Mercurial > repos > drosofff > msp_blastparser_and_hits

annotate BlastParser_and_hits.py @ 15:1991c830504a draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/msp_blastparser_and_hits commit b6de14061c479f0418cd89e26d6f5ac26e565a07
author drosofff
date Wed, 09 Nov 2016 11:32:32 -0500
parents 78c34df2dd8d
children
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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 argparse
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6 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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32
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33 def median(lst):
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34 lst = sorted(lst)
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35 if len(lst) < 1:
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36 return None
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37 if len(lst) % 2 == 1:
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38 return lst[((len(lst)+1)/2)-1]
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39 if len(lst) % 2 == 0:
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40 return float(sum(lst[(len(lst)/2)-1:(len(lst)/2)+1]))/2.0
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42
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43 def mean(lst):
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44 if len(lst) < 1:
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45 return 0
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46 return sum(lst) / float(len(lst))
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47
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49 def getfasta(fastafile):
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50 fastadic = {}
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51 for line in open(fastafile):
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52 if line[0] == ">":
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53 header = line[1:-1]
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54 fastadic[header] = ""
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55 else:
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56 fastadic[header] += line
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57 for header in fastadic:
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58 fastadic[header] = "".join(fastadic[header].split("\n"))
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59 return fastadic
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61
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62 def insert_newlines(string, every=60):
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63 lines = []
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64 for i in xrange(0, len(string), every):
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65 lines.append(string[i:i+every])
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66 return '\n'.join(lines)
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69 def getblast(blastfile):
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70 '''blastinfo [0] Percentage of identical matches
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71 blastinfo [1] Alignment length
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72 blastinfo [2] Number of mismatches
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73 blastinfo [3] Number of gap openings
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74 blastinfo [4] Start of alignment in query
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75 blastinfo [5] End of alignment in query
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76 blastinfo [6] Start of alignment in subject (database hit)
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77 blastinfo [7] End of alignment in subject (database hit)
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78 blastinfo [8] Expectation value (E-value)
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79 blastinfo [9] Bit score
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80 blastinfo [10] Subject length (NEED TO BE SPECIFIED WHEN RUNNING BLAST) '''
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81 blastdic = defaultdict(dict)
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82 for line in open(blastfile):
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83 fields = line[:-1].split("\t")
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84 transcript = fields[0]
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85 subject = fields[1]
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86 blastinfo = [float(fields[2])] # blastinfo[0]
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87 blastinfo = blastinfo + [int(i) for i in fields[3:10]] # blastinfo[1:8] insets 1 to 7
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88 blastinfo.append(fields[10]) # blastinfo[8] E-value remains as a string type
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89 blastinfo.append(float(fields[11])) # blastinfo[9] Bit score
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90 blastinfo.append(int(fields[12])) # blastinfo[10] Subject length MUST BE RETRIEVED THROUGH A 13 COLUMN BLAST OUTPUT
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91 try:
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92 blastdic[subject][transcript].append(blastinfo)
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93 except Exception:
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94 blastdic[subject][transcript] = [blastinfo]
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95 return blastdic
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96
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98 def getseq(fastadict, transcript, up, down, orientation="direct"):
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99 def reverse(seq):
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100 revdict = {"A": "T", "T": "A", "G": "C", "C": "G", "N": "N"}
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101 revseq = [revdict[i] for i in seq[::-1]]
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102 return "".join(revseq)
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103 pickseq = fastadict[transcript][up-1:down]
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104 if orientation == "direct":
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105 return pickseq
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106 else:
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107 return reverse(pickseq)
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108
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110 def subjectCoverage(fastadict, blastdict, subject, QueriesFlankingNucleotides=0):
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111 SubjectCoverageList = []
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112 HitDic = {}
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113 bitScores = []
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114 for transcript in blastdict[subject]:
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115 prefix = "%s--%s_" % (subject, transcript)
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116 hitNumber = 0
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117 for hit in blastdict[subject][transcript]:
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118 hitNumber += 1
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119 suffix = "hit%s_IdMatch=%s,AligLength=%s,E-val=%s" % (hitNumber, hit[0], hit[1], hit[8])
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120 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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121 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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122 bitScores.append(hit[9])
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123 subjectLength = hit[10] # always the same value for a given subject. Stupid but simple
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124 TotalSubjectCoverage = len(set(SubjectCoverageList))
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125 RelativeSubjectCoverage = TotalSubjectCoverage/float(subjectLength)
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126 return HitDic, subjectLength, TotalSubjectCoverage, RelativeSubjectCoverage, max(bitScores), mean(bitScores)
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127
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128
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129 def GetHitSequence(fastadict, FastaHeader, leftCoordinate, rightCoordinate, FlankingValue):
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130 if rightCoordinate > leftCoordinate:
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131 polarity = "direct"
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132 else:
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133 polarity = "reverse"
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134 leftCoordinate, rightCoordinate = rightCoordinate, leftCoordinate
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135 if leftCoordinate - FlankingValue > 0:
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136 leftCoordinate -= FlankingValue
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137 else:
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138 leftCoordinate = 1
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139 return getseq(fastadict, FastaHeader, leftCoordinate, rightCoordinate, polarity)
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140
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141
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142 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"):
1991c830504a planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/msp_blastparser_and_hits commit b6de14061c479f0418cd89e26d6f5ac26e565a07
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143 def filter_results(results, filter_relativeCov=0, filter_maxScore=0, filter_meanScore=0, filter_term_in="", filter_term_out=""):
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144 for subject in results.keys():
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145 if results[subject]["RelativeSubjectCoverage"] < filter_relativeCov:
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146 del results[subject]
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147 continue
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148 if results[subject]["maxBitScores"] < filter_maxScore:
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149 del results[subject]
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150 continue
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151 if results[subject]["meanBitScores"] < filter_meanScore:
4
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152 del results[subject]
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153 continue
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154 if filter_term_in in subject:
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155 pass
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156 else:
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157 del results[subject]
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158 continue
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159 if filter_term_out and filter_term_out in subject:
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160 del results[subject]
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161 continue
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162 return results
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163
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164 F = open(F, "w")
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165 Fasta = open(Fasta, "w")
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166 blasted_transcripts = []
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167 filter_results(results, filter_relativeCov, filter_maxScore, filter_meanScore, filter_term_in, filter_term_out)
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168 for subject in results:
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169 for transcript in Xblastdict[subject]:
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170 blasted_transcripts.append(transcript)
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171 blasted_transcripts = list(set(blasted_transcripts))
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172 if mode == "verbose":
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173 print >>F, "--- %s ---" % (dataset_name)
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174 print >>F, "# SeqId\t%Identity\tAlignLength\tStartSubject\tEndSubject\t%QueryHitCov\tE-value\tBitScore"
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175 for subject in sorted(results, key=lambda x: results[x]["meanBitScores"], reverse=True):
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176 print >> F, " \n# %s" % subject
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177 print >> F, "# Suject Length: %s" % (results[subject]["subjectLength"])
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178 print >> F, "# Total Subject Coverage: %s" % (results[subject]["TotalCoverage"])
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179 print >> F, "# Relative Subject Coverage: %s" % (results[subject]["RelativeSubjectCoverage"])
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180 print >> F, "# Best Bit Score: %s" % (results[subject]["maxBitScores"])
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181 print >> F, "# Mean Bit Score: %s" % (results[subject]["meanBitScores"])
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182 for header in results[subject]["HitDic"]:
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183 print >> Fasta, ">%s\n%s" % (header, insert_newlines(results[subject]["HitDic"][header]))
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184 print >> Fasta, "" # final carriage return for the sequence
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185 for transcript in Xblastdict[subject]:
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186 transcriptSize = float(len(fastadict[transcript]))
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187 for hit in Xblastdict[subject][transcript]:
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188 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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189 Eval, BitScore = hit[8], hit[9]
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190 info = [transcript] + [percentIdentity, alignLenght, subjectStart, subjectEnd, queryCov, Eval, BitScore]
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191 info = [str(i) for i in info]
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192 info = "\t".join(info)
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193 print >> F, info
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194 else:
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195 print >>F, "--- %s ---" % (dataset_name)
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196 print >>F, "# subject\tsubject length\tTotal Subject Coverage\tRelative Subject Coverage\tBest Bit Score\tMean Bit Score"
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197 for subject in sorted(results, key=lambda x: results[x]["meanBitScores"], reverse=True):
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198 line = []
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199 line.append(subject)
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200 line.append(results[subject]["subjectLength"])
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201 line.append(results[subject]["TotalCoverage"])
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202 line.append(results[subject]["RelativeSubjectCoverage"])
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203 line.append(results[subject]["maxBitScores"])
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204 line.append(results[subject]["meanBitScores"])
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205 line = [str(i) for i in line]
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206 print >> F, "\t".join(line)
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207 for header in results[subject]["HitDic"]:
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208 print >> Fasta, ">%s\n%s" % (header, insert_newlines(results[subject]["HitDic"][header]))
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209 print >> Fasta, "" # final carriage return for the sequence
0
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210 F.close()
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211 Fasta.close()
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212 return blasted_transcripts
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213
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215 def dispatch_sequences(fastadict, blasted_transcripts, matched_sequences, unmatched_sequences):
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216 '''to output the sequences that matched and did not matched in the blast'''
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217 F_matched = open(matched_sequences, "w")
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218 F_unmatched = open(unmatched_sequences, "w")
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219 for transcript in fastadict:
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220 if transcript in blasted_transcripts: # list of blasted_transcripts is generated by the outputParsing function
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221 print >> F_matched, ">%s\n%s" % (transcript, insert_newlines(fastadict[transcript]))
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222 else:
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223 print >> F_unmatched, ">%s\n%s" % (transcript, insert_newlines(fastadict[transcript]))
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224 F_matched.close()
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225 F_unmatched.close()
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226 return
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227
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229 def __main__():
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230 args = Parser()
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231 fastadict = getfasta(args.sequences)
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232 Xblastdict = getblast(args.blast)
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233 results = defaultdict(dict)
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234 for subject in Xblastdict:
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235 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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236 blasted_transcripts = outputParsing(args.dataset_name, args.tabularOutput,
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237 args.fastaOutput, results, Xblastdict, fastadict,
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238 filter_relativeCov=args.filter_relativeCov,
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239 filter_maxScore=args.filter_maxScore,
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240 filter_meanScore=args.filter_meanScore,
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241 filter_term_in=args.filter_term_in,
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242 filter_term_out=args.filter_term_out,
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243 mode=args.mode)
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244 dispatch_sequences(fastadict, blasted_transcripts, args.al_sequences, args.un_sequences)
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245
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246 if __name__ == "__main__":
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247 __main__()