Mercurial > repos > bgruening > glimmer_knowledge_based
view glimmer_wo_icm.py @ 1:febc61f3c67d draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/glimmer commit a4b0969b33a68a0ea9ba12291f6694aec24f13ed
author | iuc |
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date | Tue, 30 Oct 2018 18:52:08 -0400 |
parents | 9b2e283dc3b5 |
children |
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#!/usr/bin/env python """ Input: DNA Fasta File Output: Tabular Return Tabular File with predicted ORF's Bjoern Gruening """ import os import shutil import subprocess import sys import tempfile from glimmer2seq import glimmer2seq def main(): genome_seq_file = sys.argv[1] outfile_classic_glimmer = sys.argv[2] outfile_ext_path = sys.argv[3] oufile_genes = sys.argv[8] tag = 'glimmer_non_knowlegde_based_prediction' tempdir = tempfile.gettempdir() trainingset = os.path.join(tempdir, tag + ".train") icm = os.path.join(tempdir, tag + ".icm") longorfs = tempfile.NamedTemporaryFile() trainingset = tempfile.NamedTemporaryFile() icm = tempfile.NamedTemporaryFile() # glimmeropts = "-o0 -g110 -t30 -l" glimmeropts = "-o%s -g%s -t%s" % (sys.argv[4], sys.argv[5], sys.argv[6]) if sys.argv[7] == "true": glimmeropts += " -l" """ 1. Find long, non-overlapping orfs to use as a training set """ subprocess.Popen(["long-orfs", "-n", "-t", "1.15", genome_seq_file, "-"], stdout=longorfs, stderr=subprocess.PIPE).communicate() """ 2. Extract the training sequences from the genome file """ subprocess.Popen(["extract", "-t", genome_seq_file, longorfs.name], stdout=trainingset, stderr=subprocess.PIPE).communicate() """ 3. Build the icm from the training sequences """ # the "-" parameter is used to redirect the output to stdout subprocess.Popen(["build-icm", "-r", "-"], stdin=open(trainingset.name), stdout=icm, stderr=subprocess.PIPE).communicate() """ Run Glimmer3 """ subprocess.Popen(["glimmer3", glimmeropts, genome_seq_file, icm.name, os.path.join(tempdir, tag)], stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate() if outfile_classic_glimmer.strip() != 'None': shutil.copyfile(os.path.join(tempdir, tag + ".predict"), outfile_classic_glimmer) if outfile_ext_path.strip() != 'None': shutil.copyfile(os.path.join(tempdir, tag + ".detail"), outfile_ext_path) glimmer2seq(os.path.join(tempdir, tag + ".predict"), genome_seq_file, oufile_genes) if __name__ == "__main__": main()