from listsgalaxy_sequence_utilsrpylimmabiopython
python $__tool_directory__/venn_list.py --version
python $__tool_directory__/venn_list.py
#if $universe.type_select=="implicit":
- -
#else:
'$main' $main.ext
#end if
'$main_lab'
#for $s in $sets:
'$s.set' $s.set.ext '$s.lab'
#end for
$PDF
.. class:: infomark
**TIP:** If your data is in tabular files, the identifier is assumed to be in column one.
**What it does**
Draws Venn Diagram for one, two or three sets (as a PDF file).
You must supply one, two or three sets of identifiers -- corresponding
to one, two or three circles on the Venn Diagram.
In general you should also give the full list of all the identifiers
explicitly. This is used to calculate the number of identifers outside
the circles (and check the identifiers in the other files match up).
The full list can be omitted by implicitly taking the union of the
category sets. In this case, the count outside the categories (circles)
will always be zero.
The identifiers can be taken from the first column of a tabular file
(e.g. query names in BLAST tabular output, or signal peptide predictions
after filtering, etc), or from a sequence file (FASTA, FASTQ, SFF).
For example, you may have a set of NGS reads (as a FASTA, FASTQ or SFF
file), and the results of several different read mappings (e.g. to
different references) as tabular files (filtered to have just the mapped
reads). You could then show the different mappings (and their overlaps)
as a Venn Diagram, and the outside count would be the unmapped reads.
**Citations**
The Venn Diagrams are drawn using Gordon Smyth's limma package from
R/Bioconductor, http://www.bioconductor.org/
The R library is called from Python via rpy, http://rpy.sourceforge.net/
If you use this Galaxy tool in work leading to a scientific publication please
cite:
Peter J.A. Cock, Björn A. Grüning, Konrad Paszkiewicz and Leighton Pritchard (2013).
Galaxy tools and workflows for sequence analysis with applications
in molecular plant pathology. PeerJ 1:e167
http://dx.doi.org/10.7717/peerj.167
This tool uses Biopython to read and write SFF files, so you may also wish to
cite the Biopython application note (and Galaxy too of course):
Cock et al 2009. Biopython: freely available Python tools for computational
molecular biology and bioinformatics. Bioinformatics 25(11) 1422-3.
http://dx.doi.org/10.1093/bioinformatics/btp163 pmid:19304878.
10.7717/peerj.16710.1093/bioinformatics/15.5.356