Mercurial > repos > peterjc > tmhmm_and_signalp
annotate tools/protein_analysis/wolf_psort.py @ 9:e52220a9ddad draft
Uploaded v0.1.2
Use the new <stdio> settings in the XML wrappers to catch errors.
Obeys SGE style XNSLOTS environment variable for thread count (otherwise default to 4).
author | peterjc |
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date | Fri, 25 Jan 2013 06:08:31 -0500 |
parents | a290c6d4e658 |
children | 99b82a2b1272 |
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1 #!/usr/bin/env python |
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2 """Wrapper for WoLF PSORT v0.2 for use in Galaxy. |
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3 |
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4 This script takes exactly four command line arguments: |
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5 * the organism type (animal, plant or fungi) |
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6 * number of threads to use (integer) |
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7 * an input protein FASTA filename |
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8 * output tabular filename. |
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9 |
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10 It then calls the standalone WoLF PSORT v0.2 program runWolfPsortSummary |
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11 (not the webservice), and coverts the output from something like this: |
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12 |
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13 # k used for kNN is: 27 |
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14 gi|301087619|ref|XP_002894699.1| extr 12, mito 4, E.R. 3, golg 3, mito_nucl 3 |
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15 gi|301087623|ref|XP_002894700.1| extr 21, mito 2, cyto 2, cyto_mito 2 |
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16 |
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17 In order to make it easier to use in Galaxy, this wrapper script reformats |
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18 this to use tab separators, with one line per compartment prediction: |
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19 |
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20 #ID Compartment Score Rank |
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21 gi|301087619|ref|XP_002894699.1| extr 12 1 |
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22 gi|301087619|ref|XP_002894699.1| mito 4 2 |
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23 gi|301087619|ref|XP_002894699.1| E.R. 3 3 |
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24 gi|301087619|ref|XP_002894699.1| golg 3 4 |
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25 gi|301087619|ref|XP_002894699.1| mito_nucl 3 5 |
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26 gi|301087623|ref|XP_002894700.1| extr 21 1 |
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27 gi|301087623|ref|XP_002894700.1| mito 2 2 |
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28 gi|301087623|ref|XP_002894700.1| cyto 2 3 |
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29 gi|301087623|ref|XP_002894700.1| cyto_mito 2 4 |
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30 |
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31 Additionally in order to take full advantage of multiple cores, by subdividing |
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32 the input FASTA file multiple copies of WoLF PSORT are run in parallel. I would |
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33 normally use Python's multiprocessing library in this situation but it requires |
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34 at 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 import sys |
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37 import os |
9 | 38 from seq_analysis_utils import stop_err, split_fasta, run_jobs, thread_count |
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39 |
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40 FASTA_CHUNK = 500 |
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41 exe = "runWolfPsortSummary" |
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42 |
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43 """ |
9 | 44 Note: I had trouble getting runWolfPsortSummary on the path (via a link), other |
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45 than by including all of /opt/WoLFPSORT_package_v0.2/bin , so used a wrapper |
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46 python script called runWolfPsortSummary as follows: |
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47 |
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48 #!/usr/bin/env python |
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49 #Wrapper script to call WoLF PSORT from its own directory. |
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50 import os |
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51 import sys |
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52 import subprocess |
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53 saved_dir = os.path.abspath(os.curdir) |
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54 os.chdir("/opt/WoLFPSORT_package_v0.2/bin") |
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55 args = ["./runWolfPsortSummary"] + sys.argv[1:] |
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56 return_code = subprocess.call(args) |
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57 os.chdir(saved_dir) |
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58 sys.exit(return_code) |
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59 """ |
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60 |
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61 if len(sys.argv) != 5: |
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62 stop_err("Require four arguments, organism, threads, input protein FASTA file & output tabular file") |
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63 |
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64 organism = sys.argv[1] |
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65 if organism not in ["animal", "plant", "fungi"]: |
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66 stop_err("Organism argument %s is not one of animal, plant, fungi" % organism) |
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67 |
9 | 68 num_threads = thread_count(sys.argv[2], default=4) |
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69 fasta_file = sys.argv[3] |
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70 tabular_file = sys.argv[4] |
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71 |
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72 def clean_tabular(raw_handle, out_handle): |
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73 """Clean up WoLF PSORT output to make it tabular.""" |
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74 for line in raw_handle: |
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75 if not line or line.startswith("#"): |
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76 continue |
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77 name, data = line.rstrip("\r\n").split(None,1) |
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78 for rank, comp_data in enumerate(data.split(",")): |
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79 comp, score = comp_data.split() |
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80 out_handle.write("%s\t%s\t%s\t%i\n" \ |
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81 % (name, comp, score, rank+1)) |
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82 |
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83 fasta_files = split_fasta(fasta_file, tabular_file, n=FASTA_CHUNK) |
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84 temp_files = [f+".out" for f in fasta_files] |
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85 assert len(fasta_files) == len(temp_files) |
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86 jobs = ["%s %s < %s > %s" % (exe, organism, fasta, temp) |
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87 for (fasta, temp) in zip(fasta_files, temp_files)] |
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88 assert len(fasta_files) == len(temp_files) == len(jobs) |
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89 |
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90 def clean_up(file_list): |
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91 for f in file_list: |
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92 if os.path.isfile(f): |
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93 os.remove(f) |
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94 |
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95 if len(jobs) > 1 and num_threads > 1: |
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96 #A small "info" message for Galaxy to show the user. |
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97 print "Using %i threads for %i tasks" % (min(num_threads, len(jobs)), len(jobs)) |
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98 results = run_jobs(jobs, num_threads) |
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99 assert len(fasta_files) == len(temp_files) == len(jobs) |
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100 for fasta, temp, cmd in zip(fasta_files, temp_files, jobs): |
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101 error_level = results[cmd] |
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102 try: |
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103 output = open(temp).readline() |
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104 except IOError: |
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105 output = "" |
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106 if error_level or output.lower().startswith("error running"): |
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107 clean_up(fasta_files) |
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108 clean_up(temp_files) |
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109 stop_err("One or more tasks failed, e.g. %i from %r gave:\n%s" % (error_level, cmd, output), |
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110 error_level) |
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111 del results |
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112 |
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113 out_handle = open(tabular_file, "w") |
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114 out_handle.write("#ID\tCompartment\tScore\tRank\n") |
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115 for temp in temp_files: |
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116 data_handle = open(temp) |
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117 clean_tabular(data_handle, out_handle) |
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118 data_handle.close() |
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119 out_handle.close() |
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120 |
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121 clean_up(fasta_files) |
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122 clean_up(temp_files) |