diff tools/protein_analysis/promoter2.xml @ 19:4cd848c5590b draft

Uploaded v0.2.5 preview 3, use $NSLOTS in the PSORT wrappers.

--- a/tools/protein_analysis/promoter2.xml	Fri May 10 07:48:26 2013 -0400
+++ b/tools/protein_analysis/promoter2.xml	Thu May 23 12:49:18 2013 -0400
@@ -1,13 +1,15 @@
-<tool id="promoter2" name="Promoter 2.0" version="0.0.5">
+<tool id="promoter2" name="Promoter 2.0" version="0.0.6">
     <description>Find eukaryotic PolII promoters in DNA sequences</description>
     <!-- If job splitting is enabled, break up the query file into parts -->
     <!-- Using 2000 per chunk so 4 threads each doing 500 is ideal -->
     <parallelism method="basic" split_inputs="fasta_file" split_mode="to_size" split_size="2000" merge_outputs="tabular_file"></parallelism>
     <command interpreter="python">
         promoter2.py "\$NSLOTS" $fasta_file $tabular_file
+        ##I want the number of threads to be a Galaxy config option...
         ##Set the number of threads in the runner entry in universe_wsgi.ini
         ##which (on SGE at least) will set the $NSLOTS environment variable.
-        ##If the environment variable isn't set, get "", and defaults to one.
+        ##If the environment variable isn't set, get "", and the python wrapper
+        ##defaults to four threads.
     </command>
     <stdio>
         <!-- Anything other than zero is an error -->
@@ -41,10 +43,14 @@
 
 The input is a FASTA file of nucleotide sequences (e.g. upstream regions of your genes), and the output is tabular with five columns (one row per promoter):
 
- 1. Sequence identifier (first word of FASTA header)
- 2. Promoter position, e.g. 600
- 3. Promoter score, e.g. 1.063
- 4. Promoter likelihood, e.g. Highly likely prediction
+====== ==================================================
+Column Description
+------ --------------------------------------------------
+     1 Sequence identifier (first word of FASTA header)
+     2 Promoter position, e.g. 600
+     3 Promoter score, e.g. 1.063
+     4 Promoter likelihood, e.g. Highly likely prediction
+====== ==================================================
 
 The scores are classified very simply as follows: