Mercurial > repos > peterjc > tmhmm_and_signalp

annotate tools/protein_analysis/tmhmm2.py @ 4:81caef04ce8b

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Migrated tool version 0.0.7 from old tool shed archive to new tool shed repository
author peterjc
date Tue, 07 Jun 2011 18:05:50 -0400
parents 6901298ac16c
children 9b45a8743100
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1 #!/usr/bin/env python
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2 """Wrapper for TMHMM v2.0 for use in Galaxy.
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3
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4 This script takes exactly two command line arguments - an input protein FASTA
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5 filename and an output tabular filename. It then calls the standalone TMHMM
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6 v2.0 program (not the webservice) requesting the short output (one line per
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7 protein).
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8
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9 The first major feature is cleaning up the tabular output. The short form raw
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10 output from TMHMM v2.0 looks like this (six columns tab separated):
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12 gi|2781234|pdb|1JLY|B len=304 ExpAA=0.01 First60=0.00 PredHel=0 Topology=o
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13 gi|4959044|gb|AAD34209.1|AF069992_1 len=600 ExpAA=0.00 First60=0.00 PredHel=0 Topology=o
bca9bc7fdaef Migrated tool version 0.0.1 from old tool shed archive to new tool shed repository
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14 gi|671626|emb|CAA85685.1| len=473 ExpAA=0.19 First60=0.00 PredHel=0 Topology=o
bca9bc7fdaef Migrated tool version 0.0.1 from old tool shed archive to new tool shed repository
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15 gi|3298468|dbj|BAA31520.1| len=107 ExpAA=59.37 First60=31.17 PredHel=3 Topology=o23-45i52-74o89-106i
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16
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17 If there are any additional 'comment' lines starting with the hash (#)
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18 character these are ignored by this script.
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19
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20 In order to make it easier to use in Galaxy, this wrapper script simplifies
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21 this to remove the redundant tags, and instead adds a comment line at the
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22 top with the column names:
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23
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24 #ID len ExpAA First60 PredHel Topology
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25 gi|2781234|pdb|1JLY|B 304 0.01 60 0.00 0 o
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26 gi|4959044|gb|AAD34209.1|AF069992_1 600 0.00 0 0.00 0 o
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27 gi|671626|emb|CAA85685.1| 473 0.19 0.00 0 o
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28 gi|3298468|dbj|BAA31520.1| 107 59.37 31.17 3 o23-45i52-74o89-106i
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29
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30 The second major potential feature is taking advantage of multiple cores
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31 (since TMHMM v2.0 itself is single threaded) by dividing the input FASTA file
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32 into chunks and running multiple copies of TMHMM in parallel. I would normally
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33 use Python's multiprocessing library in this situation but it requires at
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34 least Python 2.6 and at the time of writing Galaxy still supports Python 2.4.
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35
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36 Also tmhmm2 can fail without returning an error code, for example if run on a
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37 64 bit machine with only the 32 bit binaries installed. This script will spot
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38 when there is no output from tmhmm2, and raise an error.
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39 """
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40 import sys
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41 import os
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42 from seq_analysis_utils import stop_err, split_fasta, run_jobs
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43
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44 FASTA_CHUNK = 500
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45
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46 if len(sys.argv) != 4:
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47 stop_err("Require three arguments, number of threads (int), input protein FASTA file & output tabular file")
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48 try:
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49 num_threads = int(sys.argv[1])
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50 except:
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51 num_threads = 0
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52 if num_threads < 1:
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53 stop_err("Threads argument %s is not a positive integer" % sys.argv[1])
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54 fasta_file = sys.argv[2]
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55 tabular_file = sys.argv[3]
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56
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57 def clean_tabular(raw_handle, out_handle):
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58 """Clean up tabular TMHMM output, returns output line count."""
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59 count = 0
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60 for line in raw_handle:
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61 if not line.strip() or line.startswith("#"):
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62 #Ignore any blank lines or comment lines
0
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63 continue
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64 parts = line.rstrip("\r\n").split("\t")
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65 try:
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66 identifier, length, expAA, first60, predhel, topology = parts
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67 except:
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68 assert len(parts)!=6
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69 stop_err("Bad line: %r" % line)
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70 assert length.startswith("len="), line
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71 length = length[4:]
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72 assert expAA.startswith("ExpAA="), line
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73 expAA = expAA[6:]
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74 assert first60.startswith("First60="), line
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75 first60 = first60[8:]
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76 assert predhel.startswith("PredHel="), line
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77 predhel = predhel[8:]
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78 assert topology.startswith("Topology="), line
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79 topology = topology[9:]
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80 out_handle.write("%s\t%s\t%s\t%s\t%s\t%s\n" \
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81 % (identifier, length, expAA, first60, predhel, topology))
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82 count += 1
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83 return count
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84
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85 #Note that if the input FASTA file contains no sequences,
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86 #split_fasta returns an empty list (i.e. zero temp files).
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87 fasta_files = split_fasta(fasta_file, tabular_file, FASTA_CHUNK)
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88 temp_files = [f+".out" for f in fasta_files]
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89 jobs = ["tmhmm -short %s > %s" % (fasta, temp)
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90 for fasta, temp in zip(fasta_files, temp_files)]
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91
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92 def clean_up(file_list):
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93 for f in file_list:
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94 if os.path.isfile(f):
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95 os.remove(f)
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96
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97 if len(jobs) > 1 and num_threads > 1:
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98 #A small "info" message for Galaxy to show the user.
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99 print "Using %i threads for %i tasks" % (min(num_threads, len(jobs)), len(jobs))
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100 results = run_jobs(jobs, num_threads)
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101 for fasta, temp, cmd in zip(fasta_files, temp_files, jobs):
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102 error_level = results[cmd]
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103 if error_level:
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104 try:
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105 output = open(temp).readline()
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106 except IOError:
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107 output = ""
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108 clean_up(fasta_files)
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109 clean_up(temp_files)
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110 stop_err("One or more tasks failed, e.g. %i from %r gave:\n%s" % (error_level, cmd, output),
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111 error_level)
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112 del results
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113 del jobs
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114
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115 out_handle = open(tabular_file, "w")
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116 out_handle.write("#ID\tlen\tExpAA\tFirst60\tPredHel\tTopology\n")
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117 for temp in temp_files:
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118 data_handle = open(temp)
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119 count = clean_tabular(data_handle, out_handle)
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120 data_handle.close()
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121 if not count:
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122 clean_up(fasta_files)
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123 clean_up(temp_files)
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124 stop_err("No output from tmhmm2")
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125 out_handle.close()
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126
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127 clean_up(fasta_files)
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128 clean_up(temp_files)