>\n+ <valid initial="string.printable">\n+ <remove value="'"/>\n+ </valid>\n+ <mapping>\n+ <add source=" " target=""/> \n+ '..b'an input file with identifiers (uniprot_AC) and fold_change values.\n+\n+.. csv-table:: Simulated data\n+ :header: "Uniprot_AC","Protein.name","Number_of_peptides","fc_values 1","fc_values 2","fc_values 3"\n+\n+ "P15924","Desmoplakin","69","0.172302292051025","-0.757435966487116","0.0411240398990759"\n+ "P02538","Keratin, type II cytoskeletal 6A","53","-0.988842456122076","0.654626325100182","-0.219153396366064"\n+ "P02768","Serum albumin","44","-0.983493243315454","0.113752002761474","-0.645886132600729"\n+ "P08779","Keratin, type I cytoskeletal 16","29","0.552302597284443","-0.329045605110646","2.10616106806788"\n+\n+|\n+\n+.. class:: warningmark \n+\n+If there\'s more than one ID per line in the specified column, it will be splitted resulting in one unique ID per line. For example this table:\n+\n+.. csv-table:: Simulated data\n+ :header: "Ensembl_Gene","UniProt.AC","UniProt.ID"\n+\n+ "ENSG00000136881","Q14032","BAAT_HUMAN"\n+ "ENSG00000170099","P08185;G3V350;G3V4V7","CBG_HUMAN;G3V350_HUMAN;G3V4V7_HUMAN"\n+ "ENSG00000244731","P0C0L4","CO4A_HUMAN"\n+\n+|\n+\n+will be converted as follow:\n+\n+.. csv-table:: Simulated data\n+ :header: "Ensembl_Gene","UniProt.AC","UniProt.ID"\n+\n+ "ENSG00000136881","Q14032","BAAT_HUMAN"\n+ "ENSG00000170099","P08185","CBG_HUMAN;G3V350_HUMAN;G3V4V7_HUMAN"\n+ "ENSG00000170099","G3V350","CBG_HUMAN;G3V350_HUMAN;G3V4V7_HUMAN"\n+ "ENSG00000170099","G3V4V7","CBG_HUMAN;G3V350_HUMAN;G3V4V7_HUMAN"\n+ "ENSG00000244731","P0C0L4","CO4A_HUMAN"\n+\n+|\n+\n+-----\n+\n+**Output:**\n+\n+- a **collection dataset** named \'KEGG maps visualization from <dataset>\', one file (png or pdf) for each given pathway.\n+\n+- a **summary text file** (.tsv) of the mapping(s) with the following columns\n+ - **KEGG pathway ID**: KEGG pathway(s) used to map given genes/proteins ids\n+ - **pathway name**: name(s) of KEGG pathway(s) used for mapping\n+ - **nb of Uniprot_AC used** (only when Uniprot accession number is given): number of Uniprot accession number which will be converted to Entrez genes IDs\n+ - **nb of Entrez gene ID used**: number of Entrez gene IDs used for mapping\n+ - **nb of Entrez gene ID mapped**: number of Entrez gene IDs mapped on a given pathway\n+ - **nb of Entrez gene ID in the pathway**: number total of Entrez gene IDs in a given pathway\n+ - **ratio of Entrez gene ID mapped**: number of Entrez gene IDs mapped / number total of Entrez gene IDs\n+ - **Entrez gene ID mapped**: list of mapped Entrez gene IDs\n+ - **uniprot_AC mapped** (only when Uniprot accession number is given): list of Uniprot accession number corresponding to the mapped Entrez gene IDs\n+\n+-----\n+\n+.. class:: infomark\n+\n+**Data source (release date)**\n+\n+KEGG Pathways names list are from http://rest.kegg.jp/list/pathway/\n+\n+User manual / Documentation: http://www.bioconductor.org/packages/release/bioc/html/pathview.html\n+\n+-----\n+\n+.. class:: infomark\n+\n+**Authors**\n+\n+Luo, Weijun, Brouwer, Cory (2013). \xe2\x80\x9cPathview: an R/Bioconductor package for pathway-based data integration and visualization.\xe2\x80\x9d Bioinformatics, 29(14), 1830-1831. doi: 10.1093/bioinformatics/btt285.\n+\n+-----\n+\n+.. class:: infomark\n+\n+**Galaxy integration**\n+\n+David Christiany, Florence Combes, Yves Vandenbrouck - CEA, INSERM, CNRS, Grenoble-Alpes University, BIG Institute, FR\n+\n+Sandra D\xc3\xa9rozier, Olivier Ru\xc3\xa9, Christophe Caron, Valentin Loux - INRA, Paris-Saclay University, MAIAGE Unit, Migale Bioinformatics platform, FR\n+\n+This work has been partially funded through the French National Agency for Research (ANR) IFB project.\n+\n+Contact support@proteore.org for any questions or concerns about the Galaxy implementation of this tool.\n+ ]]></help>\n+ <citations>\n+ <citation type="doi">10.1093/nar/gkx372</citation>\n+ <citation type="bibtex">\n+@misc{renameTODO,\n+ author = {Weijun Luo},\n+ year = {2013},\n+ title = {pathview},\n+ url = {https://bioconductor.org/packages/release/bioc/html/pathview.html},\n+}</citation>\n+ </citations>\n+</tool>\n'

diff -r 000000000000 -r 9845dc9c7323 kegg_pathways_list_index.loc.sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/kegg_pathways_list_index.loc.sample Tue Dec 18 10:02:54 2018 -0500

@@ -0,0 +1,4 @@
+#value name path
+hsa Homo sapiens tool-data/hsa_pathways.loc
+mmu Mus musculus tool-data/mmu_pathways.loc
+rno Rattus norvegicus tool-data/rno_pathways.loc

diff -r 000000000000 -r 9845dc9c7323 test-data/Lacombe_et_al_2017_OK.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Lacombe_et_al_2017_OK.txt Tue Dec 18 10:02:54 2018 -0500

[

@@ -0,0 +1,165 @@
+Protein accession number (UniProt) Protein name Number of peptides (razor + unique)
+P15924 Desmoplakin 69
+P02538 Keratin, type II cytoskeletal 6A 53
+P02768 Serum albumin 44
+P08779 Keratin, type I cytoskeletal 16 29
+Q02413 Desmoglein-1 24
+P07355 "Annexin A2;Putative annexin A2-like protein" 22
+P14923 Junction plakoglobin 22
+P02788 Lactotransferrin 21
+Q9HC84 Mucin-5B 21
+P29508 Serpin B3 20
+P63261 Actin, cytoplasmic 2 19
+Q8N1N4 Keratin, type II cytoskeletal 78 18
+Q04695 Keratin, type I cytoskeletal 17 18
+P01876 Ig alpha-1 chain C region 16
+Q01469 Fatty acid-binding protein 5, epidermal 15
+P31944 Caspase-14 15
+P01833 Polymeric immunoglobulin receptor 15
+P06733 Alpha-enolase 15
+P25311 Zinc-alpha-2-glycoprotein 15
+Q15149 Plectin 15
+P19013 Keratin, type II cytoskeletal 4 13
+Q6KB66 Keratin, type II cytoskeletal 80 13
+Q08188 Protein-glutamine gamma-glutamyltransferase E 12
+P13646 Keratin, type I cytoskeletal 13 11
+Q86YZ3 Hornerin 11
+P04259 Keratin, type II cytoskeletal 6B 10
+P02545 "Prelamin-A/C;Lamin-A/C" 10
+P04083 Annexin A1 10
+P11021 78 kDa glucose-regulated protein 10
+P02787 Serotransferrin 9
+P04040 Catalase 9
+P31151 Protein S100-A7 9
+P31947 14-3-3 protein sigma 9
+Q96P63 Serpin B12 9
+P14618 Pyruvate kinase PKM 9
+P60174 Triosephosphate isomerase 9
+Q06830 Peroxiredoxin-1 9
+P01040 Cystatin-A 8
+P05089 Arginase-1 8
+P01834 Ig kappa chain C region 8
+P04406 Glyceraldehyde-3-phosphate dehydrogenase 8
+P0DMV9 Heat shock 70 kDa protein 1B 8
+P13639 Elongation factor 2 8
+P35579 Myosin-9 8
+P68371 Tubulin beta-4B chain 8
+Q8WVV4 Protein POF1B 8
+O75635 Serpin B7 7
+P01857 Ig gamma-1 chain C region 7
+P61626 Lysozyme C 7
+P68363 Tubulin alpha-1B chain 7
+P01009 "Alpha-1-antitrypsin;Short peptide from AAT" 6
+P07900 Heat shock protein HSP 90-alpha 6
+Q9NZH8 Interleukin-36 gamma 6
+O43707 "Alpha-actinin-4;Alpha-actinin-1" 6
+O75223 Gamma-glutamylcyclotransferase 6
+P00338 L-lactate dehydrogenase A chain 6
+P07339 Cathepsin D 6
+P62987 Ubiquitin-60S ribosomal protein L40 6
+P10599 Thioredoxin 6
+Q9UGM3 Deleted in malignant brain tumors 1 protein 6
+Q9UI42 Carboxypeptidase A4 6
+P47929 Galectin-7 5
+Q13867 Bleomycin hydrolase 5
+Q6P4A8 Phospholipase B-like 1 5
+O75369 Filamin-B 5
+P00441 Superoxide dismutase [Cu-Zn] 5
+P04792 Heat shock protein beta-1 5
+P11142 Heat shock cognate 71 kDa protein 5
+P58107 Epiplakin 5
+P60842 Eukaryotic initiation factor 4A-I 5
+P62937 Peptidyl-prolyl cis-trans isomerase A 5
+P63104 14-3-3 protein zeta/delta 5
+Q92820 Gamma-glutamyl hydrolase 5
+O75342 Arachidonate 12-lipoxygenase, 12R-type 4
+P09211 Glutathione S-transferase P 4
+P31025 Lipocalin-1 4
+P48594 Serpin B4 4
+Q14574 Desmocollin-3 4
+Q5T750 Skin-specific protein 32 4
+Q6UWP8 Suprabasin 4
+O60911 Cathepsin L2 4
+P00558 Phosphoglycerate kinase 1 4
+P04075 Fructose-bisphosphate aldolase A 4
+P07384 Calpain-1 catalytic subunit 4
+P0CG05 Ig lambda-2 chain C regions 4
+P18206 Vinculin 4
+P62258 14-3-3 protein epsilon 4
+P68871 Hemoglobin subunit beta 4
+Q9C075 Keratin, type I cytoskeletal 23 4
+A8K2U0 Alpha-2-macroglobulin-like protein 1 3
+P00738 Haptoglobin 3
+P01011 Alpha-1-antichymotrypsin 3
+P02763 Alpha-1-acid glycoprotein 1 3
+P18510 Interleukin-1 receptor antagonist protein 3
+P22528 Cornifin-B 3
+P30740 Leukocyte elastase inhibitor 3
+P80188 Neutrophil gelatinase-associated lipocalin 3
+Q15828 Cystatin-M 3
+Q9HCY8 Protein S100-A14 3
+P01623 Ig kappa chain V-III region 3
+P01877 Ig alpha-2 chain C region 3
+P06396 Gelsolin 3
+P14735 Insulin-degrading enzyme 3
+P20933 N(4)-(beta-N-acetylglucosaminyl)-L-asparaginase 3
+P25788 Proteasome subunit alpha type-3 3
+P26641 Elongation factor 1-gamma 3
+P36952 Serpin B5 3
+P40926 Malate dehydrogenase, mitochondrial 3
+Q9Y6R7 IgGFc-binding protein 3
+O95274 Ly6/PLAUR domain-containing protein 3 2
+P00491 Purine nucleoside phosphorylase 2
+P04080 Cystatin-B 2
+P09972 Fructose-bisphosphate aldolase C 2
+P19012 Keratin, type I cytoskeletal 15 2
+P20930 Filaggrin 2
+Q96FX8 p53 apoptosis effector related to PMP-22 2
+Q9UIV8 Serpin B13 2
+P01625 Ig kappa chain V-IV region Len 2
+P01765 Ig heavy chain V-III region TIL 2
+P01766 Ig heavy chain V-III region BRO 2
+P01860 Ig gamma-3 chain C region 2
+P01871 Ig mu chain C region 2
+P05090 Apolipoprotein D 2
+P06870 Kallikrein-1 2
+P07858 Cathepsin B 2
+P08865 40S ribosomal protein SA 2
+P11279 Lysosome-associated membrane glycoprotein 1 2
+P13473 Lysosome-associated membrane glycoprotein 2 2
+P19971 Thymidine phosphorylase 2
+P23284 Peptidyl-prolyl cis-trans isomerase B 2
+P23396 40S ribosomal protein S3 2
+P25705 ATP synthase subunit alpha, mitochondrial 2
+P27482 Calmodulin-like protein 3 2
+P31949 Protein S100-A11 2
+P40121 Macrophage-capping protein 2
+P42357 Histidine ammonia-lyase 2
+P47756 F-actin-capping protein subunit beta 2
+P48637 Glutathione synthetase 2
+P49720 Proteasome subunit beta type-3 2
+P50395 Rab GDP dissociation inhibitor beta 2
+P59998 Actin-related protein 2/3 complex subunit 4 2
+P61160 Actin-related protein 2 2
+P61916 Epididymal secretory protein E1 2
+P04745 Alpha-amylase 1 23
+Q9NZT1 Calmodulin-like protein 5 8
+P12273 Prolactin-inducible protein 6
+Q96DA0 Zymogen granule protein 16 homolog B 5
+P01036 Cystatin-S 5
+Q8TAX7 Mucin-7 2
+P01037 Cystatin-SN 2
+P09228 Cystatin-SA 2
+P04264 Keratin, type II cytoskeletal 1 61
+P35908 Keratin, type II cytoskeletal 2 epidermal 40
+P13645 Keratin, type I cytoskeletal 10 40
+Q5D862 Filaggrin-2 14
+Q5T749 Keratinocyte proline-rich protein 13
+Q8IW75 Serpin A12 3
+P81605 Dermcidin 3
+P22531 Small proline-rich protein 2E 3
+P59666 Neutrophil defensin 3 2
+P78386 Keratin, type II cuticular Hb5 2
+
+
+

diff -r 000000000000 -r 9845dc9c7323 test-data/hsa00010.pathview.png

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diff -r 000000000000 -r 9845dc9c7323 test-data/hsa04514.pathview.png

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diff -r 000000000000 -r 9845dc9c7323 test-data/hsa05167.pathview.png

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diff -r 000000000000 -r 9845dc9c7323 tool-data/hsa_pathways.loc
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/hsa_pathways.loc Tue Dec 18 10:02:54 2018 -0500

b'@@ -0,0 +1,330 @@\n+#value\tname\r\n+hsa00010\tGlycolysis / Gluconeogenesis\r\n+hsa00020\tCitrate cycle (TCA cycle)\r\n+hsa00030\tPentose phosphate pathway\r\n+hsa00040\tPentose and glucuronate interconversions\r\n+hsa00051\tFructose and mannose metabolism\r\n+hsa00052\tGalactose metabolism\r\n+hsa00053\tAscorbate and aldarate metabolism\r\n+hsa00061\tFatty acid biosynthesis\r\n+hsa00062\tFatty acid elongation\r\n+hsa00071\tFatty acid degradation\r\n+hsa00072\tSynthesis and degradation of ketone bodies\r\n+hsa00100\tSteroid biosynthesis\r\n+hsa00120\tPrimary bile acid biosynthesis\r\n+hsa00130\tUbiquinone and other terpenoid-quinone biosynthesis\r\n+hsa00140\tSteroid hormone biosynthesis\r\n+hsa00190\tOxidative phosphorylation\r\n+hsa00220\tArginine biosynthesis\r\n+hsa00230\tPurine metabolism\r\n+hsa00232\tCaffeine metabolism\r\n+hsa00240\tPyrimidine metabolism\r\n+hsa00250\tAlanine, aspartate and glutamate metabolism\r\n+hsa00260\tGlycine, serine and threonine metabolism\r\n+hsa00270\tCysteine and methionine metabolism\r\n+hsa00280\tValine, leucine and isoleucine degradation\r\n+hsa00290\tValine, leucine and isoleucine biosynthesis\r\n+hsa00310\tLysine degradation\r\n+hsa00330\tArginine and proline metabolism\r\n+hsa00340\tHistidine metabolism\r\n+hsa00350\tTyrosine metabolism\r\n+hsa00360\tPhenylalanine metabolism\r\n+hsa00380\tTryptophan metabolism\r\n+hsa00400\tPhenylalanine, tyrosine and tryptophan biosynthesis\r\n+hsa00410\tbeta-Alanine metabolism\r\n+hsa00430\tTaurine and hypotaurine metabolism\r\n+hsa00440\tPhosphonate and phosphinate metabolism\r\n+hsa00450\tSelenocompound metabolism\r\n+hsa00471\tD-Glutamine and D-glutamate metabolism\r\n+hsa00472\tD-Arginine and D-ornithine metabolism\r\n+hsa00480\tGlutathione metabolism\r\n+hsa00500\tStarch and sucrose metabolism\r\n+hsa00510\tN-Glycan biosynthesis\r\n+hsa00511\tOther glycan degradation\r\n+hsa00512\tMucin type O-glycan biosynthesis\r\n+hsa00514\tOther types of O-glycan biosynthesis\r\n+hsa00515\tMannose type O-glycan biosynthesis\r\n+hsa00520\tAmino sugar and nucleotide sugar metabolism\r\n+hsa00524\tNeomycin, kanamycin and gentamicin biosynthesis\r\n+hsa00531\tGlycosaminoglycan degradation\r\n+hsa00532\tGlycosaminoglycan biosynthesis\r\n+hsa00533\tGlycosaminoglycan biosynthesis\r\n+hsa00534\tGlycosaminoglycan biosynthesis\r\n+hsa00561\tGlycerolipid metabolism\r\n+hsa00562\tInositol phosphate metabolism\r\n+hsa00563\tGlycosylphosphatidylinositol (GPI)-anchor biosynthesis\r\n+hsa00564\tGlycerophospholipid metabolism\r\n+hsa00565\tEther lipid metabolism\r\n+hsa00590\tArachidonic acid metabolism\r\n+hsa00591\tLinoleic acid metabolism\r\n+hsa00592\talpha-Linolenic acid metabolism\r\n+hsa00600\tSphingolipid metabolism\r\n+hsa00601\tGlycosphingolipid biosynthesis\r\n+hsa00603\tGlycosphingolipid biosynthesis\r\n+hsa00604\tGlycosphingolipid biosynthesis\r\n+hsa00620\tPyruvate metabolism\r\n+hsa00630\tGlyoxylate and dicarboxylate metabolism\r\n+hsa00640\tPropanoate metabolism\r\n+hsa00650\tButanoate metabolism\r\n+hsa00670\tOne carbon pool by folate\r\n+hsa00730\tThiamine metabolism\r\n+hsa00740\tRiboflavin metabolism\r\n+hsa00750\tVitamin B6 metabolism\r\n+hsa00760\tNicotinate and nicotinamide metabolism\r\n+hsa00770\tPantothenate and CoA biosynthesis\r\n+hsa00780\tBiotin metabolism\r\n+hsa00785\tLipoic acid metabolism\r\n+hsa00790\tFolate biosynthesis\r\n+hsa00830\tRetinol metabolism\r\n+hsa00860\tPorphyrin and chlorophyll metabolism\r\n+hsa00900\tTerpenoid backbone biosynthesis\r\n+hsa00910\tNitrogen metabolism\r\n+hsa00920\tSulfur metabolism\r\n+hsa00970\tAminoacyl-tRNA biosynthesis\r\n+hsa00980\tMetabolism of xenobiotics by cytochrome P450\r\n+hsa00982\tDrug metabolism\r\n+hsa00983\tDrug metabolism\r\n+hsa01040\tBiosynthesis of unsaturated fatty acids\r\n+hsa01100\tMetabolic pathways\r\n+hsa01200\tCarbon metabolism\r\n+hsa01210\t2-Oxocarboxylic acid metabolism\r\n+hsa01212\tFatty acid metabolism\r\n+hsa01230\tBiosynthesis of amino acids\r\n+hsa01521\tEGFR tyrosine kinase inhibitor resistance\r\n+hsa01522\tEndocrine resistance\r\n+hsa01523\tAntifolate resistance\r\n+hsa01524\tPlatinum drug resistance\r\n+hsa02010\tABC transporters\r\n+hsa03008\tRibosome biogenesis in eukaryotes\r\n+hsa03010\tRibosome\r\n+hsa03013\tRNA transport\r\n+hsa03015\tmRNA surveillance pat'..b"915\tEstrogen signaling pathway\r\n+hsa04916\tMelanogenesis\r\n+hsa04917\tProlactin signaling pathway\r\n+hsa04918\tThyroid hormone synthesis\r\n+hsa04919\tThyroid hormone signaling pathway\r\n+hsa04920\tAdipocytokine signaling pathway\r\n+hsa04921\tOxytocin signaling pathway\r\n+hsa04922\tGlucagon signaling pathway\r\n+hsa04923\tRegulation of lipolysis in adipocytes\r\n+hsa04924\tRenin secretion\r\n+hsa04925\tAldosterone synthesis and secretion\r\n+hsa04926\tRelaxin signaling pathway\r\n+hsa04927\tCortisol synthesis and secretion\r\n+hsa04928\tParathyroid hormone synthesis, secretion and action\r\n+hsa04930\tType II diabetes mellitus\r\n+hsa04931\tInsulin resistance\r\n+hsa04932\tNon-alcoholic fatty liver disease (NAFLD)\r\n+hsa04933\tAGE-RAGE signaling pathway in diabetic complications\r\n+hsa04934\tCushing's syndrome\r\n+hsa04940\tType I diabetes mellitus\r\n+hsa04950\tMaturity onset diabetes of the young\r\n+hsa04960\tAldosterone-regulated sodium reabsorption\r\n+hsa04961\tEndocrine and other factor-regulated calcium reabsorption\r\n+hsa04962\tVasopressin-regulated water reabsorption\r\n+hsa04964\tProximal tubule bicarbonate reclamation\r\n+hsa04966\tCollecting duct acid secretion\r\n+hsa04970\tSalivary secretion\r\n+hsa04971\tGastric acid secretion\r\n+hsa04972\tPancreatic secretion\r\n+hsa04973\tCarbohydrate digestion and absorption\r\n+hsa04974\tProtein digestion and absorption\r\n+hsa04975\tFat digestion and absorption\r\n+hsa04976\tBile secretion\r\n+hsa04977\tVitamin digestion and absorption\r\n+hsa04978\tMineral absorption\r\n+hsa04979\tCholesterol metabolism\r\n+hsa05010\tAlzheimer's disease\r\n+hsa05012\tParkinson's disease\r\n+hsa05014\tAmyotrophic lateral sclerosis (ALS)\r\n+hsa05016\tHuntington's disease\r\n+hsa05020\tPrion diseases\r\n+hsa05030\tCocaine addiction\r\n+hsa05031\tAmphetamine addiction\r\n+hsa05032\tMorphine addiction\r\n+hsa05033\tNicotine addiction\r\n+hsa05034\tAlcoholism\r\n+hsa05100\tBacterial invasion of epithelial cells\r\n+hsa05110\tVibrio cholerae infection\r\n+hsa05120\tEpithelial cell signaling in Helicobacter pylori infection\r\n+hsa05130\tPathogenic Escherichia coli infection\r\n+hsa05131\tShigellosis\r\n+hsa05132\tSalmonella infection\r\n+hsa05133\tPertussis\r\n+hsa05134\tLegionellosis\r\n+hsa05140\tLeishmaniasis\r\n+hsa05142\tChagas disease (American trypanosomiasis)\r\n+hsa05143\tAfrican trypanosomiasis\r\n+hsa05144\tMalaria\r\n+hsa05145\tToxoplasmosis\r\n+hsa05146\tAmoebiasis\r\n+hsa05150\tStaphylococcus aureus infection\r\n+hsa05152\tTuberculosis\r\n+hsa05160\tHepatitis C\r\n+hsa05161\tHepatitis B\r\n+hsa05162\tMeasles\r\n+hsa05163\tHuman cytomegalovirus infection\r\n+hsa05164\tInfluenza A\r\n+hsa05165\tHuman papillomavirus infection\r\n+hsa05166\tHTLV-I infection\r\n+hsa05167\tKaposi's sarcoma-associated herpesvirus infection\r\n+hsa05168\tHerpes simplex infection\r\n+hsa05169\tEpstein-Barr virus infection\r\n+hsa05200\tPathways in cancer\r\n+hsa05202\tTranscriptional misregulation in cancer\r\n+hsa05203\tViral carcinogenesis\r\n+hsa05204\tChemical carcinogenesis\r\n+hsa05205\tProteoglycans in cancer\r\n+hsa05206\tMicroRNAs in cancer\r\n+hsa05210\tColorectal cancer\r\n+hsa05211\tRenal cell carcinoma\r\n+hsa05212\tPancreatic cancer\r\n+hsa05213\tEndometrial cancer\r\n+hsa05214\tGlioma\r\n+hsa05215\tProstate cancer\r\n+hsa05216\tThyroid cancer\r\n+hsa05217\tBasal cell carcinoma\r\n+hsa05218\tMelanoma\r\n+hsa05219\tBladder cancer\r\n+hsa05220\tChronic myeloid leukemia\r\n+hsa05221\tAcute myeloid leukemia\r\n+hsa05222\tSmall cell lung cancer\r\n+hsa05223\tNon-small cell lung cancer\r\n+hsa05224\tBreast cancer\r\n+hsa05225\tHepatocellular carcinoma\r\n+hsa05226\tGastric cancer\r\n+hsa05230\tCentral carbon metabolism in cancer\r\n+hsa05231\tCholine metabolism in cancer\r\n+hsa05310\tAsthma\r\n+hsa05320\tAutoimmune thyroid disease\r\n+hsa05321\tInflammatory bowel disease (IBD)\r\n+hsa05322\tSystemic lupus erythematosus\r\n+hsa05323\tRheumatoid arthritis\r\n+hsa05330\tAllograft rejection\r\n+hsa05332\tGraft-versus-host disease\r\n+hsa05340\tPrimary immunodeficiency\r\n+hsa05410\tHypertrophic cardiomyopathy (HCM)\r\n+hsa05412\tArrhythmogenic right ventricular cardiomyopathy (ARVC)\r\n+hsa05414\tDilated cardiomyopathy (DCM)\r\n+hsa05416\tViral myocarditis\r\n+hsa05418\tFluid shear stress and atherosclerosis\r\n"

diff -r 000000000000 -r 9845dc9c7323 tool-data/hsa_pathways.loc.sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/hsa_pathways.loc.sample Tue Dec 18 10:02:54 2018 -0500

diff -r 000000000000 -r 9845dc9c7323 tool-data/mmu_pathways.loc
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/mmu_pathways.loc Tue Dec 18 10:02:54 2018 -0500

b'@@ -0,0 +1,326 @@\n+#value\tname\r\n+mmu00010\tGlycolysis / Gluconeogenesis\r\n+mmu00020\tCitrate cycle (TCA cycle)\r\n+mmu00030\tPentose phosphate pathway\r\n+mmu00040\tPentose and glucuronate interconversions\r\n+mmu00051\tFructose and mannose metabolism\r\n+mmu00052\tGalactose metabolism\r\n+mmu00053\tAscorbate and aldarate metabolism\r\n+mmu00061\tFatty acid biosynthesis\r\n+mmu00062\tFatty acid elongation\r\n+mmu00071\tFatty acid degradation\r\n+mmu00072\tSynthesis and degradation of ketone bodies\r\n+mmu00100\tSteroid biosynthesis\r\n+mmu00120\tPrimary bile acid biosynthesis\r\n+mmu00130\tUbiquinone and other terpenoid-quinone biosynthesis\r\n+mmu00140\tSteroid hormone biosynthesis\r\n+mmu00190\tOxidative phosphorylation\r\n+mmu00220\tArginine biosynthesis\r\n+mmu00230\tPurine metabolism\r\n+mmu00232\tCaffeine metabolism\r\n+mmu00240\tPyrimidine metabolism\r\n+mmu00250\tAlanine, aspartate and glutamate metabolism\r\n+mmu00260\tGlycine, serine and threonine metabolism\r\n+mmu00270\tCysteine and methionine metabolism\r\n+mmu00280\tValine, leucine and isoleucine degradation\r\n+mmu00290\tValine, leucine and isoleucine biosynthesis\r\n+mmu00310\tLysine degradation\r\n+mmu00330\tArginine and proline metabolism\r\n+mmu00340\tHistidine metabolism\r\n+mmu00350\tTyrosine metabolism\r\n+mmu00360\tPhenylalanine metabolism\r\n+mmu00380\tTryptophan metabolism\r\n+mmu00400\tPhenylalanine, tyrosine and tryptophan biosynthesis\r\n+mmu00410\tbeta-Alanine metabolism\r\n+mmu00430\tTaurine and hypotaurine metabolism\r\n+mmu00440\tPhosphonate and phosphinate metabolism\r\n+mmu00450\tSelenocompound metabolism\r\n+mmu00471\tD-Glutamine and D-glutamate metabolism\r\n+mmu00472\tD-Arginine and D-ornithine metabolism\r\n+mmu00480\tGlutathione metabolism\r\n+mmu00500\tStarch and sucrose metabolism\r\n+mmu00510\tN-Glycan biosynthesis\r\n+mmu00511\tOther glycan degradation\r\n+mmu00512\tMucin type O-glycan biosynthesis\r\n+mmu00514\tOther types of O-glycan biosynthesis\r\n+mmu00515\tMannose type O-glycan biosynthesis\r\n+mmu00520\tAmino sugar and nucleotide sugar metabolism\r\n+mmu00524\tNeomycin, kanamycin and gentamicin biosynthesis\r\n+mmu00531\tGlycosaminoglycan degradation\r\n+mmu00532\tGlycosaminoglycan biosynthesis\r\n+mmu00533\tGlycosaminoglycan biosynthesis\r\n+mmu00534\tGlycosaminoglycan biosynthesis\r\n+mmu00561\tGlycerolipid metabolism\r\n+mmu00562\tInositol phosphate metabolism\r\n+mmu00563\tGlycosylphosphatidylinositol (GPI)-anchor biosynthesis\r\n+mmu00564\tGlycerophospholipid metabolism\r\n+mmu00565\tEther lipid metabolism\r\n+mmu00590\tArachidonic acid metabolism\r\n+mmu00591\tLinoleic acid metabolism\r\n+mmu00592\talpha-Linolenic acid metabolism\r\n+mmu00600\tSphingolipid metabolism\r\n+mmu00601\tGlycosphingolipid biosynthesis\r\n+mmu00603\tGlycosphingolipid biosynthesis\r\n+mmu00604\tGlycosphingolipid biosynthesis\r\n+mmu00620\tPyruvate metabolism\r\n+mmu00630\tGlyoxylate and dicarboxylate metabolism\r\n+mmu00640\tPropanoate metabolism\r\n+mmu00650\tButanoate metabolism\r\n+mmu00670\tOne carbon pool by folate\r\n+mmu00730\tThiamine metabolism\r\n+mmu00740\tRiboflavin metabolism\r\n+mmu00750\tVitamin B6 metabolism\r\n+mmu00760\tNicotinate and nicotinamide metabolism\r\n+mmu00770\tPantothenate and CoA biosynthesis\r\n+mmu00780\tBiotin metabolism\r\n+mmu00785\tLipoic acid metabolism\r\n+mmu00790\tFolate biosynthesis\r\n+mmu00830\tRetinol metabolism\r\n+mmu00860\tPorphyrin and chlorophyll metabolism\r\n+mmu00900\tTerpenoid backbone biosynthesis\r\n+mmu00910\tNitrogen metabolism\r\n+mmu00920\tSulfur metabolism\r\n+mmu00970\tAminoacyl-tRNA biosynthesis\r\n+mmu00980\tMetabolism of xenobiotics by cytochrome P450\r\n+mmu00982\tDrug metabolism\r\n+mmu00983\tDrug metabolism\r\n+mmu01040\tBiosynthesis of unsaturated fatty acids\r\n+mmu01100\tMetabolic pathways\r\n+mmu01200\tCarbon metabolism\r\n+mmu01210\t2-Oxocarboxylic acid metabolism\r\n+mmu01212\tFatty acid metabolism\r\n+mmu01230\tBiosynthesis of amino acids\r\n+mmu01521\tEGFR tyrosine kinase inhibitor resistance\r\n+mmu01522\tEndocrine resistance\r\n+mmu01523\tAntifolate resistance\r\n+mmu01524\tPlatinum drug resistance\r\n+mmu02010\tABC transporters\r\n+mmu03008\tRibosome biogenesis in eukaryotes\r\n+mmu03010\tRibosome\r\n+mmu03013\tRNA transport\r\n+mmu03015\tmRNA surveillance pat'..b"ulin signaling pathway\r\n+mmu04911\tInsulin secretion\r\n+mmu04912\tGnRH signaling pathway\r\n+mmu04913\tOvarian steroidogenesis\r\n+mmu04914\tProgesterone-mediated oocyte maturation\r\n+mmu04915\tEstrogen signaling pathway\r\n+mmu04916\tMelanogenesis\r\n+mmu04917\tProlactin signaling pathway\r\n+mmu04918\tThyroid hormone synthesis\r\n+mmu04919\tThyroid hormone signaling pathway\r\n+mmu04920\tAdipocytokine signaling pathway\r\n+mmu04921\tOxytocin signaling pathway\r\n+mmu04922\tGlucagon signaling pathway\r\n+mmu04923\tRegulation of lipolysis in adipocytes\r\n+mmu04924\tRenin secretion\r\n+mmu04925\tAldosterone synthesis and secretion\r\n+mmu04926\tRelaxin signaling pathway\r\n+mmu04927\tCortisol synthesis and secretion\r\n+mmu04928\tParathyroid hormone synthesis, secretion and action\r\n+mmu04930\tType II diabetes mellitus\r\n+mmu04931\tInsulin resistance\r\n+mmu04932\tNon-alcoholic fatty liver disease (NAFLD)\r\n+mmu04933\tAGE-RAGE signaling pathway in diabetic complications\r\n+mmu04934\tCushing's syndrome\r\n+mmu04940\tType I diabetes mellitus\r\n+mmu04950\tMaturity onset diabetes of the young\r\n+mmu04960\tAldosterone-regulated sodium reabsorption\r\n+mmu04961\tEndocrine and other factor-regulated calcium reabsorption\r\n+mmu04962\tVasopressin-regulated water reabsorption\r\n+mmu04964\tProximal tubule bicarbonate reclamation\r\n+mmu04966\tCollecting duct acid secretion\r\n+mmu04970\tSalivary secretion\r\n+mmu04971\tGastric acid secretion\r\n+mmu04972\tPancreatic secretion\r\n+mmu04973\tCarbohydrate digestion and absorption\r\n+mmu04974\tProtein digestion and absorption\r\n+mmu04975\tFat digestion and absorption\r\n+mmu04976\tBile secretion\r\n+mmu04977\tVitamin digestion and absorption\r\n+mmu04978\tMineral absorption\r\n+mmu04979\tCholesterol metabolism\r\n+mmu05010\tAlzheimer's disease\r\n+mmu05012\tParkinson's disease\r\n+mmu05014\tAmyotrophic lateral sclerosis (ALS)\r\n+mmu05016\tHuntington's disease\r\n+mmu05020\tPrion diseases\r\n+mmu05030\tCocaine addiction\r\n+mmu05031\tAmphetamine addiction\r\n+mmu05032\tMorphine addiction\r\n+mmu05033\tNicotine addiction\r\n+mmu05034\tAlcoholism\r\n+mmu05100\tBacterial invasion of epithelial cells\r\n+mmu05132\tSalmonella infection\r\n+mmu05133\tPertussis\r\n+mmu05134\tLegionellosis\r\n+mmu05140\tLeishmaniasis\r\n+mmu05142\tChagas disease (American trypanosomiasis)\r\n+mmu05143\tAfrican trypanosomiasis\r\n+mmu05144\tMalaria\r\n+mmu05145\tToxoplasmosis\r\n+mmu05146\tAmoebiasis\r\n+mmu05150\tStaphylococcus aureus infection\r\n+mmu05152\tTuberculosis\r\n+mmu05160\tHepatitis C\r\n+mmu05161\tHepatitis B\r\n+mmu05162\tMeasles\r\n+mmu05163\tHuman cytomegalovirus infection\r\n+mmu05164\tInfluenza A\r\n+mmu05165\tHuman papillomavirus infection\r\n+mmu05166\tHTLV-I infection\r\n+mmu05167\tKaposi's sarcoma-associated herpesvirus infection\r\n+mmu05168\tHerpes simplex infection\r\n+mmu05169\tEpstein-Barr virus infection\r\n+mmu05200\tPathways in cancer\r\n+mmu05202\tTranscriptional misregulation in cancer\r\n+mmu05203\tViral carcinogenesis\r\n+mmu05204\tChemical carcinogenesis\r\n+mmu05205\tProteoglycans in cancer\r\n+mmu05206\tMicroRNAs in cancer\r\n+mmu05210\tColorectal cancer\r\n+mmu05211\tRenal cell carcinoma\r\n+mmu05212\tPancreatic cancer\r\n+mmu05213\tEndometrial cancer\r\n+mmu05214\tGlioma\r\n+mmu05215\tProstate cancer\r\n+mmu05216\tThyroid cancer\r\n+mmu05217\tBasal cell carcinoma\r\n+mmu05218\tMelanoma\r\n+mmu05219\tBladder cancer\r\n+mmu05220\tChronic myeloid leukemia\r\n+mmu05221\tAcute myeloid leukemia\r\n+mmu05222\tSmall cell lung cancer\r\n+mmu05223\tNon-small cell lung cancer\r\n+mmu05224\tBreast cancer\r\n+mmu05225\tHepatocellular carcinoma\r\n+mmu05226\tGastric cancer\r\n+mmu05230\tCentral carbon metabolism in cancer\r\n+mmu05231\tCholine metabolism in cancer\r\n+mmu05310\tAsthma\r\n+mmu05320\tAutoimmune thyroid disease\r\n+mmu05321\tInflammatory bowel disease (IBD)\r\n+mmu05322\tSystemic lupus erythematosus\r\n+mmu05323\tRheumatoid arthritis\r\n+mmu05330\tAllograft rejection\r\n+mmu05332\tGraft-versus-host disease\r\n+mmu05340\tPrimary immunodeficiency\r\n+mmu05410\tHypertrophic cardiomyopathy (HCM)\r\n+mmu05412\tArrhythmogenic right ventricular cardiomyopathy (ARVC)\r\n+mmu05414\tDilated cardiomyopathy (DCM)\r\n+mmu05416\tViral myocarditis\r\n+mmu05418\tFluid shear stress and atherosclerosis\r\n"

diff -r 000000000000 -r 9845dc9c7323 tool-data/mmu_pathways.loc.sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/mmu_pathways.loc.sample Tue Dec 18 10:02:54 2018 -0500

diff -r 000000000000 -r 9845dc9c7323 tool-data/rno_pathways.loc
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/rno_pathways.loc Tue Dec 18 10:02:54 2018 -0500

b'@@ -0,0 +1,326 @@\n+rno00010\tGlycolysis / Gluconeogenesis\n+rno00020\tCitrate cycle (TCA cycle)\n+rno00030\tPentose phosphate pathway\n+rno00040\tPentose and glucuronate interconversions\n+rno00051\tFructose and mannose metabolism\n+rno00052\tGalactose metabolism\n+rno00053\tAscorbate and aldarate metabolism\n+rno00061\tFatty acid biosynthesis\n+rno00062\tFatty acid elongation\n+rno00071\tFatty acid degradation\n+rno00072\tSynthesis and degradation of ketone bodies\n+rno00100\tSteroid biosynthesis\n+rno00120\tPrimary bile acid biosynthesis\n+rno00130\tUbiquinone and other terpenoid-quinone biosynthesis\n+rno00140\tSteroid hormone biosynthesis\n+rno00190\tOxidative phosphorylation\n+rno00220\tArginine biosynthesis\n+rno00230\tPurine metabolism\n+rno00232\tCaffeine metabolism\n+rno00240\tPyrimidine metabolism\n+rno00250\tAlanine, aspartate and glutamate metabolism\n+rno00260\tGlycine, serine and threonine metabolism\n+rno00270\tCysteine and methionine metabolism\n+rno00280\tValine, leucine and isoleucine degradation\n+rno00290\tValine, leucine and isoleucine biosynthesis\n+rno00310\tLysine degradation\n+rno00330\tArginine and proline metabolism\n+rno00340\tHistidine metabolism\n+rno00350\tTyrosine metabolism\n+rno00360\tPhenylalanine metabolism\n+rno00380\tTryptophan metabolism\n+rno00400\tPhenylalanine, tyrosine and tryptophan biosynthesis\n+rno00410\tbeta-Alanine metabolism\n+rno00430\tTaurine and hypotaurine metabolism\n+rno00440\tPhosphonate and phosphinate metabolism\n+rno00450\tSelenocompound metabolism\n+rno00471\tD-Glutamine and D-glutamate metabolism\n+rno00472\tD-Arginine and D-ornithine metabolism\n+rno00480\tGlutathione metabolism\n+rno00500\tStarch and sucrose metabolism\n+rno00510\tN-Glycan biosynthesis\n+rno00511\tOther glycan degradation\n+rno00512\tMucin type O-glycan biosynthesis\n+rno00514\tOther types of O-glycan biosynthesis\n+rno00515\tMannose type O-glycan biosynthesis\n+rno00520\tAmino sugar and nucleotide sugar metabolism\n+rno00524\tNeomycin, kanamycin and gentamicin biosynthesis\n+rno00531\tGlycosaminoglycan degradation\n+rno00532\tGlycosaminoglycan biosynthesis\n+rno00533\tGlycosaminoglycan biosynthesis\n+rno00534\tGlycosaminoglycan biosynthesis\n+rno00561\tGlycerolipid metabolism\n+rno00562\tInositol phosphate metabolism\n+rno00563\tGlycosylphosphatidylinositol (GPI)-anchor biosynthesis\n+rno00564\tGlycerophospholipid metabolism\n+rno00565\tEther lipid metabolism\n+rno00590\tArachidonic acid metabolism\n+rno00591\tLinoleic acid metabolism\n+rno00592\talpha-Linolenic acid metabolism\n+rno00600\tSphingolipid metabolism\n+rno00601\tGlycosphingolipid biosynthesis\n+rno00603\tGlycosphingolipid biosynthesis\n+rno00604\tGlycosphingolipid biosynthesis\n+rno00620\tPyruvate metabolism\n+rno00630\tGlyoxylate and dicarboxylate metabolism\n+rno00640\tPropanoate metabolism\n+rno00650\tButanoate metabolism\n+rno00670\tOne carbon pool by folate\n+rno00730\tThiamine metabolism\n+rno00740\tRiboflavin metabolism\n+rno00750\tVitamin B6 metabolism\n+rno00760\tNicotinate and nicotinamide metabolism\n+rno00770\tPantothenate and CoA biosynthesis\n+rno00780\tBiotin metabolism\n+rno00785\tLipoic acid metabolism\n+rno00790\tFolate biosynthesis\n+rno00830\tRetinol metabolism\n+rno00860\tPorphyrin and chlorophyll metabolism\n+rno00900\tTerpenoid backbone biosynthesis\n+rno00910\tNitrogen metabolism\n+rno00920\tSulfur metabolism\n+rno00970\tAminoacyl-tRNA biosynthesis\n+rno00980\tMetabolism of xenobiotics by cytochrome P450\n+rno00982\tDrug metabolism\n+rno00983\tDrug metabolism\n+rno01040\tBiosynthesis of unsaturated fatty acids\n+rno01100\tMetabolic pathways\n+rno01200\tCarbon metabolism\n+rno01210\t2-Oxocarboxylic acid metabolism\n+rno01212\tFatty acid metabolism\n+rno01230\tBiosynthesis of amino acids\n+rno01521\tEGFR tyrosine kinase inhibitor resistance\n+rno01522\tEndocrine resistance\n+rno01523\tAntifolate resistance\n+rno01524\tPlatinum drug resistance\n+rno02010\tABC transporters\n+rno03008\tRibosome biogenesis in eukaryotes\n+rno03010\tRibosome\n+rno03013\tRNA transport\n+rno03015\tmRNA surveillance pathway\n+rno03018\tRNA degradation\n+rno03020\tRNA polymerase\n+rno03022\tBasal transcription factors\n+rno03030\tDNA repli'..b'\tRegulation of actin cytoskeleton\n+rno04910\tInsulin signaling pathway\n+rno04911\tInsulin secretion\n+rno04912\tGnRH signaling pathway\n+rno04913\tOvarian steroidogenesis\n+rno04914\tProgesterone-mediated oocyte maturation\n+rno04915\tEstrogen signaling pathway\n+rno04916\tMelanogenesis\n+rno04917\tProlactin signaling pathway\n+rno04918\tThyroid hormone synthesis\n+rno04919\tThyroid hormone signaling pathway\n+rno04920\tAdipocytokine signaling pathway\n+rno04921\tOxytocin signaling pathway\n+rno04922\tGlucagon signaling pathway\n+rno04923\tRegulation of lipolysis in adipocytes\n+rno04924\tRenin secretion\n+rno04925\tAldosterone synthesis and secretion\n+rno04926\tRelaxin signaling pathway\n+rno04927\tCortisol synthesis and secretion\n+rno04928\tParathyroid hormone synthesis, secretion and action\n+rno04930\tType II diabetes mellitus\n+rno04931\tInsulin resistance\n+rno04932\tNon-alcoholic fatty liver disease (NAFLD)\n+rno04933\tAGE-RAGE signaling pathway in diabetic complications\n+rno04934\tCushing syndrome\n+rno04940\tType I diabetes mellitus\n+rno04950\tMaturity onset diabetes of the young\n+rno04960\tAldosterone-regulated sodium reabsorption\n+rno04961\tEndocrine and other factor-regulated calcium reabsorption\n+rno04962\tVasopressin-regulated water reabsorption\n+rno04964\tProximal tubule bicarbonate reclamation\n+rno04966\tCollecting duct acid secretion\n+rno04970\tSalivary secretion\n+rno04971\tGastric acid secretion\n+rno04972\tPancreatic secretion\n+rno04973\tCarbohydrate digestion and absorption\n+rno04974\tProtein digestion and absorption\n+rno04975\tFat digestion and absorption\n+rno04976\tBile secretion\n+rno04977\tVitamin digestion and absorption\n+rno04978\tMineral absorption\n+rno04979\tCholesterol metabolism\n+rno05010\tAlzheimer disease\n+rno05012\tParkinson disease\n+rno05014\tAmyotrophic lateral sclerosis (ALS)\n+rno05016\tHuntington disease\n+rno05020\tPrion diseases\n+rno05030\tCocaine addiction\n+rno05031\tAmphetamine addiction\n+rno05032\tMorphine addiction\n+rno05033\tNicotine addiction\n+rno05034\tAlcoholism\n+rno05100\tBacterial invasion of epithelial cells\n+rno05132\tSalmonella infection\n+rno05133\tPertussis\n+rno05134\tLegionellosis\n+rno05140\tLeishmaniasis\n+rno05142\tChagas disease (American trypanosomiasis)\n+rno05143\tAfrican trypanosomiasis\n+rno05144\tMalaria\n+rno05145\tToxoplasmosis\n+rno05146\tAmoebiasis\n+rno05150\tStaphylococcus aureus infection\n+rno05152\tTuberculosis\n+rno05160\tHepatitis C\n+rno05161\tHepatitis B\n+rno05162\tMeasles\n+rno05163\tHuman cytomegalovirus infection\n+rno05164\tInfluenza A\n+rno05165\tHuman papillomavirus infection\n+rno05166\tHuman T-cell leukemia virus 1 infection\n+rno05167\tKaposi sarcoma-associated herpesvirus infection\n+rno05168\tHerpes simplex infection\n+rno05169\tEpstein-Barr virus infection\n+rno05170\tHuman immunodeficiency virus 1 infection\n+rno05200\tPathways in cancer\n+rno05202\tTranscriptional misregulation in cancer\n+rno05203\tViral carcinogenesis\n+rno05204\tChemical carcinogenesis\n+rno05205\tProteoglycans in cancer\n+rno05206\tMicroRNAs in cancer\n+rno05210\tColorectal cancer\n+rno05211\tRenal cell carcinoma\n+rno05212\tPancreatic cancer\n+rno05213\tEndometrial cancer\n+rno05214\tGlioma\n+rno05215\tProstate cancer\n+rno05216\tThyroid cancer\n+rno05217\tBasal cell carcinoma\n+rno05218\tMelanoma\n+rno05219\tBladder cancer\n+rno05220\tChronic myeloid leukemia\n+rno05221\tAcute myeloid leukemia\n+rno05222\tSmall cell lung cancer\n+rno05223\tNon-small cell lung cancer\n+rno05224\tBreast cancer\n+rno05225\tHepatocellular carcinoma\n+rno05226\tGastric cancer\n+rno05230\tCentral carbon metabolism in cancer\n+rno05231\tCholine metabolism in cancer\n+rno05310\tAsthma\n+rno05320\tAutoimmune thyroid disease\n+rno05321\tInflammatory bowel disease (IBD)\n+rno05322\tSystemic lupus erythematosus\n+rno05323\tRheumatoid arthritis\n+rno05330\tAllograft rejection\n+rno05332\tGraft-versus-host disease\n+rno05340\tPrimary immunodeficiency\n+rno05410\tHypertrophic cardiomyopathy (HCM)\n+rno05412\tArrhythmogenic right ventricular cardiomyopathy (ARVC)\n+rno05414\tDilated cardiomyopathy (DCM)\n+rno05416\tViral myocarditis\n+rno05418\tFluid shear stress and atherosclerosis\n'

diff -r 000000000000 -r 9845dc9c7323 tool-data/rno_pathways.loc.sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool-data/rno_pathways.loc.sample Tue Dec 18 10:02:54 2018 -0500

diff -r 000000000000 -r 9845dc9c7323 tool_data_table_conf.xml.sample
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_data_table_conf.xml.sample Tue Dec 18 10:02:54 2018 -0500

@@ -0,0 +1,19 @@
+<tables>
+    
+    <table name="kegg_pathways_list_index" comment_char="#">
+ <columns>value,name,path</columns>
+ <file path="tool-data/kegg_pathways_list_index.loc"/>
+    </table>
+    <table name="hsa_pathways" comment_char="#">
+        <columns>value,name</columns>
+        <file path="tool-data/hsa_pathways.loc" />
+    </table>
+    <table name="mmu_pathways" comment_char="#">
+        <columns>value,name</columns>
+        <file path="tool-data/mmu_pathways.loc" />
+    </table>
+    <table name="rno_pathways" comment_char="#">
+        <columns>value,name</columns>
+        <file path="tool-data/rno_pathways.loc" />
+    </table>
+</tables>