Mercurial > repos > proteore > proteore_go_terms_enrich_comparison

changeset 1:528652235016 draft

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planemo upload commit c9d70181a2b587e53dcc4b5885b74b625def6b8c-dirty
author proteore
date Fri, 13 Dec 2019 05:16:36 -0500
parents 04f363ee805a
children ec6f7de49e86
files GO_terms_enrich_comparison.xml
diffstat 1 files changed, 17 insertions(+), 24 deletions(-) [+]
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line diff
--- a/GO_terms_enrich_comparison.xml	Tue Dec 10 04:15:39 2019 -0500
+++ b/GO_terms_enrich_comparison.xml	Fri Dec 13 05:16:36 2019 -0500
@@ -1,4 +1,4 @@
-<tool id="go_terms_enrich_comparison" name="GO terms enrich comparison " version="2019.11.19.1">
+<tool id="go_terms_enrich_comparison" name="GO terms enrich comparison " version="2019.12.13">
     <description>(Human, Mouse, Rat)[clusterProfiler]</description>
     <requirements>
         <requirement type="package">R</requirement>
@@ -135,13 +135,12 @@
 
 **Description**
 
-This tool is based on R package clusterProfiler and allows to perform GO terms classification and enrichment analyses on gene/protein sets (e.g. given a set of genes that are up-regulated under certain conditions, an enrichment analysis will find which GO terms are over-represented (or under-represented) using annotations for that gene/protein set).
-
-Given a list of IDs, this tool: 
+This tool is based on the R package clusterProfiler and allows to compare GO terms enrichment analyses of gene/protein sets. 
+Given a list of gene set, this function will compute GO enrichment profiles of each gene list.
 
-(i)  performs gene classification based on GO distribution at a specific level,
-  
-(ii) calculates GO categories enrichment (over- or under-representation) for the IDs of the input list, compared to a background. User has the possibility to use background corresponding to the whole organism or to a user-defined list. In this latter case, we recommand to use the "Build tissue-specific expression dataset" ProteoRE tool to create this list according to your need. 
+This tool calculates GO categories enrichment (over- or under-representation) for the IDs of the input list, compared to a background. 
+User has the possibility to use background corresponding to the whole organism or to a user-defined list. 
+In this latter case, user has the possibility to use the "Build tissue-specific expression dataset" ProteoRE tool to create this list according to your need. 
 
 -----
 
@@ -149,7 +148,14 @@
 
 Two modes are allowed: either by supplying a tabular file (.csv, .tsv, .txt, .tab) including your IDs (identifiers) or by copy/pasting your IDs (separated by a space).
 
-"Select type/source of IDs": only entrez gene ID (e.g : 4151, 7412) or Uniprot accession number (e.g. P31946) are allowed. If your list is not in this form, please use the ID_Converter tool of ProteoRE. 
+"Enter your Gene ID list": only Entrez Gene ID (e.g : 4151, 7412).  
+If your IDs are not Entrez Gene IDs, please use the ID_Converter tool of ProteoRE. 
+
+"Does file contain header?": in case of file input, you specify if there is header or not. 
+
+"Column number of IDs": in which column are your IDs. (e.g. for column 2 you write "c2")
+
+"name of your list": write the name for this list
 
 -----
 
@@ -157,27 +163,14 @@
 
 "Species": the three supported species are Homo sapiens, Mus musculus and Rattus norvegicus 
 
-"Perform GO categories representation analysis?": classify genes based on their projection at a specific level of the GO corpus (see parameter below), and provides functions (set to "Yes")
-
-"Ontology level (the higher this number, the deeper the GO level)": correspond to the level of GO hierarchy (from 1 to 3) (set to level "2" by default). In general the higher the level, the more semantically specific the term is.
-
-"Perform GO categories enrichment analysis?": calculate enrichment test for GO terms based on hypergeometric distribution (set to "Yes")
-
-"P-value cut off": P-value threshold value for the declaration of significance (default is < 0.01)
-
-"Q-value cut off": to prevent high false discovery rate (FDR) in multiple testing, Q-values (adjusted P-values) are estimated for FDR control. (default is < 0.05)
-
-"Define your own background IDs?": by default the whole genome/proteome is used as a reference background to compute the enrichment. As this reference set should normally only include genes/proteins that were monitored during your analysis, this option allows to provide your own background; this could be for instance, the total number of genes/proteins expressed in the tissue/sample under study. 
-
-If you want to use your own background, click on the "Yes" button. Your gene/protein set must be a list of Entrez gene ID or Uniprot accession number (otherwise, use the ID-Converter tool of ProteoRE). Select the file containing your list of ID (as background), then specify the column number which contains IDs and the type of IDs (gene Entrez or Uniprot Accession number) as requested. 
-
-Of note: for Human species, you can build your own background by using the "Build tissue-specific expression dataset" tool of ProteoRE.
+"Select GO terms category": allows you to perform analysis on one, two or three categories of the Gene Ontology. The categories are 
+ Cellular Component (CC), Biological Process (BP) and Molecular Function (MF). 
 
 ----- 
 
 **Output**
 
-Diagram output: graphical output in the form of bar-plot or dot-plot (png, jpeg or pdf format), one figure for each GO category. 
+Diagram output: graphical output in the form of a dot-plot (png, jpeg or pdf format), one figure for each GO category. 
 Text tables: with the following information GO category description (e.g.BP.Description), GO term identifier (e.g. BP.GOID) and GO term frequency (e.g. BP.Frequency)d graphics representing the repartition and/or enrichment of GO categories. One table and one graphic will be produced for each GO catagory. 
 
 -----