Mercurial > repos > drosofff > msp_blastparser_and_hits

annotate BlastParser_and_hits.py @ 12:80e4273db58d draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/msp_blastparser_and_hits commit ef2184fc4283307b338d41a6bcaf49dc37543e43
author drosofff
date Tue, 05 Apr 2016 05:15:26 -0400
parents 78c34df2dd8d
children 1991c830504a
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1 #!/usr/bin/python
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2 # blastn tblastn blastx parser revised 14-1-2016.
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3 # drosofff@gmail.com
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4
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5 import sys
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6 import argparse
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7 from collections import defaultdict
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8
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9 def Parser():
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10 the_parser = argparse.ArgumentParser()
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11 the_parser.add_argument('--blast', action="store", type=str, help="Path to the blast output (tabular format, 12 column)")
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12 the_parser.add_argument('--sequences', action="store", type=str, help="Path to the fasta file with blasted sequences")
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13 the_parser.add_argument('--fastaOutput', action="store", type=str, help="fasta output file of blast hits")
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14 the_parser.add_argument('--tabularOutput', action="store", type=str, help="tabular output file of blast analysis")
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15 the_parser.add_argument('--flanking', action="store", type=int, help="number of flanking nucleotides added to the hit sequences")
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16 the_parser.add_argument('--mode', action="store", choices=["verbose", "short"], type=str, help="reporting (verbose) or not reporting (short) oases contigs")
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17 the_parser.add_argument('--filter_relativeCov', action="store", type=float, default=0, help="filter out relative coverages below the specified ratio (float number)")
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18 the_parser.add_argument('--filter_maxScore', action="store", type=float, default=0, help="filter out best BitScores below the specified float number")
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19 the_parser.add_argument('--filter_meanScore', action="store", type=float, default=0, help="filter out mean BitScores below the specified float number")
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20 the_parser.add_argument('--filter_term_in', action="store", type=str, default="", help="select the specified term in the subject list")
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21 the_parser.add_argument('--filter_term_out', action="store", type=str, default="", help="exclude the specified term from the subject list")
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22 the_parser.add_argument('--al_sequences', action="store", type=str, help="sequences that have been blast aligned")
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23 the_parser.add_argument('--un_sequences', action="store", type=str, help="sequences that have not been blast aligned")
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24 the_parser.add_argument('--dataset_name', action="store", type=str, default="", help="the name of the dataset that has been parsed, to be reported in the output")
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25 args = the_parser.parse_args()
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26 if not all ( (args.sequences, args.blast, args.fastaOutput, args.tabularOutput) ):
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27 the_parser.error('argument(s) missing, call the -h option of the script')
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28 if not args.flanking:
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29 args.flanking = 0
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30 return args
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31
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32 def median(lst):
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33 lst = sorted(lst)
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34 if len(lst) < 1:
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35 return None
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36 if len(lst) %2 == 1:
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37 return lst[((len(lst)+1)/2)-1]
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38 if len(lst) %2 == 0:
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39 return float(sum(lst[(len(lst)/2)-1:(len(lst)/2)+1]))/2.0
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40
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41 def mean(lst):
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42 if len(lst) < 1:
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43 return 0
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44 return sum(lst) / float(len(lst))
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45
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46 def getfasta (fastafile):
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47 fastadic = {}
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48 for line in open (fastafile):
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49 if line[0] == ">":
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50 header = line[1:-1]
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51 fastadic[header] = ""
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52 else:
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53 fastadic[header] += line
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54 for header in fastadic:
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55 fastadic[header] = "".join(fastadic[header].split("\n"))
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56 return fastadic
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57
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58 def insert_newlines(string, every=60):
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59 lines = []
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60 for i in xrange(0, len(string), every):
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61 lines.append(string[i:i+every])
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62 return '\n'.join(lines)
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63
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64 def getblast (blastfile):
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65 '''blastinfo [0] Percentage of identical matches
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66 blastinfo [1] Alignment length
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67 blastinfo [2] Number of mismatches
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68 blastinfo [3] Number of gap openings
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69 blastinfo [4] Start of alignment in query
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70 blastinfo [5] End of alignment in query
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71 blastinfo [6] Start of alignment in subject (database hit)
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72 blastinfo [7] End of alignment in subject (database hit)
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73 blastinfo [8] Expectation value (E-value)
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74 blastinfo [9] Bit score
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75 blastinfo [10] Subject length (NEED TO BE SPECIFIED WHEN RUNNING BLAST) '''
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76 blastdic = defaultdict (dict)
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77 for line in open (blastfile):
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78 fields = line[:-1].split("\t")
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79 transcript = fields[0]
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80 subject = fields[1]
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81 blastinfo = [float(fields[2]) ] # blastinfo[0]
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82 blastinfo = blastinfo + [int(i) for i in fields[3:10] ] # blastinfo[1:8] insets 1 to 7
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83 blastinfo.append(fields[10]) # blastinfo[8] E-value remains as a string type
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84 blastinfo.append(float(fields[11])) # blastinfo[9] Bit score
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85 blastinfo.append(int(fields[12])) # blastinfo[10] Subject length MUST BE RETRIEVED THROUGH A 13 COLUMN BLAST OUTPUT
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86 try:
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87 blastdic[subject][transcript].append(blastinfo)
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88 except:
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89 blastdic[subject][transcript] = [ blastinfo ]
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90 return blastdic
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91
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92 def getseq (fastadict, transcript, up, down, orientation="direct"):
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93 def reverse (seq):
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94 revdict = {"A":"T","T":"A","G":"C","C":"G","N":"N"}
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95 revseq = [revdict[i] for i in seq[::-1]]
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96 return "".join(revseq)
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97 pickseq = fastadict[transcript][up-1:down]
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98 if orientation == "direct":
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99 return pickseq
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100 else:
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101 return reverse(pickseq)
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102
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103 def subjectCoverage (fastadict, blastdict, subject, QueriesFlankingNucleotides=0):
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104 SubjectCoverageList = []
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105 HitDic = {}
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106 bitScores = []
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107 for transcript in blastdict[subject]:
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108 prefix = "%s--%s_" % (subject, transcript)
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109 hitNumber = 0
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110 for hit in blastdict[subject][transcript]:
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111 hitNumber += 1
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112 suffix = "hit%s_IdMatch=%s,AligLength=%s,E-val=%s" % (hitNumber, hit[0], hit[1], hit[8])
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113 HitDic[prefix+suffix] = GetHitSequence (fastadict, transcript, hit[4], hit[5], QueriesFlankingNucleotides) #query coverage by a hit is in hit[4:6]
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114 SubjectCoverageList += range (min([hit[6], hit[7]]), max([hit[6], hit[7]]) + 1) # subject coverage by a hit is in hit[6:8]
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115 bitScores.append(hit[9])
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116 subjectLength = hit [10] # always the same value for a given subject. Stupid but simple
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117 TotalSubjectCoverage = len ( set (SubjectCoverageList) )
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118 RelativeSubjectCoverage = TotalSubjectCoverage/float(subjectLength)
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119 return HitDic, subjectLength, TotalSubjectCoverage, RelativeSubjectCoverage, max(bitScores), mean(bitScores)
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120
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121 def GetHitSequence (fastadict, FastaHeader, leftCoordinate, rightCoordinate, FlankingValue):
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122 if rightCoordinate > leftCoordinate:
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123 polarity = "direct"
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124 else:
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125 polarity = "reverse"
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126 leftCoordinate, rightCoordinate = rightCoordinate, leftCoordinate
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127 if leftCoordinate - FlankingValue > 0:
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128 leftCoordinate -= FlankingValue
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129 else:
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130 leftCoordinate = 1
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131 return getseq (fastadict, FastaHeader, leftCoordinate, rightCoordinate, polarity)
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132
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133 def outputParsing (dataset_name, F, Fasta, results, Xblastdict, fastadict, filter_relativeCov=0, filter_maxScore=0, filter_meanScore=0, filter_term_in="", filter_term_out="", mode="verbose"):
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134 def filter_results (results, filter_relativeCov=0, filter_maxScore=0, filter_meanScore=0, filter_term_in="", filter_term_out=""):
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135 for subject in results.keys():
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136 if results[subject]["RelativeSubjectCoverage"]<filter_relativeCov:
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137 del results[subject]
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138 continue
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139 if results[subject]["maxBitScores"]<filter_maxScore:
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140 del results[subject]
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141 continue
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142 if results[subject]["meanBitScores"]<filter_meanScore:
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143 del results[subject]
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144 continue
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145 if filter_term_in in subject:
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146 pass
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147 else:
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148 del results[subject]
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149 continue
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150 if filter_term_out and filter_term_out in subject:
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151 del results[subject]
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152 continue
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153 return results
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154
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155 F= open(F, "w")
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156 Fasta=open(Fasta, "w")
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157 blasted_transcripts = []
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158 filter_results (results, filter_relativeCov, filter_maxScore, filter_meanScore, filter_term_in, filter_term_out)
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159 for subject in results:
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160 for transcript in Xblastdict[subject]:
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161 blasted_transcripts.append(transcript)
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162 blasted_transcripts = list( set( blasted_transcripts))
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163 if mode == "verbose":
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164 print >>F, "--- %s ---" % (dataset_name)
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165 print >>F, "# SeqId\t%Identity\tAlignLength\tStartSubject\tEndSubject\t%QueryHitCov\tE-value\tBitScore"
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166 for subject in sorted (results, key=lambda x: results[x]["meanBitScores"], reverse=True):
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167 print >> F, " \n# %s" % subject
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168 print >> F, "# Suject Length: %s" % (results[subject]["subjectLength"])
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169 print >> F, "# Total Subject Coverage: %s" % (results[subject]["TotalCoverage"])
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170 print >> F, "# Relative Subject Coverage: %s" % (results[subject]["RelativeSubjectCoverage"])
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171 print >> F, "# Best Bit Score: %s" % (results[subject]["maxBitScores"])
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172 print >> F, "# Mean Bit Score: %s" % (results[subject]["meanBitScores"])
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173 for header in results[subject]["HitDic"]:
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174 print >> Fasta, ">%s\n%s" % (header, insert_newlines(results[subject]["HitDic"][header]) )
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175 print >> Fasta, "" # final carriage return for the sequence
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176 for transcript in Xblastdict[subject]:
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177 transcriptSize = float(len(fastadict[transcript]))
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178 for hit in Xblastdict[subject][transcript]:
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179 percentIdentity, alignLenght, subjectStart, subjectEnd, queryCov = hit[0], hit[1], hit[6], hit[7], "%.1f" % (abs(hit[5]-hit[4])/transcriptSize*100)
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180 Eval, BitScore = hit[8], hit[9]
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181 info = [transcript] + [percentIdentity, alignLenght, subjectStart, subjectEnd, queryCov, Eval, BitScore]
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182 info = [str(i) for i in info]
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183 info = "\t".join(info)
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184 print >> F, info
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185 else:
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186 print >>F, "--- %s ---" % (dataset_name)
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187 print >>F, "# subject\tsubject length\tTotal Subject Coverage\tRelative Subject Coverage\tBest Bit Score\tMean Bit Score"
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188 for subject in sorted (results, key=lambda x: results[x]["meanBitScores"], reverse=True):
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189 line = []
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190 line.append(subject)
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191 line.append(results[subject]["subjectLength"])
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192 line.append(results[subject]["TotalCoverage"])
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193 line.append(results[subject]["RelativeSubjectCoverage"])
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194 line.append(results[subject]["maxBitScores"])
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195 line.append(results[subject]["meanBitScores"])
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196 line = [str(i) for i in line]
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197 print >> F, "\t".join(line)
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198 for header in results[subject]["HitDic"]:
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199 print >> Fasta, ">%s\n%s" % (header, insert_newlines(results[subject]["HitDic"][header]) )
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200 print >> Fasta, "" # final carriage return for the sequence
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201 F.close()
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202 Fasta.close()
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203 return blasted_transcripts
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204
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205 def dispatch_sequences (fastadict, blasted_transcripts, matched_sequences, unmatched_sequences):
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206 '''to output the sequences that matched and did not matched in the blast'''
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207 F_matched = open (matched_sequences, "w")
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208 F_unmatched = open (unmatched_sequences, "w")
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209 for transcript in fastadict:
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210 if transcript in blasted_transcripts: # le list of blasted_transcripts is generated by the outputParsing function
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211 print >> F_matched, ">%s\n%s" % (transcript, insert_newlines(fastadict[transcript]) )
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212 else:
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213 print >> F_unmatched, ">%s\n%s" % (transcript, insert_newlines(fastadict[transcript]) )
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214 F_matched.close()
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215 F_unmatched.close()
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216 return
0
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217
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218 def __main__ ():
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219 args = Parser()
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220 fastadict = getfasta (args.sequences)
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221 Xblastdict = getblast (args.blast)
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222 results = defaultdict(dict)
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223 for subject in Xblastdict:
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224 results[subject]["HitDic"], results[subject]["subjectLength"], results[subject]["TotalCoverage"], results[subject]["RelativeSubjectCoverage"], results[subject]["maxBitScores"], results[subject]["meanBitScores"] = subjectCoverage(fastadict, Xblastdict, subject, args.flanking)
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225 blasted_transcripts = outputParsing (args.dataset_name, args.tabularOutput, args.fastaOutput, results, Xblastdict, fastadict,
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226 filter_relativeCov=args.filter_relativeCov, filter_maxScore=args.filter_maxScore,
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227 filter_meanScore=args.filter_meanScore, filter_term_in=args.filter_term_in,
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228 filter_term_out=args.filter_term_out, mode=args.mode)
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229 dispatch_sequences (fastadict, blasted_transcripts, args.al_sequences, args.un_sequences)
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230
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231 if __name__=="__main__": __main__()