Mercurial > repos > peterjc > tmhmm_and_signalp
annotate tools/protein_analysis/signalp3.py @ 4:81caef04ce8b
Migrated tool version 0.0.7 from old tool shed archive to new tool shed repository
author | peterjc |
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date | Tue, 07 Jun 2011 18:05:50 -0400 |
parents | bca9bc7fdaef |
children | 0f1c61998b22 |
rev | line source |
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0
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1 #!/usr/bin/env python |
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2 """Wrapper for SignalP v3.0 for use in Galaxy. |
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3 |
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4 This script takes exactly fives command line arguments: |
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5 * the organism type (euk, gram+ or gram-) |
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6 * length to truncate sequences to (integer) |
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7 * number of threads to use (integer) |
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8 * an input protein FASTA filename |
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9 * output tabular filename. |
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10 |
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11 It then calls the standalone SignalP v3.0 program (not the webservice) |
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12 requesting the short output (one line per protein) using both NN and HMM |
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13 for predictions. |
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14 |
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15 First major feature is cleaning up the output. The raw output from SignalP |
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16 v3.0 looks like this (21 columns space separated): |
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17 |
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18 # SignalP-NN euk predictions # SignalP-HMM euk predictions |
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19 # name Cmax pos ? Ymax pos ? Smax pos ? Smean ? D ? # name ! Cmax pos ? Sprob ? |
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20 gi|2781234|pdb|1JLY| 0.061 17 N 0.043 17 N 0.199 1 N 0.067 N 0.055 N gi|2781234|pdb|1JLY|B Q 0.000 17 N 0.000 N |
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21 gi|4959044|gb|AAD342 0.099 191 N 0.012 38 N 0.023 12 N 0.014 N 0.013 N gi|4959044|gb|AAD34209.1|AF069992_1 Q 0.000 0 N 0.000 N |
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22 gi|671626|emb|CAA856 0.139 381 N 0.020 8 N 0.121 4 N 0.067 N 0.044 N gi|671626|emb|CAA85685.1| Q 0.000 0 N 0.000 N |
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23 gi|3298468|dbj|BAA31 0.208 24 N 0.184 38 N 0.980 32 Y 0.613 Y 0.398 N gi|3298468|dbj|BAA31520.1| Q 0.066 24 N 0.139 N |
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24 |
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25 In order to make it easier to use in Galaxy, this wrapper script reformats |
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26 this to use tab separators. Also it removes the redundant truncated name |
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27 column, and assigns unique column names in the header: |
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28 |
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29 #ID NN_Cmax_score NN_Cmax_pos NN_Cmax_pred NN_Ymax_score NN_Ymax_pos NN_Ymax_pred NN_Smax_score NN_Smax_pos NN_Smax_pred NN_Smean_score NN_Smean_pred NN_D_score NN_D_pred HMM_bang HMM_Cmax_score HMM_Cmax_pos HMM_Cmax_pred HMM_Sprob_score HMM_Sprob_pred |
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30 gi|2781234|pdb|1JLY|B 0.061 17 N 0.043 17 N 0.199 1 N 0.067 N 0.055 N Q 0.000 17 N 0.000 N |
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31 gi|4959044|gb|AAD34209.1|AF069992_1 0.099 191 N 0.012 38 N 0.023 12 N 0.014 N 0.013 N Q 0.000 0 N 0.000 N |
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32 gi|671626|emb|CAA85685.1| 0.139 381 N 0.020 8 N 0.121 4 N 0.067 N 0.044 N Q 0.000 0 N 0.000 N |
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33 gi|3298468|dbj|BAA31520.1| 0.208 24 N 0.184 38 N 0.980 32 Y 0.613 Y 0.398 N Q 0.066 24 N 0.139 N |
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34 |
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35 The second major feature is overcoming SignalP's built in limit of 4000 |
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36 sequences by breaking up the input FASTA file into chunks. This also allows |
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37 us to pre-trim the sequences since SignalP only needs their starts. |
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38 |
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39 The third major feature is taking advantage of multiple cores (since SignalP |
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40 v3.0 itself is single threaded) by using the individual FASTA input files to |
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41 run multiple copies of TMHMM in parallel. I would normally use Python's |
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42 multiprocessing library in this situation but it requires at least Python 2.6 |
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43 and at the time of writing Galaxy still supports Python 2.4. |
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44 """ |
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45 import sys |
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46 import os |
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47 from seq_analysis_utils import stop_err, split_fasta, run_jobs |
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48 |
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49 FASTA_CHUNK = 500 |
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50 MAX_LEN = 6000 #Found by trial and error |
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51 |
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52 if len(sys.argv) != 6: |
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53 stop_err("Require five arguments, organism, truncate, threads, input protein FASTA file & output tabular file") |
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54 |
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55 organism = sys.argv[1] |
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56 if organism not in ["euk", "gram+", "gram-"]: |
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57 stop_err("Organism argument %s is not one of euk, gram+ or gram-" % organism) |
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58 |
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59 try: |
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60 truncate = int(sys.argv[2]) |
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61 except: |
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62 truncate = 0 |
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63 if truncate < 0: |
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64 stop_err("Truncate argument %s is not a positive integer (or zero)" % sys.argv[2]) |
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65 |
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66 try: |
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67 num_threads = int(sys.argv[3]) |
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68 except: |
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69 num_threads = 0 |
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70 if num_threads < 1: |
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71 stop_err("Threads argument %s is not a positive integer" % sys.argv[3]) |
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72 |
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73 fasta_file = sys.argv[4] |
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74 |
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75 tabular_file = sys.argv[5] |
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76 |
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77 def clean_tabular(raw_handle, out_handle): |
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78 """Clean up SignalP output to make it tabular.""" |
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79 for line in raw_handle: |
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80 if not line or line.startswith("#"): |
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81 continue |
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82 parts = line.rstrip("\r\n").split() |
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83 assert len(parts)==21, repr(line) |
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84 assert parts[14].startswith(parts[0]) |
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85 #Remove redundant truncated name column (col 0) |
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86 #and put full name at start (col 14) |
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87 parts = parts[14:15] + parts[1:14] + parts[15:] |
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88 out_handle.write("\t".join(parts) + "\n") |
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89 |
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90 fasta_files = split_fasta(fasta_file, tabular_file, n=FASTA_CHUNK, truncate=truncate, max_len=MAX_LEN) |
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91 temp_files = [f+".out" for f in fasta_files] |
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92 assert len(fasta_files) == len(temp_files) |
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93 jobs = ["signalp -short -t %s %s > %s" % (organism, fasta, temp) |
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94 for (fasta, temp) in zip(fasta_files, temp_files)] |
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95 assert len(fasta_files) == len(temp_files) == len(jobs) |
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96 |
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97 def clean_up(file_list): |
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98 for f in file_list: |
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99 if os.path.isfile(f): |
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100 os.remove(f) |
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101 |
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102 if len(jobs) > 1 and num_threads > 1: |
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103 #A small "info" message for Galaxy to show the user. |
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104 print "Using %i threads for %i tasks" % (min(num_threads, len(jobs)), len(jobs)) |
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105 results = run_jobs(jobs, num_threads) |
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106 assert len(fasta_files) == len(temp_files) == len(jobs) |
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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 try: |
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110 output = open(temp).readline() |
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111 except IOError: |
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112 output = "" |
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113 if error_level or output.lower().startswith("error running"): |
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114 clean_up(fasta_files) |
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115 clean_up(temp_files) |
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116 stop_err("One or more tasks failed, e.g. %i from %r gave:\n%s" % (error_level, cmd, output), |
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117 error_level) |
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118 del results |
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119 |
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120 out_handle = open(tabular_file, "w") |
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121 fields = ["ID"] |
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122 #NN results: |
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123 for name in ["Cmax", "Ymax", "Smax"]: |
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124 fields.extend(["NN_%s_score"%name, "NN_%s_pos"%name, "NN_%s_pred"%name]) |
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125 fields.extend(["NN_Smean_score", "NN_Smean_pred", "NN_D_score", "NN_D_pred"]) |
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126 #HMM results: |
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127 fields.extend(["HMM_type", "HMM_Cmax_score", "HMM_Cmax_pos", "HMM_Cmax_pred", |
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128 "HMM_Sprob_score", "HMM_Sprob_pred"]) |
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129 out_handle.write("#" + "\t".join(fields) + "\n") |
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130 for temp in temp_files: |
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131 data_handle = open(temp) |
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132 clean_tabular(data_handle, out_handle) |
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133 data_handle.close() |
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134 out_handle.close() |
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135 |
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136 clean_up(fasta_files) |
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137 clean_up(temp_files) |