annotate tools/protein_analysis/tmhmm2.py @ 13:c6e5a737d3a0 draft

Uploaded v0.2.2, was missing some test files
author peterjc
date Mon, 22 Apr 2013 11:46:20 -0400
parents 391a142c1e60
children 20139cb4c844
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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 three command line arguments - number of threads,
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5 an input protein FASTA filename, and an output tabular filename. It then
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6 calls the standalone TMHMM v2.0 program (not the webservice) requesting
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7 the short output (one line per 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
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14 gi|671626|emb|CAA85685.1| len=473 ExpAA=0.19 First60=0.00 PredHel=0 Topology=o
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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 Note that this is somewhat redundant with job-splitting available in Galaxy
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37 itself (see the SignalP XML file for settings).
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38
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39 Also tmhmm2 can fail without returning an error code, for example if run on a
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40 64 bit machine with only the 32 bit binaries installed. This script will spot
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41 when there is no output from tmhmm2, and raise an error.
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42 """
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43 import sys
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44 import os
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45 import tempfile
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46 from seq_analysis_utils import stop_err, split_fasta, run_jobs, thread_count
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47
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48 FASTA_CHUNK = 500
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49
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50 if len(sys.argv) != 4:
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51 stop_err("Require three arguments, number of threads (int), input protein FASTA file & output tabular file")
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52
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53 num_threads = thread_count(sys.argv[1], default=4)
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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 tmp_dir = tempfile.mkdtemp()
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58
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59 def clean_tabular(raw_handle, out_handle):
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60 """Clean up tabular TMHMM output, returns output line count."""
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61 count = 0
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62 for line in raw_handle:
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63 if not line.strip() or line.startswith("#"):
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64 #Ignore any blank lines or comment lines
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65 continue
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66 parts = line.rstrip("\r\n").split("\t")
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67 try:
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68 identifier, length, expAA, first60, predhel, topology = parts
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69 except:
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70 assert len(parts)!=6
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71 stop_err("Bad line: %r" % line)
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72 assert length.startswith("len="), line
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73 length = length[4:]
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74 assert expAA.startswith("ExpAA="), line
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75 expAA = expAA[6:]
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76 assert first60.startswith("First60="), line
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77 first60 = first60[8:]
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78 assert predhel.startswith("PredHel="), line
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79 predhel = predhel[8:]
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80 assert topology.startswith("Topology="), line
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81 topology = topology[9:]
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82 out_handle.write("%s\t%s\t%s\t%s\t%s\t%s\n" \
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83 % (identifier, length, expAA, first60, predhel, topology))
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84 count += 1
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85 return count
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86
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87 #Note that if the input FASTA file contains no sequences,
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88 #split_fasta returns an empty list (i.e. zero temp files).
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89 fasta_files = split_fasta(fasta_file, os.path.join(tmp_dir, "tmhmm"), FASTA_CHUNK)
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90 temp_files = [f+".out" for f in fasta_files]
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91 jobs = ["tmhmm -short %s > %s" % (fasta, temp)
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92 for fasta, temp in zip(fasta_files, temp_files)]
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93
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94 def clean_up(file_list):
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95 for f in file_list:
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96 if os.path.isfile(f):
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97 os.remove(f)
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98 try:
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99 os.rmdir(tmp_dir)
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100 except:
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101 pass
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102
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103 if len(jobs) > 1 and num_threads > 1:
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104 #A small "info" message for Galaxy to show the user.
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105 print "Using %i threads for %i tasks" % (min(num_threads, len(jobs)), len(jobs))
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106 results = run_jobs(jobs, num_threads)
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107 for fasta, temp, cmd in zip(fasta_files, temp_files, jobs):
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108 error_level = results[cmd]
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109 if error_level:
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110 try:
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111 output = open(temp).readline()
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112 except IOError:
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113 output = ""
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114 clean_up(fasta_files + temp_files)
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115 stop_err("One or more tasks failed, e.g. %i from %r gave:\n%s" % (error_level, cmd, output),
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116 error_level)
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117 del results
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118 del jobs
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119
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120 out_handle = open(tabular_file, "w")
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121 out_handle.write("#ID\tlen\tExpAA\tFirst60\tPredHel\tTopology\n")
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122 for temp in temp_files:
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123 data_handle = open(temp)
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124 count = clean_tabular(data_handle, out_handle)
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125 data_handle.close()
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126 if not count:
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127 clean_up(fasta_files + temp_files)
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128 stop_err("No output from tmhmm2")
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129 out_handle.close()
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130
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131 clean_up(fasta_files + temp_files)