Mercurial > repos > peterjc > tmhmm_and_signalp
annotate tools/protein_analysis/signalp3.py @ 30:6d9d7cdf00fc draft
v0.2.11 Job splitting fast-fail; RXLR tools supports HMMER2 from BioConda; Capture more version information; misc internal changes
author | peterjc |
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date | Thu, 21 Sep 2017 11:15:55 -0400 |
parents | 3cb02adf4326 |
children | 20da7f48b56f |
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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) |
7 | 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 |
7 | 11 There are two further optional arguments |
12 * cut type (NN_Cmax, NN_Ymax, NN_Smax or HMM_Cmax) | |
13 * output GFF3 filename | |
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 ? |
29 | 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. |
7 | 48 |
49 Note that this is somewhat redundant with job-splitting available in Galaxy | |
50 itself (see the SignalP XML file for settings). | |
51 | |
52 Finally, you can opt to have a GFF3 file produced which will describe the | |
53 predicted signal peptide and mature peptide for each protein (using one of | |
54 the predictors which gives a cleavage site). *WORK IN PROGRESS* | |
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55 """ # noqa: E501 |
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56 |
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57 from __future__ import print_function |
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58 |
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59 import os |
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60 import sys |
7 | 61 import tempfile |
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62 |
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63 from seq_analysis_utils import fasta_iterator, split_fasta |
7 | 64 from seq_analysis_utils import run_jobs, thread_count |
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65 |
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66 FASTA_CHUNK = 500 |
29 | 67 MAX_LEN = 6000 # Found by trial and error |
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68 |
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69 if "-v" in sys.argv or "--version" in sys.argv: |
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70 print("SignalP Galaxy wrapper version 0.0.19") |
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71 sys.exit(os.system("signalp -version")) |
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72 |
29 | 73 if len(sys.argv) not in [6, 8]: |
74 sys.exit("Require five (or 7) arguments, organism, truncate, threads, " | |
8 | 75 "input protein FASTA file & output tabular file (plus " |
76 "optionally cut method and GFF3 output file). " | |
29 | 77 "Got %i arguments." % (len(sys.argv) - 1)) |
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78 |
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79 organism = sys.argv[1] |
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80 if organism not in ["euk", "gram+", "gram-"]: |
29 | 81 sys.exit("Organism argument %s is not one of euk, gram+ or gram-" % organism) |
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82 |
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83 try: |
8 | 84 truncate = int(sys.argv[2]) |
29 | 85 except ValueError: |
8 | 86 truncate = 0 |
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87 if truncate < 0: |
29 | 88 sys.exit("Truncate argument %s is not a positive integer (or zero)" % sys.argv[2]) |
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89 |
7 | 90 num_threads = thread_count(sys.argv[3], default=4) |
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91 fasta_file = sys.argv[4] |
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92 tabular_file = sys.argv[5] |
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93 |
7 | 94 if len(sys.argv) == 8: |
8 | 95 cut_method = sys.argv[6] |
96 if cut_method not in ["NN_Cmax", "NN_Ymax", "NN_Smax", "HMM_Cmax"]: | |
29 | 97 sys.exit("Invalid cut method %r" % cut_method) |
8 | 98 gff3_file = sys.argv[7] |
7 | 99 else: |
8 | 100 cut_method = None |
101 gff3_file = None | |
7 | 102 |
103 | |
104 tmp_dir = tempfile.mkdtemp() | |
105 | |
29 | 106 |
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107 def clean_tabular(raw_handle, out_handle, gff_handle=None): |
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108 """Clean up SignalP output to make it tabular.""" |
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109 for line in raw_handle: |
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110 if not line or line.startswith("#"): |
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111 continue |
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112 parts = line.rstrip("\r\n").split() |
29 | 113 assert len(parts) == 21, repr(line) |
8 | 114 assert parts[14].startswith(parts[0]), \ |
115 "Bad entry in SignalP output, ID miss-match:\n%r" % line | |
29 | 116 # Remove redundant truncated name column (col 0) |
117 # and put full name at start (col 14) | |
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118 parts = parts[14:15] + parts[1:14] + parts[15:] |
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119 out_handle.write("\t".join(parts) + "\n") |
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120 |
29 | 121 |
7 | 122 def make_gff(fasta_file, tabular_file, gff_file, cut_method): |
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123 """Make a GFF file.""" |
29 | 124 cut_col, score_col = {"NN_Cmax": (2, 1), |
125 "NN_Ymax": (5, 4), | |
126 "NN_Smax": (8, 7), | |
127 "HMM_Cmax": (16, 15), | |
7 | 128 }[cut_method] |
129 | |
130 source = "SignalP" | |
29 | 131 strand = "." # not stranded |
132 phase = "." # not phased | |
7 | 133 tags = "Note=%s" % cut_method |
29 | 134 |
7 | 135 tab_handle = open(tabular_file) |
136 line = tab_handle.readline() | |
137 assert line.startswith("#ID\t"), line | |
138 | |
139 gff_handle = open(gff_file, "w") | |
140 gff_handle.write("##gff-version 3\n") | |
141 | |
142 for (title, seq), line in zip(fasta_iterator(fasta_file), tab_handle): | |
143 parts = line.rstrip("\n").split("\t") | |
144 seqid = parts[0] | |
145 assert title.startswith(seqid), "%s vs %s" % (seqid, title) | |
29 | 146 if not seq: |
147 # Is it possible to have a zero length reference in GFF3? | |
7 | 148 continue |
149 cut = int(parts[cut_col]) | |
150 if cut == 0: | |
151 assert cut_method == "HMM_Cmax", cut_method | |
29 | 152 # TODO - Why does it do this? |
7 | 153 cut = 1 |
154 assert 1 <= cut <= len(seq), "%i for %s len %i" % (cut, seqid, len(seq)) | |
155 score = parts[score_col] | |
29 | 156 gff_handle.write("##sequence-region %s %i %i\n" |
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157 % (seqid, 1, len(seq))) |
29 | 158 # If the cut is at the very begining, there is no signal peptide! |
7 | 159 if cut > 1: |
29 | 160 # signal_peptide = SO:0000418 |
161 gff_handle.write("%s\t%s\t%s\t%i\t%i\t%s\t%s\t%s\t%s\n" | |
7 | 162 % (seqid, source, |
29 | 163 "signal_peptide", 1, cut - 1, |
7 | 164 score, strand, phase, tags)) |
29 | 165 # mature_protein_region = SO:0000419 |
166 gff_handle.write("%s\t%s\t%s\t%i\t%i\t%s\t%s\t%s\t%s\n" | |
7 | 167 % (seqid, source, |
168 "mature_protein_region", cut, len(seq), | |
169 score, strand, phase, tags)) | |
29 | 170 tab_handle.close() |
7 | 171 gff_handle.close() |
172 | |
173 | |
174 fasta_files = split_fasta(fasta_file, os.path.join(tmp_dir, "signalp"), | |
175 n=FASTA_CHUNK, truncate=truncate, max_len=MAX_LEN) | |
29 | 176 temp_files = [f + ".out" for f in fasta_files] |
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177 assert len(fasta_files) == len(temp_files) |
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178 jobs = ["signalp -short -t %s %s > %s" % (organism, fasta, temp) |
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179 for (fasta, temp) in zip(fasta_files, temp_files)] |
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180 assert len(fasta_files) == len(temp_files) == len(jobs) |
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181 |
29 | 182 |
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183 def clean_up(file_list): |
29 | 184 """Remove temp files, and if possible the temp directory.""" |
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185 for f in file_list: |
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186 if os.path.isfile(f): |
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187 os.remove(f) |
7 | 188 try: |
189 os.rmdir(tmp_dir) | |
29 | 190 except Exception: |
7 | 191 pass |
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192 |
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193 |
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194 if len(jobs) > 1 and num_threads > 1: |
29 | 195 # A small "info" message for Galaxy to show the user. |
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196 print("Using %i threads for %i tasks" % (min(num_threads, len(jobs)), len(jobs))) |
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197 results = run_jobs(jobs, num_threads) |
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198 assert len(fasta_files) == len(temp_files) == len(jobs) |
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199 for fasta, temp, cmd in zip(fasta_files, temp_files, jobs): |
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200 error_level = results[cmd] |
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201 try: |
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202 output = open(temp).readline() |
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203 except IOError: |
7 | 204 output = "(no output)" |
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205 if error_level or output.lower().startswith("error running"): |
7 | 206 clean_up(fasta_files + temp_files) |
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207 if output: |
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208 sys.stderr.write("One or more tasks failed, e.g. %i from %r gave:\n%s" % (error_level, cmd, output)) |
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209 else: |
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210 sys.stderr.write("One or more tasks failed, e.g. %i from %r with no output\n" % (error_level, cmd)) |
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211 sys.exit(error_level) |
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212 del results |
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213 |
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214 out_handle = open(tabular_file, "w") |
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215 fields = ["ID"] |
29 | 216 # NN results: |
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217 for name in ["Cmax", "Ymax", "Smax"]: |
29 | 218 fields.extend(["NN_%s_score" % name, "NN_%s_pos" % name, "NN_%s_pred" % name]) |
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219 fields.extend(["NN_Smean_score", "NN_Smean_pred", "NN_D_score", "NN_D_pred"]) |
29 | 220 # HMM results: |
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221 fields.extend(["HMM_type", "HMM_Cmax_score", "HMM_Cmax_pos", "HMM_Cmax_pred", |
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222 "HMM_Sprob_score", "HMM_Sprob_pred"]) |
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223 out_handle.write("#" + "\t".join(fields) + "\n") |
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224 for temp in temp_files: |
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225 data_handle = open(temp) |
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226 clean_tabular(data_handle, out_handle) |
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227 data_handle.close() |
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228 out_handle.close() |
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229 |
29 | 230 # GFF3: |
7 | 231 if cut_method: |
8 | 232 make_gff(fasta_file, tabular_file, gff3_file, cut_method) |
7 | 233 |
234 clean_up(fasta_files + temp_files) |