Mercurial > repos > peterjc > tmhmm_and_signalp

annotate tools/protein_analysis/signalp3.py @ 20:a19b3ded8f33 draft

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v0.2.11 Job splitting fast-fail; RXLR tools supports HMMER2 from BioConda; Capture more version information; misc internal changes
author peterjc
date Thu, 21 Sep 2017 11:35:20 -0400
parents f3ecd80850e2
children 238eae32483c
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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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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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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 ?
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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
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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
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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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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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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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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 """ # 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
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61 import tempfile
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63 from seq_analysis_utils import fasta_iterator, split_fasta
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64 from seq_analysis_utils import run_jobs, thread_count
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66 FASTA_CHUNK = 500
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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
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73 if len(sys.argv) not in [6, 8]:
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74 sys.exit("Require five (or 7) arguments, organism, truncate, threads, "
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75 "input protein FASTA file & output tabular file (plus "
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76 "optionally cut method and GFF3 output file). "
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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-"]:
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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:
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84 truncate = int(sys.argv[2])
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85 except ValueError:
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86 truncate = 0
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87 if truncate < 0:
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88 sys.exit("Truncate argument %s is not a positive integer (or zero)" % sys.argv[2])
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89
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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
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94 if len(sys.argv) == 8:
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95 cut_method = sys.argv[6]
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96 if cut_method not in ["NN_Cmax", "NN_Ymax", "NN_Smax", "HMM_Cmax"]:
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97 sys.exit("Invalid cut method %r" % cut_method)
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98 gff3_file = sys.argv[7]
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99 else:
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100 cut_method = None
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101 gff3_file = None
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102
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103
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104 tmp_dir = tempfile.mkdtemp()
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105
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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()
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113 assert len(parts) == 21, repr(line)
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114 assert parts[14].startswith(parts[0]), \
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115 "Bad entry in SignalP output, ID miss-match:\n%r" % line
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116 # Remove redundant truncated name column (col 0)
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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
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121
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122 def make_gff(fasta_file, tabular_file, gff_file, cut_method):
20
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123 """Make a GFF file."""
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124 cut_col, score_col = {"NN_Cmax": (2, 1),
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125 "NN_Ymax": (5, 4),
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126 "NN_Smax": (8, 7),
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127 "HMM_Cmax": (16, 15),
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128 }[cut_method]
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129
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130 source = "SignalP"
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131 strand = "." # not stranded
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132 phase = "." # not phased
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133 tags = "Note=%s" % cut_method
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134
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135 tab_handle = open(tabular_file)
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136 line = tab_handle.readline()
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137 assert line.startswith("#ID\t"), line
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138
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139 gff_handle = open(gff_file, "w")
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140 gff_handle.write("##gff-version 3\n")
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141
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142 for (title, seq), line in zip(fasta_iterator(fasta_file), tab_handle):
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143 parts = line.rstrip("\n").split("\t")
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144 seqid = parts[0]
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145 assert title.startswith(seqid), "%s vs %s" % (seqid, title)
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146 if not seq:
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147 # Is it possible to have a zero length reference in GFF3?
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148 continue
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149 cut = int(parts[cut_col])
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150 if cut == 0:
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151 assert cut_method == "HMM_Cmax", cut_method
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152 # TODO - Why does it do this?
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153 cut = 1
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154 assert 1 <= cut <= len(seq), "%i for %s len %i" % (cut, seqid, len(seq))
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155 score = parts[score_col]
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156 gff_handle.write("##sequence-region %s %i %i\n"
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157 % (seqid, 1, len(seq)))
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158 # If the cut is at the very begining, there is no signal peptide!
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159 if cut > 1:
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160 # signal_peptide = SO:0000418
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161 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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162 % (seqid, source,
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163 "signal_peptide", 1, cut - 1,
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164 score, strand, phase, tags))
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165 # mature_protein_region = SO:0000419
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166 gff_handle.write("%s\t%s\t%s\t%i\t%i\t%s\t%s\t%s\t%s\n"
7
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167 % (seqid, source,
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168 "mature_protein_region", cut, len(seq),
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169 score, strand, phase, tags))
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170 tab_handle.close()
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171 gff_handle.close()
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172
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173
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174 fasta_files = split_fasta(fasta_file, os.path.join(tmp_dir, "signalp"),
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175 n=FASTA_CHUNK, truncate=truncate, max_len=MAX_LEN)
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176 temp_files = [f + ".out" for f in fasta_files]
0
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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
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182
0
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183 def clean_up(file_list):
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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)
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188 try:
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189 os.rmdir(tmp_dir)
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190 except Exception:
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191 pass
0
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192
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193
0
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194 if len(jobs) > 1 and num_threads > 1:
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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)))
0
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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:
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204 output = "(no output)"
0
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205 if error_level or output.lower().startswith("error running"):
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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)
0
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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"]
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216 # NN results:
0
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217 for name in ["Cmax", "Ymax", "Smax"]:
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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"])
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220 # HMM results:
0
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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
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230 # GFF3:
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231 if cut_method:
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232 make_gff(fasta_file, tabular_file, gff3_file, cut_method)
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233
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234 clean_up(fasta_files + temp_files)