Mercurial > repos > peterjc > tmhmm_and_signalp
annotate tools/protein_analysis/rxlr_motifs.py @ 11:99b82a2b1272 draft
Uploaded v0.2.0 which added PSORTb wrapper (written with Konrad Paszkiewicz)
author | peterjc |
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date | Wed, 03 Apr 2013 10:49:10 -0400 |
parents | a290c6d4e658 |
children | 7de64c8b258d |
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1 #!/usr/bin/env python |
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2 """Implements assorted RXLR motif methods from the literature |
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3 |
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4 This script takes exactly four command line arguments: |
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5 * Protein FASTA filename |
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6 * Number of threads |
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7 * Model name (Bhattacharjee2006, Win2007, Whisson2007) |
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8 * Output tabular filename |
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9 |
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10 The model names are: |
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11 |
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12 Bhattacharjee2006: Simple regular expression search for RXLR |
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13 with additional requirements for positioning and signal peptide. |
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14 |
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15 Win2007: Simple regular expression search for RXLR, but with |
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16 different positional requirements. |
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17 |
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18 Whisson2007: As Bhattacharjee2006 but with a more complex regular |
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19 expression to look for RXLR-EER domain, and additionally calls HMMER. |
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20 |
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21 See the help text in the accompanying Galaxy tool XML file for more |
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22 details including the full references. |
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23 |
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24 Note: |
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25 |
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26 Bhattacharjee et al. (2006) and Win et al. (2007) used SignalP v2.0, |
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27 which is no longer available. The current release is SignalP v3.0 |
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28 (Mar 5, 2007). We have therefore opted to use the NN Ymax position for |
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29 the predicted cleavage site, as this is expected to be more accurate. |
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30 Also note that the HMM score values have changed from v2.0 to v3.0. |
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31 Whisson et al. (2007) used SignalP v3.0 anyway. |
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32 |
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33 Whisson et al. (2007) used HMMER 2.3.2, and althought their HMM model |
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34 can still be used with hmmsearch from HMMER 3 this this does give |
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35 slightly different results. We expect the hmmsearch from HMMER 2.3.2 |
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36 (the last stable release of HMMER 2) to be present on the path under |
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37 the name hmmsearch2 (allowing it to co-exist with HMMER 3). |
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38 """ |
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39 import os |
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40 import sys |
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41 import re |
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42 import subprocess |
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43 from seq_analysis_utils import stop_err, fasta_iterator |
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44 |
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45 if len(sys.argv) != 5: |
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46 stop_err("Requires four arguments: protein FASTA filename, threads, model, and output filename") |
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47 |
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48 fasta_file, threads, model, tabular_file = sys.argv[1:] |
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49 hmm_output_file = tabular_file + ".hmm.tmp" |
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50 signalp_input_file = tabular_file + ".fasta.tmp" |
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51 signalp_output_file = tabular_file + ".tabular.tmp" |
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52 min_signalp_hmm = 0.9 |
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53 hmmer_search = "hmmsearch2" |
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54 |
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55 if model == "Bhattacharjee2006": |
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56 signalp_trunc = 70 |
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57 re_rxlr = re.compile("R.LR") |
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58 min_sp = 10 |
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59 max_sp = 40 |
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60 max_sp_rxlr = 100 |
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61 min_rxlr_start = 1 |
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62 #Allow signal peptide to be at most 40aa, and want RXLR to be |
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63 #within 100aa, therefore for the prescreen the max start is 140: |
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64 max_rxlr_start = max_sp + max_sp_rxlr |
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65 elif model == "Win2007": |
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66 signalp_trunc = 70 |
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67 re_rxlr = re.compile("R.LR") |
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68 min_sp = 10 |
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69 max_sp = 40 |
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70 min_rxlr_start = 30 |
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71 max_rxlr_start = 60 |
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72 #No explicit limit on separation of signal peptide clevage |
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73 #and RXLR, but shortest signal peptide is 10, and furthest |
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74 #away RXLR is 60, so effectively limit is 50. |
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75 max_sp_rxlr = max_rxlr_start - min_sp + 1 |
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76 elif model == "Whisson2007": |
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77 signalp_trunc = 0 #zero for no truncation |
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78 re_rxlr = re.compile("R.LR.{,40}[ED][ED][KR]") |
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79 min_sp = 10 |
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80 max_sp = 40 |
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81 max_sp_rxlr = 100 |
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82 min_rxlr_start = 1 |
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83 max_rxlr_start = max_sp + max_sp_rxlr |
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84 else: |
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85 stop_err("Did not recognise the model name %r\n" |
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86 "Use Bhattacharjee2006, Win2007, or Whisson2007" % model) |
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87 |
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88 |
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89 def get_hmmer_version(exe, required=None): |
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90 cmd = "%s -h" % exe |
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91 try: |
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92 child = subprocess.Popen([exe, "-h"], stdout=subprocess.PIPE, stderr=subprocess.PIPE) |
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93 except OSError: |
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94 raise ValueError("Could not run %s" % exe) |
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95 stdout, stderr = child.communicate() |
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96 if required: |
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97 return required in stdout |
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98 elif "HMMER 2" in stdout: |
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99 return 2 |
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100 elif "HMMER 3" in stdout: |
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101 return 3 |
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102 else: |
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103 raise ValueError("Could not determine version of %s" % exe) |
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104 |
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105 |
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106 #Run hmmsearch for Whisson et al. (2007) |
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107 if model == "Whisson2007": |
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108 hmm_file = os.path.join(os.path.split(sys.argv[0])[0], |
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109 "whisson_et_al_rxlr_eer_cropped.hmm") |
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110 if not os.path.isfile(hmm_file): |
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111 stop_err("Missing HMM file for Whisson et al. (2007)") |
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112 if not get_hmmer_version(hmmer_search, "HMMER 2.3.2 (Oct 2003)"): |
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113 stop_err("Missing HMMER 2.3.2 (Oct 2003) binary, %s" % hmmer_searcher) |
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114 #I've left the code to handle HMMER 3 in situ, in case |
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115 #we revisit the choice to insist on HMMER 2. |
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116 hmmer3 = (3 == get_hmmer_version(hmmer_search)) |
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117 #Using zero (or 5.6?) for bitscore threshold |
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118 if hmmer3: |
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119 #The HMMER3 table output is easy to parse |
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120 #In HMMER3 can't use both -T and -E |
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121 cmd = "%s -T 0 --tblout %s --noali %s %s > /dev/null" \ |
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122 % (hmmer_search, hmm_output_file, hmm_file, fasta_file) |
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123 else: |
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124 #For HMMER2 we are stuck with parsing stdout |
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125 #Put 1e6 to effectively have no expectation threshold (otherwise |
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126 #HMMER defaults to 10 and the calculated e-value depends on the |
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127 #input FASTA file, and we can loose hits of interest). |
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128 cmd = "%s -T 0 -E 1e6 %s %s > %s" \ |
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129 % (hmmer_search, hmm_file, fasta_file, hmm_output_file) |
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130 return_code = os.system(cmd) |
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131 if return_code: |
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132 stop_err("Error %i from hmmsearch:\n%s" % (return_code, cmd)) |
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133 hmm_hits = set() |
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134 valid_ids = set() |
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135 for title, seq in fasta_iterator(fasta_file): |
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136 name = title.split(None,1)[0] |
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137 if name in valid_ids: |
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138 stop_err("Duplicated identifier %r" % name) |
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139 else: |
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140 valid_ids.add(name) |
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141 handle = open(hmm_output_file) |
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142 for line in handle: |
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143 if not line.strip(): |
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144 #We expect blank lines in the HMMER2 stdout |
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145 continue |
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146 elif line.startswith("#"): |
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147 #Header |
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148 continue |
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149 else: |
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150 name = line.split(None,1)[0] |
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151 #Should be a sequence name in the HMMER3 table output. |
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152 #Could be anything in the HMMER2 stdout. |
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153 if name in valid_ids: |
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154 hmm_hits.add(name) |
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155 elif hmmer3: |
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156 stop_err("Unexpected identifer %r in hmmsearch output" % name) |
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157 handle.close() |
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158 #if hmmer3: |
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159 # print "HMMER3 hits for %i/%i" % (len(hmm_hits), len(valid_ids)) |
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160 #else: |
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161 # print "HMMER2 hits for %i/%i" % (len(hmm_hits), len(valid_ids)) |
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162 #print "%i/%i matched HMM" % (len(hmm_hits), len(valid_ids)) |
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163 os.remove(hmm_output_file) |
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164 del valid_ids |
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165 |
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166 |
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167 #Prepare short list of candidates containing RXLR to pass to SignalP |
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168 assert min_rxlr_start > 0, "Min value one, since zero based counting" |
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169 count = 0 |
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170 total = 0 |
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171 handle = open(signalp_input_file, "w") |
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172 for title, seq in fasta_iterator(fasta_file): |
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173 total += 1 |
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174 name = title.split(None,1)[0] |
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175 match = re_rxlr.search(seq[min_rxlr_start-1:].upper()) |
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176 if match and min_rxlr_start - 1 + match.start() + 1 <= max_rxlr_start: |
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177 #This is a potential RXLR, depending on the SignalP results. |
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178 #Might as well truncate the sequence now, makes the temp file smaller |
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179 if signalp_trunc: |
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180 handle.write(">%s (truncated)\n%s\n" % (name, seq[:signalp_trunc])) |
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181 else: |
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182 #Does it matter we don't line wrap? |
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183 handle.write(">%s\n%s\n" % (name, seq)) |
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184 count += 1 |
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185 handle.close() |
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186 #print "Running SignalP on %i/%i potentials." % (count, total) |
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187 |
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188 |
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189 #Run SignalP (using our wrapper script to get multi-core support etc) |
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190 signalp_script = os.path.join(os.path.split(sys.argv[0])[0], "signalp3.py") |
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191 if not os.path.isfile(signalp_script): |
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192 stop_err("Error - missing signalp3.py script") |
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193 cmd = "python %s euk %i %s %s %s" % (signalp_script, signalp_trunc, threads, signalp_input_file, signalp_output_file) |
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194 return_code = os.system(cmd) |
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195 if return_code: |
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196 stop_err("Error %i from SignalP:\n%s" % (return_code, cmd)) |
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197 #print "SignalP done" |
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198 |
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199 def parse_signalp(filename): |
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200 """Parse SignalP output, yield tuples of ID, HMM_Sprob_score and NN predicted signal peptide length. |
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201 |
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202 For signal peptide length we use NN_Ymax_pos (minus one). |
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203 """ |
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204 handle = open(filename) |
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205 line = handle.readline() |
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206 assert line.startswith("#ID\t"), line |
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207 for line in handle: |
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208 parts = line.rstrip("\t").split("\t") |
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209 assert len(parts)==20, repr(line) |
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210 yield parts[0], float(parts[18]), int(parts[5])-1 |
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211 handle.close() |
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212 |
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213 |
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214 #Parse SignalP results and apply the strict RXLR criteria |
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215 total = 0 |
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216 tally = dict() |
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217 handle = open(tabular_file, "w") |
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218 handle.write("#ID\t%s\n" % model) |
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219 signalp_results = parse_signalp(signalp_output_file) |
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220 for title, seq in fasta_iterator(fasta_file): |
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221 total += 1 |
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222 rxlr = "N" |
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223 name = title.split(None,1)[0] |
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224 match = re_rxlr.search(seq[min_rxlr_start-1:].upper()) |
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225 if match and min_rxlr_start - 1 + match.start() + 1 <= max_rxlr_start: |
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226 del match |
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227 #This was the criteria for calling SignalP, |
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228 #so it will be in the SignalP results. |
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229 sp_id, sp_hmm_score, sp_nn_len = signalp_results.next() |
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230 assert name == sp_id, "%s vs %s" % (name, sp_id) |
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231 if sp_hmm_score >= min_signalp_hmm and min_sp <= sp_nn_len <= max_sp: |
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232 match = re_rxlr.search(seq[sp_nn_len:].upper()) |
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233 if match and match.start() + 1 <= max_sp_rxlr: #1-based counting |
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234 rxlr_start = sp_nn_len + match.start() + 1 |
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235 if min_rxlr_start <= rxlr_start <= max_rxlr_start: |
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236 rxlr = "Y" |
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237 if model == "Whisson2007": |
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238 #Combine the signalp with regular expression heuristic and the HMM |
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239 if name in hmm_hits and rxlr == "N": |
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240 rxlr = "hmm" #HMM only |
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241 elif rxlr == "N": |
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242 rxlr = "neither" #Don't use N (no) |
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243 elif name not in hmm_hits and rxlr == "Y": |
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244 rxlr = "re" #Heuristic only |
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245 #Now have a four way classifier: Y, hmm, re, neither |
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246 #and count is the number of Y results (both HMM and heuristic) |
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247 handle.write("%s\t%s\n" % (name, rxlr)) |
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248 try: |
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249 tally[rxlr] += 1 |
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250 except KeyError: |
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251 tally[rxlr] = 1 |
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252 handle.close() |
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253 assert sum(tally.values()) == total |
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254 |
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255 #Check the iterator is finished |
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256 try: |
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257 signalp_results.next() |
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258 assert False, "Unexpected data in SignalP output" |
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259 except StopIteration: |
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260 pass |
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261 |
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262 #Cleanup |
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263 os.remove(signalp_input_file) |
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264 os.remove(signalp_output_file) |
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265 |
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266 #Short summary to stdout for Galaxy's info display |
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267 print "%s for %i sequences:" % (model, total) |
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268 print ", ".join("%s = %i" % kv for kv in sorted(tally.iteritems())) |