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1 import sys
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2 import pysam
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3 import operator
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4 import os
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5 import time
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6 import sqlite3
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7 from sqlitedict import SqliteDict
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8
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9
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10 def tran_to_genome(tran, pos, transcriptome_info_dict):
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11 # print ("tran",list(transcriptome_info_dict))
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12 traninfo = transcriptome_info_dict[tran]
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13 chrom = traninfo["chrom"]
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14 strand = traninfo["strand"]
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15 exons = sorted(traninfo["exons"])
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16 # print exons
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17 if strand == "+":
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18 exon_start = 0
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19 for tup in exons:
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20 exon_start = tup[0]
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21 exonlen = tup[1] - tup[0]
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22 if pos > exonlen:
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23 pos = (pos - exonlen) - 1
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24 else:
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25 break
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26 genomic_pos = (exon_start + pos) - 1
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27 elif strand == "-":
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28 exon_start = 0
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29 for tup in exons[::-1]:
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30 exon_start = tup[1]
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31 exonlen = tup[1] - tup[0]
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32 if pos > exonlen:
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33 pos = (pos - exonlen) - 1
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34 else:
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35 break
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36 genomic_pos = (exon_start - pos) + 1
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37 return (chrom, genomic_pos)
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38
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39
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40 # Takes a dictionary with a readname as key and a list of lists as value, each sub list has consists of two elements a transcript and the position the read aligns to in the transcript
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41 # This function will count the number of genes that the transcripts correspond to and if less than or equal to 3 will add the relevant value to transcript_counts_dict
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42 def processor(
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43 process_chunk,
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44 master_read_dict,
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45 transcriptome_info_dict,
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46 master_dict,
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47 readseq,
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48 unambig_read_length_dict,
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49 ):
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50 readlen = len(readseq)
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51 ambiguously_mapped_reads = 0
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52 # get the read name
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53 read = list(process_chunk)[0]
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54
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55 read_list = process_chunk[
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56 read
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57 ] # a list of lists of all transcripts the read aligns to and the positions
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58 # used to store different genomic poistions
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59 genomic_positions = []
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60
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61 # This section is just to get the different genomic positions the read aligns to
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62
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63 for listname in process_chunk[read]:
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64
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65 tran = listname[0].replace("-", "_").replace("(", "").replace(")", "")
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66
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67 pos = int(listname[1])
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68 genomic_pos = tran_to_genome(tran, pos, transcriptome_info_dict)
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69 # print ("genomic pos",genomic_pos)
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70 if genomic_pos not in genomic_positions:
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71 genomic_positions.append(genomic_pos)
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72
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73 # If the read maps unambiguously
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74 if len(genomic_positions) == 1:
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75 if readlen not in unambig_read_length_dict:
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76 unambig_read_length_dict[readlen] = 0
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77 unambig_read_length_dict[readlen] += 1
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78 # assume this read aligns to a noncoding position, if we find that it does align to a coding region change this to True
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79 coding = False
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80
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81 # For each transcript this read alings to
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82 for listname in process_chunk[read]:
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83 # get the transcript name
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84 tran = listname[0].replace("-", "_").replace("(", "").replace(")", "")
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85 # If we haven't come across this transcript already then add to master_read_dict
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86 if tran not in master_read_dict:
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87 master_read_dict[tran] = {
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88 "ambig": {},
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89 "unambig": {},
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90 "mismatches": {},
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91 "seq": {},
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92 }
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93 # get the raw unedited positon, and read tags
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94 pos = int(listname[1])
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95 read_tags = listname[2]
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96 # If there is mismatches in this line, then modify the postion and readlen (if mismatches at start or end) and add mismatches to dictionary
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97 nm_tag = 0
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98
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99 for tag in read_tags:
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100 if tag[0] == "NM":
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101 nm_tag = int(tag[1])
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102 if nm_tag > 0:
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103 md_tag = ""
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104 for tag in read_tags:
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105 if tag[0] == "MD":
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106 md_tag = tag[1]
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107 pos_modifier, readlen_modifier, mismatches = get_mismatch_pos(
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108 md_tag, pos, readlen, master_read_dict, tran, readseq
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109 )
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110 # Count the mismatches (we only do this for unambiguous)
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111 for mismatch in mismatches:
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112 # Ignore mismatches appearing in the first position (due to non templated addition)
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113 if mismatch != 0:
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114 char = mismatches[mismatch]
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115 mismatch_pos = pos + mismatch
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116 if mismatch_pos not in master_read_dict[tran]["seq"]:
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117 master_read_dict[tran]["seq"][mismatch_pos] = {}
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118 if char not in master_read_dict[tran]["seq"][mismatch_pos]:
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119 master_read_dict[tran]["seq"][mismatch_pos][char] = 0
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120 master_read_dict[tran]["seq"][mismatch_pos][char] += 1
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121 # apply the modifiers
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122 # pos = pos+pos_modifier
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123 # readlen = readlen - readlen_modifier
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124
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125 try:
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126 cds_start = transcriptome_info_dict[tran]["cds_start"]
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127 cds_stop = transcriptome_info_dict[tran]["cds_stop"]
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128
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129 if pos >= cds_start and pos <= cds_stop:
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130 coding = True
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131 except:
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132 pass
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133
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134 if readlen in master_read_dict[tran]["unambig"]:
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135 if pos in master_read_dict[tran]["unambig"][readlen]:
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136 master_read_dict[tran]["unambig"][readlen][pos] += 1
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137 else:
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138 master_read_dict[tran]["unambig"][readlen][pos] = 1
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139 else:
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140 master_read_dict[tran]["unambig"][readlen] = {pos: 1}
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141
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142 if coding == True:
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143 master_dict["unambiguous_coding_count"] += 1
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144 elif coding == False:
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145 master_dict["unambiguous_non_coding_count"] += 1
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146
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147 else:
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148 ambiguously_mapped_reads += 1
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149 for listname in process_chunk[read]:
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150 tran = listname[0].replace("-", "_").replace("(", "").replace(")", "")
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151 if tran not in master_read_dict:
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152 master_read_dict[tran] = {
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153 "ambig": {},
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154 "unambig": {},
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155 "mismatches": {},
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156 "seq": {},
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157 }
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158 pos = int(listname[1])
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159 read_tags = listname[2]
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160 nm_tag = 0
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161 for tag in read_tags:
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162 if tag[0] == "NM":
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163 nm_tag = int(tag[1])
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164 if nm_tag > 0:
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165 md_tag = ""
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166 for tag in read_tags:
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167 if tag[0] == "MD":
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168 md_tag = tag[1]
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169 pos_modifier, readlen_modifier, mismatches = get_mismatch_pos(
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170 md_tag, pos, readlen, master_read_dict, tran, readseq
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171 )
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172 # apply the modifiers
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173 # pos = pos+pos_modifier
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174 # readlen = readlen - readlen_modifier
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175 if readlen in master_read_dict[tran]["ambig"]:
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176 if pos in master_read_dict[tran]["ambig"][readlen]:
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177 master_read_dict[tran]["ambig"][readlen][pos] += 1
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178 else:
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179 master_read_dict[tran]["ambig"][readlen][pos] = 1
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180 else:
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181 master_read_dict[tran]["ambig"][readlen] = {pos: 1}
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182 return ambiguously_mapped_reads
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183
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184
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185 def get_mismatch_pos(md_tag, pos, readlen, master_read_dict, tran, readseq):
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186 nucs = ["A", "T", "G", "C"]
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187 mismatches = {}
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188 total_so_far = 0
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189 prev_char = ""
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190 for char in md_tag:
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191 if char in nucs:
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192 if prev_char != "":
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193 total_so_far += int(prev_char)
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194 prev_char = ""
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195 mismatches[total_so_far + len(mismatches)] = readseq[
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196 total_so_far + len(mismatches)
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197 ]
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198 else:
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199 if char != "^" and char != "N":
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200 if prev_char == "":
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201 prev_char = char
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202 else:
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203 total_so_far += int(prev_char + char)
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204 prev_char = ""
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205 readlen_modifier = 0
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206 pos_modifier = 0
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207 five_ok = False
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208 three_ok = False
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209 while five_ok == False:
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210 for i in range(0, readlen):
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211 if i in mismatches:
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212 pos_modifier += 1
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213 readlen_modifier += 1
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214 else:
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215 five_ok = True
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216 break
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217 five_ok = True
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218
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219 while three_ok == False:
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220 for i in range(readlen - 1, 0, -1):
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221 if i in mismatches:
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222 readlen_modifier += 1
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223 else:
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224 three_ok = True
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225 break
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226 three_ok = True
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227
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228 return (pos_modifier, readlen_modifier, mismatches)
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229
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230
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231 def process_bam(bam_filepath, transcriptome_info_dict_path, outputfile):
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232 desc = "NULL"
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233 start_time = time.time()
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234 study_dict = {}
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235 nuc_count_dict = {"mapped": {}, "unmapped": {}}
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236 dinuc_count_dict = {}
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237 threeprime_nuc_count_dict = {"mapped": {}, "unmapped": {}}
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238 read_length_dict = {}
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239 unambig_read_length_dict = {}
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240 unmapped_dict = {}
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241 master_dict = {
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242 "unambiguous_non_coding_count": 0,
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243 "unambiguous_coding_count": 0,
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244 "current_dir": os.getcwd(),
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245 }
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246
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247 transcriptome_info_dict = {}
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248 connection = sqlite3.connect(transcriptome_info_dict_path)
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249 cursor = connection.cursor()
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250 cursor.execute(
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251 "SELECT transcript,cds_start,cds_stop,length,strand,chrom,tran_type from transcripts;"
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252 )
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253 result = cursor.fetchall()
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254 for row in result:
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255 transcriptome_info_dict[str(row[0])] = {
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256 "cds_start": row[1],
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257 "cds_stop": row[2],
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258 "length": row[3],
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259 "strand": row[4],
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260 "chrom": row[5],
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261 "exons": [],
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262 "tran_type": row[6],
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263 }
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264 # print list(transcriptome_info_dict)[:10]
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265
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266 cursor.execute("SELECT * from exons;")
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267 result = cursor.fetchall()
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268 for row in result:
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269 transcriptome_info_dict[str(row[0])]["exons"].append((row[1], row[2]))
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270
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271 # it might be the case that there are no multimappers, so set this to 0 first to avoid an error, it will be overwritten later if there is multimappers
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272 multimappers = 0
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273 unmapped_reads = 0
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274 unambiguous_coding_count = 0
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275 unambiguous_non_coding_count = 0
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276 trip_periodicity_reads = 0
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277
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278 final_offsets = {
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279 "fiveprime": {"offsets": {}, "read_scores": {}},
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280 "threeprime": {"offsets": {}, "read_scores": {}},
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281 }
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282 master_read_dict = {}
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283 prev_seq = ""
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284 process_chunk = {"read_name": [["placeholder_tran", "1", "28"]]}
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285 mapped_reads = 0
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286 ambiguously_mapped_reads = 0
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287 master_trip_dict = {"fiveprime": {}, "threeprime": {}}
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288 master_offset_dict = {"fiveprime": {}, "threeprime": {}}
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289 master_metagene_stop_dict = {"fiveprime": {}, "threeprime": {}}
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290
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291 infile = pysam.Samfile(bam_filepath, "rb")
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292 header = infile.header["HD"]
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293 unsorted = False
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294 if "SO" in header:
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295 if header["SO"] != "queryname":
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296 unsorted = True
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297 else:
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298 unsorted = True
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299 if unsorted == True:
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300 print(
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301 "ERROR: Bam file appears to be unsorted or not sorted by read name. To sort by read name use the command: samtools sort -n input.bam output.bam"
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302 )
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303 print(header, bam_filepath)
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304 sys.exit()
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305 total_bam_lines = 0
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306 all_ref_ids = infile.references
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307
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308 for read in infile.fetch(until_eof=True):
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309 total_bam_lines += 1
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310 if not read.is_unmapped:
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311 ref = read.reference_id
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312 tran = (all_ref_ids[ref]).split(".")[0]
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313 mapped_reads += 1
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314 if mapped_reads % 1000000 == 0:
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315 print(
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316 "{} reads parsed at {}".format(
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317 mapped_reads, (time.time() - start_time)
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318 )
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319 )
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320 pos = read.reference_start
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321 readname = read.query_name
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322 read_tags = read.tags
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323 if readname == list(process_chunk)[0]:
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324 process_chunk[readname].append([tran, pos, read_tags])
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325 # if the current read is different from previous reads send 'process_chunk' to the 'processor' function, then start 'process_chunk' over using current read
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326 else:
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327 if list(process_chunk)[0] != "read_name":
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328
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329 # At this point we work out readseq, we do this for multiple reasons, firstly so we don't count the sequence from a read multiple times, just because
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330 # it aligns multiple times and secondly we only call read.seq once (read.seq is computationally expensive)
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331 seq = read.seq
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332 readlen = len(seq)
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333
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334 # Note if a read maps ambiguously it will still be counted toward the read length distribution (however it will only be counted once, not each time it maps)
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335 if readlen not in read_length_dict:
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336 read_length_dict[readlen] = 0
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337 read_length_dict[readlen] += 1
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338
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339 if readlen not in nuc_count_dict["mapped"]:
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340 nuc_count_dict["mapped"][readlen] = {}
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341 if readlen not in threeprime_nuc_count_dict["mapped"]:
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342 threeprime_nuc_count_dict["mapped"][readlen] = {}
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343 if readlen not in dinuc_count_dict:
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344 dinuc_count_dict[readlen] = {
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345 "AA": 0,
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346 "TA": 0,
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347 "GA": 0,
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348 "CA": 0,
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349 "AT": 0,
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350 "TT": 0,
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351 "GT": 0,
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352 "CT": 0,
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353 "AG": 0,
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354 "TG": 0,
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355 "GG": 0,
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356 "CG": 0,
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357 "AC": 0,
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358 "TC": 0,
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359 "GC": 0,
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360 "CC": 0,
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361 }
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362
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363 for i in range(0, len(seq)):
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364 if i not in nuc_count_dict["mapped"][readlen]:
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365 nuc_count_dict["mapped"][readlen][i] = {
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366 "A": 0,
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367 "T": 0,
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368 "G": 0,
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369 "C": 0,
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370 "N": 0,
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371 }
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372 nuc_count_dict["mapped"][readlen][i][seq[i]] += 1
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373
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374 for i in range(0, len(seq)):
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375 try:
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376 dinuc_count_dict[readlen][seq[i : i + 2]] += 1
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377 except:
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378 pass
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379
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380 for i in range(len(seq), 0, -1):
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381 dist = i - len(seq)
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382 if dist not in threeprime_nuc_count_dict["mapped"][readlen]:
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383 threeprime_nuc_count_dict["mapped"][readlen][dist] = {
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384 "A": 0,
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385 "T": 0,
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386 "G": 0,
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387 "C": 0,
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388 "N": 0,
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389 }
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390 threeprime_nuc_count_dict["mapped"][readlen][dist][
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391 seq[dist]
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392 ] += 1
|
|
|
393 ambiguously_mapped_reads += processor(
|
|
|
394 process_chunk,
|
|
|
395 master_read_dict,
|
|
|
396 transcriptome_info_dict,
|
|
|
397 master_dict,
|
|
|
398 prev_seq,
|
|
|
399 unambig_read_length_dict,
|
|
|
400 )
|
|
|
401 process_chunk = {readname: [[tran, pos, read_tags]]}
|
|
|
402 prev_seq = read.seq
|
|
|
403 else:
|
|
|
404 unmapped_reads += 1
|
|
|
405
|
|
|
406 # Add this unmapped read to unmapped_dict so we can see what the most frequent unmapped read is.
|
|
|
407 seq = read.seq
|
|
|
408 readlen = len(seq)
|
|
|
409 if seq in unmapped_dict:
|
|
|
410 unmapped_dict[seq] += 1
|
|
|
411 else:
|
|
|
412 unmapped_dict[seq] = 1
|
|
|
413
|
|
|
414 # Populate the nuc_count_dict with this unmapped read
|
|
|
415 if readlen not in nuc_count_dict["unmapped"]:
|
|
|
416 nuc_count_dict["unmapped"][readlen] = {}
|
|
|
417 for i in range(0, len(seq)):
|
|
|
418 if i not in nuc_count_dict["unmapped"][readlen]:
|
|
|
419 nuc_count_dict["unmapped"][readlen][i] = {
|
|
|
420 "A": 0,
|
|
|
421 "T": 0,
|
|
|
422 "G": 0,
|
|
|
423 "C": 0,
|
|
|
424 "N": 0,
|
|
|
425 }
|
|
|
426 nuc_count_dict["unmapped"][readlen][i][seq[i]] += 1
|
|
|
427
|
|
|
428 if readlen not in threeprime_nuc_count_dict["unmapped"]:
|
|
|
429 threeprime_nuc_count_dict["unmapped"][readlen] = {}
|
|
|
430
|
|
|
431 for i in range(len(seq), 0, -1):
|
|
|
432 dist = i - len(seq)
|
|
|
433 if dist not in threeprime_nuc_count_dict["unmapped"][readlen]:
|
|
|
434 threeprime_nuc_count_dict["unmapped"][readlen][dist] = {
|
|
|
435 "A": 0,
|
|
|
436 "T": 0,
|
|
|
437 "G": 0,
|
|
|
438 "C": 0,
|
|
|
439 "N": 0,
|
|
|
440 }
|
|
|
441 threeprime_nuc_count_dict["unmapped"][readlen][dist][seq[dist]] += 1
|
|
|
442
|
|
|
443 # add stats about mapped/unmapped reads to file dict which will be used for the final report
|
|
|
444 master_dict["total_bam_lines"] = total_bam_lines
|
|
|
445 master_dict["mapped_reads"] = mapped_reads
|
|
|
446 master_dict["unmapped_reads"] = unmapped_reads
|
|
|
447 master_read_dict["unmapped_reads"] = unmapped_reads
|
|
|
448 master_dict["ambiguously_mapped_reads"] = ambiguously_mapped_reads
|
|
|
449
|
|
|
450 if "read_name" in master_read_dict:
|
|
|
451 del master_read_dict["read_name"]
|
|
|
452 print("BAM file processed")
|
|
|
453 print("Creating metagenes, triplet periodicity plots, etc.")
|
|
|
454 for tran in master_read_dict:
|
|
|
455 try:
|
|
|
456 cds_start = transcriptome_info_dict[tran]["cds_start"]
|
|
|
457 cds_stop = transcriptome_info_dict[tran]["cds_stop"]
|
|
|
458 except:
|
|
|
459 continue
|
|
|
460
|
|
|
461 tranlen = transcriptome_info_dict[tran]["length"]
|
|
|
462 # Use this to discard transcripts with no 5' leader or 3' trailer
|
|
|
463 if (
|
|
|
464 cds_start > 1
|
|
|
465 and cds_stop < tranlen
|
|
|
466 and transcriptome_info_dict[tran]["tran_type"] == 1
|
|
|
467 ):
|
|
|
468 for primetype in ["fiveprime", "threeprime"]:
|
|
|
469 # Create the triplet periodicity and metainfo plots based on both the 5' and 3' ends of reads
|
|
|
470 for readlength in master_read_dict[tran]["unambig"]:
|
|
|
471 # print "readlength", readlength
|
|
|
472 # for each fiveprime postion for this readlength within this transcript
|
|
|
473 for raw_pos in master_read_dict[tran]["unambig"][readlength]:
|
|
|
474 # print "raw pos", raw_pos
|
|
|
475 trip_periodicity_reads += 1
|
|
|
476 if primetype == "fiveprime":
|
|
|
477 # get the five prime postion minus the cds start postion
|
|
|
478 real_pos = raw_pos - cds_start
|
|
|
479 rel_stop_pos = raw_pos - cds_stop
|
|
|
480 elif primetype == "threeprime":
|
|
|
481 real_pos = (raw_pos + readlength) - cds_start
|
|
|
482 rel_stop_pos = (raw_pos + readlength) - cds_stop
|
|
|
483 # get the readcount at the raw postion
|
|
|
484 readcount = master_read_dict[tran]["unambig"][readlength][
|
|
|
485 raw_pos
|
|
|
486 ]
|
|
|
487 # print "readcount", readcount
|
|
|
488 frame = real_pos % 3
|
|
|
489 if real_pos >= cds_start and real_pos <= cds_stop:
|
|
|
490 if readlength in master_trip_dict[primetype]:
|
|
|
491 master_trip_dict[primetype][readlength][
|
|
|
492 str(frame)
|
|
|
493 ] += readcount
|
|
|
494 else:
|
|
|
495 master_trip_dict[primetype][readlength] = {
|
|
|
496 "0": 0.0,
|
|
|
497 "1": 0.0,
|
|
|
498 "2": 0.0,
|
|
|
499 }
|
|
|
500 master_trip_dict[primetype][readlength][
|
|
|
501 str(frame)
|
|
|
502 ] += readcount
|
|
|
503 # now populate offset dict with the 'real_positions' upstream of cds_start, these will be used for metainfo dict
|
|
|
504 if real_pos > (-600) and real_pos < (601):
|
|
|
505 if readlength in master_offset_dict[primetype]:
|
|
|
506 if (
|
|
|
507 real_pos
|
|
|
508 in master_offset_dict[primetype][readlength]
|
|
|
509 ):
|
|
|
510 # print "real pos in offset dict"
|
|
|
511 master_offset_dict[primetype][readlength][
|
|
|
512 real_pos
|
|
|
513 ] += readcount
|
|
|
514 else:
|
|
|
515 # print "real pos not in offset dict"
|
|
|
516 master_offset_dict[primetype][readlength][
|
|
|
517 real_pos
|
|
|
518 ] = readcount
|
|
|
519 else:
|
|
|
520 # initiliase with zero to avoid missing neighbours below
|
|
|
521 # print "initialising with zeros"
|
|
|
522 master_offset_dict[primetype][readlength] = {}
|
|
|
523 for i in range(-600, 601):
|
|
|
524 master_offset_dict[primetype][readlength][i] = 0
|
|
|
525 master_offset_dict[primetype][readlength][
|
|
|
526 real_pos
|
|
|
527 ] += readcount
|
|
|
528
|
|
|
529 # now populate offset dict with the 'real_positions' upstream of cds_start, these will be used for metainfo dict
|
|
|
530 if rel_stop_pos > (-600) and rel_stop_pos < (601):
|
|
|
531 if readlength in master_metagene_stop_dict[primetype]:
|
|
|
532 if (
|
|
|
533 rel_stop_pos
|
|
|
534 in master_metagene_stop_dict[primetype][readlength]
|
|
|
535 ):
|
|
|
536 master_metagene_stop_dict[primetype][readlength][
|
|
|
537 rel_stop_pos
|
|
|
538 ] += readcount
|
|
|
539 else:
|
|
|
540 master_metagene_stop_dict[primetype][readlength][
|
|
|
541 rel_stop_pos
|
|
|
542 ] = readcount
|
|
|
543 else:
|
|
|
544 # initiliase with zero to avoid missing neighbours below
|
|
|
545 master_metagene_stop_dict[primetype][readlength] = {}
|
|
|
546 for i in range(-600, 601):
|
|
|
547 master_metagene_stop_dict[primetype][readlength][
|
|
|
548 i
|
|
|
549 ] = 0
|
|
|
550 master_metagene_stop_dict[primetype][readlength][
|
|
|
551 rel_stop_pos
|
|
|
552 ] += readcount
|
|
|
553
|
|
|
554 # master trip dict is now made up of readlengths with 3 frames and a count associated with each frame
|
|
|
555 # create a 'score' for each readlength by putting the max frame count over the second highest frame count
|
|
|
556 for primetype in ["fiveprime", "threeprime"]:
|
|
|
557 for subreadlength in master_trip_dict[primetype]:
|
|
|
558 maxcount = 0
|
|
|
559 secondmaxcount = 0
|
|
|
560 for frame in master_trip_dict[primetype][subreadlength]:
|
|
|
561 if master_trip_dict[primetype][subreadlength][frame] > maxcount:
|
|
|
562 maxcount = master_trip_dict[primetype][subreadlength][frame]
|
|
|
563 for frame in master_trip_dict[primetype][subreadlength]:
|
|
|
564 if (
|
|
|
565 master_trip_dict[primetype][subreadlength][frame] > secondmaxcount
|
|
|
566 and master_trip_dict[primetype][subreadlength][frame] != maxcount
|
|
|
567 ):
|
|
|
568 secondmaxcount = master_trip_dict[primetype][subreadlength][frame]
|
|
|
569 # a perfect score would be 0 meaning there is only a single peak, the worst score would be 1 meaning two highest peaks are the same height
|
|
|
570 master_trip_dict[primetype][subreadlength]["score"] = float(
|
|
|
571 secondmaxcount
|
|
|
572 ) / float(maxcount)
|
|
|
573 # This part is to determine what offsets to give each read length
|
|
|
574 print("Calculating offsets")
|
|
|
575 for primetype in ["fiveprime", "threeprime"]:
|
|
|
576 for readlen in master_offset_dict[primetype]:
|
|
|
577 accepted_len = False
|
|
|
578 max_relative_pos = 0
|
|
|
579 max_relative_count = 0
|
|
|
580 for relative_pos in master_offset_dict[primetype][readlen]:
|
|
|
581 # This line is to ensure we don't choose an offset greater than the readlength (in cases of a large peak far up/downstream)
|
|
|
582 if abs(relative_pos) < 10 or abs(relative_pos) > (readlen - 10):
|
|
|
583 continue
|
|
|
584 if (
|
|
|
585 master_offset_dict[primetype][readlen][relative_pos]
|
|
|
586 > max_relative_count
|
|
|
587 ):
|
|
|
588 max_relative_pos = relative_pos
|
|
|
589 max_relative_count = master_offset_dict[primetype][readlen][
|
|
|
590 relative_pos
|
|
|
591 ]
|
|
|
592 # print "for readlen {} the max_relative pos is {}".format(readlen, max_relative_pos)
|
|
|
593 if primetype == "fiveprime":
|
|
|
594 # -3 to get from p-site to a-site, +1 to account for 1 based co-ordinates, resulting in -2 overall
|
|
|
595 final_offsets[primetype]["offsets"][readlen] = abs(max_relative_pos - 2)
|
|
|
596 elif primetype == "threeprime":
|
|
|
597 # +3 to get from p-site to a-site, -1 to account for 1 based co-ordinates, resulting in +2 overall
|
|
|
598 final_offsets[primetype]["offsets"][readlen] = (
|
|
|
599 max_relative_pos * (-1)
|
|
|
600 ) + 2
|
|
|
601 # If there are no reads in CDS regions for a specific length, it may not be present in master_trip_dict
|
|
|
602 if readlen in master_trip_dict[primetype]:
|
|
|
603 final_offsets[primetype]["read_scores"][readlen] = master_trip_dict[
|
|
|
604 primetype
|
|
|
605 ][readlen]["score"]
|
|
|
606 else:
|
|
|
607 final_offsets[primetype]["read_scores"][readlen] = 0.0
|
|
|
608 master_read_dict["offsets"] = final_offsets
|
|
|
609 master_read_dict["trip_periodicity"] = master_trip_dict
|
|
|
610 master_read_dict["desc"] = "Null"
|
|
|
611 master_read_dict["mapped_reads"] = mapped_reads
|
|
|
612 master_read_dict["nuc_counts"] = nuc_count_dict
|
|
|
613 master_read_dict["dinuc_counts"] = dinuc_count_dict
|
|
|
614 master_read_dict["threeprime_nuc_counts"] = threeprime_nuc_count_dict
|
|
|
615 master_read_dict["metagene_counts"] = master_offset_dict
|
|
|
616 master_read_dict["stop_metagene_counts"] = master_metagene_stop_dict
|
|
|
617 master_read_dict["read_lengths"] = read_length_dict
|
|
|
618 master_read_dict["unambig_read_lengths"] = unambig_read_length_dict
|
|
|
619 master_read_dict["coding_counts"] = master_dict["unambiguous_coding_count"]
|
|
|
620 master_read_dict["noncoding_counts"] = master_dict["unambiguous_non_coding_count"]
|
|
|
621 master_read_dict["ambiguous_counts"] = master_dict["ambiguously_mapped_reads"]
|
|
|
622 master_read_dict["frequent_unmapped_reads"] = (
|
|
|
623 sorted(unmapped_dict.items(), key=operator.itemgetter(1))
|
|
|
624 )[-2000:]
|
|
|
625 master_read_dict["cutadapt_removed"] = 0
|
|
|
626 master_read_dict["rrna_removed"] = 0
|
|
|
627 # If no reads are removed by minus m there won't be an entry in the log file, so initiliase with 0 first and change if there is a line
|
|
|
628 master_read_dict["removed_minus_m"] = 0
|
|
|
629 master_dict["removed_minus_m"] = 0
|
|
|
630 # We work out the total counts for 5', cds 3' for differential translation here, would be better to do thisn in processor but need the offsets
|
|
|
631 master_read_dict["unambiguous_all_totals"] = {}
|
|
|
632 master_read_dict["unambiguous_fiveprime_totals"] = {}
|
|
|
633 master_read_dict["unambiguous_cds_totals"] = {}
|
|
|
634 master_read_dict["unambiguous_threeprime_totals"] = {}
|
|
|
635
|
|
|
636 master_read_dict["ambiguous_all_totals"] = {}
|
|
|
637 master_read_dict["ambiguous_fiveprime_totals"] = {}
|
|
|
638 master_read_dict["ambiguous_cds_totals"] = {}
|
|
|
639 master_read_dict["ambiguous_threeprime_totals"] = {}
|
|
|
640 print("calculating transcript counts")
|
|
|
641 for tran in master_read_dict:
|
|
|
642 if tran in transcriptome_info_dict:
|
|
|
643 five_total = 0
|
|
|
644 cds_total = 0
|
|
|
645 three_total = 0
|
|
|
646
|
|
|
647 ambig_five_total = 0
|
|
|
648 ambig_cds_total = 0
|
|
|
649 ambig_three_total = 0
|
|
|
650
|
|
|
651 cds_start = transcriptome_info_dict[tran]["cds_start"]
|
|
|
652 cds_stop = transcriptome_info_dict[tran]["cds_stop"]
|
|
|
653 for readlen in master_read_dict[tran]["unambig"]:
|
|
|
654 if readlen in final_offsets["fiveprime"]["offsets"]:
|
|
|
655 offset = final_offsets["fiveprime"]["offsets"][readlen]
|
|
|
656 else:
|
|
|
657 offset = 15
|
|
|
658 for pos in master_read_dict[tran]["unambig"][readlen]:
|
|
|
659 real_pos = pos + offset
|
|
|
660 if real_pos < cds_start:
|
|
|
661 five_total += master_read_dict[tran]["unambig"][readlen][pos]
|
|
|
662 elif real_pos >= cds_start and real_pos <= cds_stop:
|
|
|
663 cds_total += master_read_dict[tran]["unambig"][readlen][pos]
|
|
|
664 elif real_pos > cds_stop:
|
|
|
665 three_total += master_read_dict[tran]["unambig"][readlen][pos]
|
|
|
666 master_read_dict["unambiguous_all_totals"][tran] = (
|
|
|
667 five_total + cds_total + three_total
|
|
|
668 )
|
|
|
669 master_read_dict["unambiguous_fiveprime_totals"][tran] = five_total
|
|
|
670 master_read_dict["unambiguous_cds_totals"][tran] = cds_total
|
|
|
671 master_read_dict["unambiguous_threeprime_totals"][tran] = three_total
|
|
|
672
|
|
|
673 for readlen in master_read_dict[tran]["ambig"]:
|
|
|
674 if readlen in final_offsets["fiveprime"]["offsets"]:
|
|
|
675 offset = final_offsets["fiveprime"]["offsets"][readlen]
|
|
|
676 else:
|
|
|
677 offset = 15
|
|
|
678 for pos in master_read_dict[tran]["ambig"][readlen]:
|
|
|
679 real_pos = pos + offset
|
|
|
680 if real_pos < cds_start:
|
|
|
681 ambig_five_total += master_read_dict[tran]["ambig"][readlen][
|
|
|
682 pos
|
|
|
683 ]
|
|
|
684 elif real_pos >= cds_start and real_pos <= cds_stop:
|
|
|
685 ambig_cds_total += master_read_dict[tran]["ambig"][readlen][pos]
|
|
|
686 elif real_pos > cds_stop:
|
|
|
687 ambig_three_total += master_read_dict[tran]["ambig"][readlen][
|
|
|
688 pos
|
|
|
689 ]
|
|
|
690
|
|
|
691 master_read_dict["ambiguous_all_totals"][tran] = (
|
|
|
692 five_total
|
|
|
693 + cds_total
|
|
|
694 + three_total
|
|
|
695 + ambig_five_total
|
|
|
696 + ambig_cds_total
|
|
|
697 + ambig_three_total
|
|
|
698 )
|
|
|
699 master_read_dict["ambiguous_fiveprime_totals"][tran] = (
|
|
|
700 five_total + ambig_five_total
|
|
|
701 )
|
|
|
702 master_read_dict["ambiguous_cds_totals"][tran] = cds_total + ambig_cds_total
|
|
|
703 master_read_dict["ambiguous_threeprime_totals"][tran] = (
|
|
|
704 three_total + ambig_three_total
|
|
|
705 )
|
|
|
706 print("Writing out to sqlite file")
|
|
|
707 sqlite_db = SqliteDict(outputfile, autocommit=False)
|
|
|
708 for key in master_read_dict:
|
|
|
709 sqlite_db[key] = master_read_dict[key]
|
|
|
710 sqlite_db["description"] = desc
|
|
|
711 sqlite_db.commit()
|
|
|
712 sqlite_db.close()
|
|
|
713
|
|
|
714
|
|
|
715 if __name__ == "__main__":
|
|
|
716 if len(sys.argv) <= 2:
|
|
|
717 print(
|
|
|
718 "Usage: python bam_to_sqlite.py <path_to_bam_file> <path_to_organism.sqlite> <file_description (optional)>"
|
|
|
719 )
|
|
|
720 sys.exit()
|
|
|
721 bam_filepath = sys.argv[1]
|
|
|
722 annotation_sqlite_filepath = sys.argv[2]
|
|
|
723 # try:
|
|
|
724 # desc = sys.argv[3]
|
|
|
725 # except:
|
|
|
726 # desc = bam_filepath.split("/")[-1]
|
|
|
727 outputfile = bam_filepath + "v2.sqlite"
|
|
|
728 process_bam(bam_filepath, annotation_sqlite_filepath, outputfile)
|
