Mercurial > repos > peterjc > tmhmm_and_signalp
annotate tools/protein_analysis/signalp3.py @ 19:f3ecd80850e2 draft
v0.2.9 Python style improvements
author | peterjc |
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date | Wed, 01 Feb 2017 09:46:42 -0500 |
parents | eb6ac44d4b8e |
children | a19b3ded8f33 |
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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 five 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, defaults to one) |
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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 There are two further optional arguments |
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12 * cut type (NN_Cmax, NN_Ymax, NN_Smax or HMM_Cmax) |
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13 * output GFF3 filename |
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14 |
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15 It then calls the standalone SignalP v3.0 program (not the webservice) |
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16 requesting the short output (one line per protein) using both NN and HMM |
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17 for predictions. |
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18 |
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19 First major feature is cleaning up the output. The raw output from SignalP |
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20 v3.0 looks like this (21 columns space separated): |
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21 |
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22 # SignalP-NN euk predictions # SignalP-HMM euk predictions |
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23 # name Cmax pos ? Ymax pos ? Smax pos ? Smean ? D ? # name ! Cmax pos ? Sprob ? |
19 | 24 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 |
25 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 | |
26 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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27 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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28 |
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29 In order to make it easier to use in Galaxy, this wrapper script reformats |
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30 this to use tab separators. Also it removes the redundant truncated name |
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31 column, and assigns unique column names in the header: |
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32 |
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33 #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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34 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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35 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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36 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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37 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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38 |
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39 The second major feature is overcoming SignalP's built in limit of 4000 |
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40 sequences by breaking up the input FASTA file into chunks. This also allows |
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41 us to pre-trim the sequences since SignalP only needs their starts. |
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42 |
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43 The third major feature is taking advantage of multiple cores (since SignalP |
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44 v3.0 itself is single threaded) by using the individual FASTA input files to |
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45 run multiple copies of TMHMM in parallel. I would normally use Python's |
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46 multiprocessing library in this situation but it requires at least Python 2.6 |
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47 and at the time of writing Galaxy still supports Python 2.4. |
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48 |
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49 Note that this is somewhat redundant with job-splitting available in Galaxy |
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50 itself (see the SignalP XML file for settings). |
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51 |
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52 Finally, you can opt to have a GFF3 file produced which will describe the |
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53 predicted signal peptide and mature peptide for each protein (using one of |
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54 the predictors which gives a cleavage site). *WORK IN PROGRESS* |
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55 """ |
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56 import sys |
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57 import os |
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58 import tempfile |
19 | 59 from seq_analysis_utils import split_fasta, fasta_iterator |
9 | 60 from seq_analysis_utils import run_jobs, thread_count |
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61 |
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62 FASTA_CHUNK = 500 |
19 | 63 MAX_LEN = 6000 # Found by trial and error |
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64 |
19 | 65 if len(sys.argv) not in [6, 8]: |
66 sys.exit("Require five (or 7) arguments, organism, truncate, threads, " | |
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67 "input protein FASTA file & output tabular file (plus " |
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68 "optionally cut method and GFF3 output file). " |
19 | 69 "Got %i arguments." % (len(sys.argv) - 1)) |
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70 |
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71 organism = sys.argv[1] |
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72 if organism not in ["euk", "gram+", "gram-"]: |
19 | 73 sys.exit("Organism argument %s is not one of euk, gram+ or gram-" % organism) |
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74 |
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75 try: |
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76 truncate = int(sys.argv[2]) |
19 | 77 except ValueError: |
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78 truncate = 0 |
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79 if truncate < 0: |
19 | 80 sys.exit("Truncate argument %s is not a positive integer (or zero)" % sys.argv[2]) |
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81 |
9 | 82 num_threads = thread_count(sys.argv[3], default=4) |
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83 fasta_file = sys.argv[4] |
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84 tabular_file = sys.argv[5] |
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85 |
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86 if len(sys.argv) == 8: |
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87 cut_method = sys.argv[6] |
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88 if cut_method not in ["NN_Cmax", "NN_Ymax", "NN_Smax", "HMM_Cmax"]: |
19 | 89 sys.exit("Invalid cut method %r" % cut_method) |
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90 gff3_file = sys.argv[7] |
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91 else: |
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92 cut_method = None |
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93 gff3_file = None |
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94 |
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95 |
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96 tmp_dir = tempfile.mkdtemp() |
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97 |
19 | 98 |
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99 def clean_tabular(raw_handle, out_handle, gff_handle=None, cut_method=None): |
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100 """Clean up SignalP output to make it tabular.""" |
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101 if cut_method: |
19 | 102 cut_col = {"NN_Cmax": 2, |
103 "NN_Ymax": 5, | |
104 "NN_Smax": 8, | |
105 "HMM_Cmax": 16}[cut_method] | |
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106 else: |
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107 cut_col = None |
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108 for line in raw_handle: |
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109 if not line or line.startswith("#"): |
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110 continue |
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111 parts = line.rstrip("\r\n").split() |
19 | 112 assert len(parts) == 21, repr(line) |
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113 assert parts[14].startswith(parts[0]), \ |
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114 "Bad entry in SignalP output, ID miss-match:\n%r" % line |
19 | 115 # Remove redundant truncated name column (col 0) |
116 # and put full name at start (col 14) | |
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117 parts = parts[14:15] + parts[1:14] + parts[15:] |
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118 out_handle.write("\t".join(parts) + "\n") |
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119 |
19 | 120 |
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121 def make_gff(fasta_file, tabular_file, gff_file, cut_method): |
19 | 122 cut_col, score_col = {"NN_Cmax": (2, 1), |
123 "NN_Ymax": (5, 4), | |
124 "NN_Smax": (8, 7), | |
125 "HMM_Cmax": (16, 15), | |
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126 }[cut_method] |
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127 |
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128 source = "SignalP" |
19 | 129 strand = "." # not stranded |
130 phase = "." # not phased | |
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131 tags = "Note=%s" % cut_method |
19 | 132 |
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133 tab_handle = open(tabular_file) |
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134 line = tab_handle.readline() |
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135 assert line.startswith("#ID\t"), line |
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136 |
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137 gff_handle = open(gff_file, "w") |
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138 gff_handle.write("##gff-version 3\n") |
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139 |
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140 for (title, seq), line in zip(fasta_iterator(fasta_file), tab_handle): |
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141 parts = line.rstrip("\n").split("\t") |
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142 seqid = parts[0] |
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143 assert title.startswith(seqid), "%s vs %s" % (seqid, title) |
19 | 144 if not seq: |
145 # Is it possible to have a zero length reference in GFF3? | |
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146 continue |
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147 cut = int(parts[cut_col]) |
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148 if cut == 0: |
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149 assert cut_method == "HMM_Cmax", cut_method |
19 | 150 # TODO - Why does it do this? |
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151 cut = 1 |
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152 assert 1 <= cut <= len(seq), "%i for %s len %i" % (cut, seqid, len(seq)) |
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153 score = parts[score_col] |
19 | 154 gff_handle.write("##sequence-region %s %i %i\n" |
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155 % (seqid, 1, len(seq))) |
19 | 156 # If the cut is at the very begining, there is no signal peptide! |
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157 if cut > 1: |
19 | 158 # signal_peptide = SO:0000418 |
159 gff_handle.write("%s\t%s\t%s\t%i\t%i\t%s\t%s\t%s\t%s\n" | |
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160 % (seqid, source, |
19 | 161 "signal_peptide", 1, cut - 1, |
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162 score, strand, phase, tags)) |
19 | 163 # mature_protein_region = SO:0000419 |
164 gff_handle.write("%s\t%s\t%s\t%i\t%i\t%s\t%s\t%s\t%s\n" | |
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165 % (seqid, source, |
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166 "mature_protein_region", cut, len(seq), |
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167 score, strand, phase, tags)) |
19 | 168 tab_handle.close() |
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169 gff_handle.close() |
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170 |
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171 |
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172 fasta_files = split_fasta(fasta_file, os.path.join(tmp_dir, "signalp"), |
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173 n=FASTA_CHUNK, truncate=truncate, max_len=MAX_LEN) |
19 | 174 temp_files = [f + ".out" for f in fasta_files] |
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175 assert len(fasta_files) == len(temp_files) |
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176 jobs = ["signalp -short -t %s %s > %s" % (organism, fasta, temp) |
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177 for (fasta, temp) in zip(fasta_files, temp_files)] |
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178 assert len(fasta_files) == len(temp_files) == len(jobs) |
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179 |
19 | 180 |
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181 def clean_up(file_list): |
19 | 182 """Remove temp files, and if possible the temp directory.""" |
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183 for f in file_list: |
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184 if os.path.isfile(f): |
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185 os.remove(f) |
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186 try: |
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187 os.rmdir(tmp_dir) |
19 | 188 except Exception: |
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189 pass |
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190 |
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191 if len(jobs) > 1 and num_threads > 1: |
19 | 192 # A small "info" message for Galaxy to show the user. |
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193 print "Using %i threads for %i tasks" % (min(num_threads, len(jobs)), len(jobs)) |
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194 results = run_jobs(jobs, num_threads) |
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195 assert len(fasta_files) == len(temp_files) == len(jobs) |
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196 for fasta, temp, cmd in zip(fasta_files, temp_files, jobs): |
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197 error_level = results[cmd] |
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198 try: |
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199 output = open(temp).readline() |
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200 except IOError: |
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201 output = "(no output)" |
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202 if error_level or output.lower().startswith("error running"): |
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203 clean_up(fasta_files + temp_files) |
19 | 204 sys.exit("One or more tasks failed, e.g. %i from %r gave:\n%s" % (error_level, cmd, output), |
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205 error_level) |
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206 del results |
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207 |
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208 out_handle = open(tabular_file, "w") |
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209 fields = ["ID"] |
19 | 210 # NN results: |
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211 for name in ["Cmax", "Ymax", "Smax"]: |
19 | 212 fields.extend(["NN_%s_score" % name, "NN_%s_pos" % name, "NN_%s_pred" % name]) |
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213 fields.extend(["NN_Smean_score", "NN_Smean_pred", "NN_D_score", "NN_D_pred"]) |
19 | 214 # HMM results: |
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215 fields.extend(["HMM_type", "HMM_Cmax_score", "HMM_Cmax_pos", "HMM_Cmax_pred", |
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216 "HMM_Sprob_score", "HMM_Sprob_pred"]) |
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217 out_handle.write("#" + "\t".join(fields) + "\n") |
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218 for temp in temp_files: |
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219 data_handle = open(temp) |
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220 clean_tabular(data_handle, out_handle) |
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221 data_handle.close() |
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222 out_handle.close() |
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223 |
19 | 224 # GFF3: |
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225 if cut_method: |
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226 make_gff(fasta_file, tabular_file, gff3_file, cut_method) |
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227 |
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228 clean_up(fasta_files + temp_files) |