annotate pep_pointer.py @ 0:032f1ee171ed draft

planemo upload
author pravs
date Fri, 22 Dec 2017 04:33:12 -0500
parents
children
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1
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2 #
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3 # Author: Praveen Kumar
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4 # Updated: Nov 8th, 2017
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5 #
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6 #
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7 #
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8
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9 import re
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10
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11
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12 def main():
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13 import sys
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14 if len(sys.argv) == 4:
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15 inputFile = sys.argv
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16 infh = open(inputFile[1], "r")
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17 # infh = open("Mus_musculus.GRCm38.90.chr.gtf", "r")
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18
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19 gtf = {}
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20 gtf_transcript = {}
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21 gtf_gene = {}
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22 for each in infh.readlines():
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23 a = each.split("\t")
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24 if re.search("^[^#]", each):
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25 if re.search("gene_biotype \"protein_coding\"", a[8]) and int(a[4].strip()) != int(a[3].strip()):
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26 type = a[2].strip()
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27 if type == "gene" or type == "exon" or type == "CDS" or type == "five_prime_utr" or type == "three_prime_utr":
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28 chr = "chr" + a[0].strip()
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29 strand = a[6].strip()
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30 if strand == "+":
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31 start = a[3].strip()
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32 end = a[4].strip()
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33 elif strand == "-":
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34 if int(a[4].strip()) > int(a[3].strip()):
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35 start = a[3].strip()
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36 end = a[4].strip()
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37 elif int(a[4].strip()) < int(a[3].strip()):
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38 start = a[4].strip()
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39 end = a[3].strip()
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40 else:
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41 print "Something fishy in start end coordinates"
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42 else:
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43 print "Something fishy in reading"
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44 if not gtf.has_key(strand):
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45 gtf[strand] = {}
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46 if not gtf[strand].has_key(type):
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47 gtf[strand][type] = []
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48 b = re.search("gene_id \"(.+?)\";", a[8].strip())
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49 gene = b.group(1)
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50 if type == "gene":
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51 transcript = ""
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52 else:
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53 b = re.search("transcript_id \"(.+?)\";", a[8].strip())
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54 transcript = b.group(1)
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55 data = (chr, start, end, gene, transcript, strand, type)
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56 gtf[strand][type].append(data)
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57
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58 if type == "exon":
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59 if gtf_transcript.has_key(chr+"#"+strand):
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60 if gtf_transcript[chr+"#"+strand].has_key(transcript+"#"+gene):
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61 gtf_transcript[chr+"#"+strand][transcript+"#"+gene][0].append(int(start))
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62 gtf_transcript[chr+"#"+strand][transcript+"#"+gene][1].append(int(end))
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63 else:
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64 gtf_transcript[chr+"#"+strand][transcript+"#"+gene] = [[],[]]
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65 gtf_transcript[chr+"#"+strand][transcript+"#"+gene][0].append(int(start))
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66 gtf_transcript[chr+"#"+strand][transcript+"#"+gene][1].append(int(end))
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67 else:
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68 gtf_transcript[chr+"#"+strand] = {}
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69 gtf_transcript[chr+"#"+strand][transcript+"#"+gene] = [[],[]]
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70 gtf_transcript[chr+"#"+strand][transcript+"#"+gene][0].append(int(start))
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71 gtf_transcript[chr+"#"+strand][transcript+"#"+gene][1].append(int(end))
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72
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73 if type == "gene":
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74 if gtf_gene.has_key(chr+"#"+strand):
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75 gtf_gene[chr+"#"+strand][0].append(int(start))
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76 gtf_gene[chr+"#"+strand][1].append(int(end))
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77 gtf_gene[chr+"#"+strand][2].append(gene)
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78 else:
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79 gtf_gene[chr+"#"+strand] = [[0],[0],["no_gene"]]
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80 gtf_gene[chr+"#"+strand][0].append(int(start))
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81 gtf_gene[chr+"#"+strand][1].append(int(end))
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82 gtf_gene[chr+"#"+strand][2].append(gene)
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83
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84
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85
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86 # "Starting Reading Intron . . ."
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87
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88 gtf["+"]["intron"] = []
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89 gtf["-"]["intron"] = []
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90 for chr_strand in gtf_transcript.keys():
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91 chr = chr_strand.split("#")[0]
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92 strand = chr_strand.split("#")[1]
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93
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94 for transcript_gene in gtf_transcript[chr_strand].keys():
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95 start_list = gtf_transcript[chr_strand][transcript_gene][0]
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96 end_list = gtf_transcript[chr_strand][transcript_gene][1]
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97 sorted_start_index = [i[0] for i in sorted(enumerate(start_list), key=lambda x:x[1])]
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98 sorted_end_index = [i[0] for i in sorted(enumerate(end_list), key=lambda x:x[1])]
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99 if sorted_start_index == sorted_end_index:
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100 sorted_start = sorted(start_list)
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101 sorted_end = [end_list[i] for i in sorted_start_index]
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102 for x in range(len(sorted_start))[1:]:
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103 intron_start = sorted_end[x-1]+1
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104 intron_end = sorted_start[x]-1
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105 transcript = transcript_gene.split("#")[0]
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106 gene = transcript_gene.split("#")[1]
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107 data = (chr, str(intron_start), str(intron_end), gene, transcript, strand, "intron")
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108 gtf[strand]["intron"].append(data)
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109
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110
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111 # "Starting Reading Intergenic . . ."
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112
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113 gtf["+"]["intergenic"] = []
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114 gtf["-"]["intergenic"] = []
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115 for chr_strand in gtf_gene.keys():
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116 chr = chr_strand.split("#")[0]
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117 strand = chr_strand.split("#")[1]
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118 start_list = gtf_gene[chr_strand][0]
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119 end_list = gtf_gene[chr_strand][1]
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120 gene_list = gtf_gene[chr_strand][2]
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121 sorted_start_index = [i[0] for i in sorted(enumerate(start_list), key=lambda x:x[1])]
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122 sorted_end_index = [i[0] for i in sorted(enumerate(end_list), key=lambda x:x[1])]
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123
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124 sorted_start = sorted(start_list)
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125 sorted_end = [end_list[i] for i in sorted_start_index]
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126 sorted_gene = [gene_list[i] for i in sorted_start_index]
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127 for x in range(len(sorted_start))[1:]:
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128 intergene_start = sorted_end[x-1]+1
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129 intergene_end = sorted_start[x]-1
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130 if intergene_start < intergene_end:
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131 intergene_1 = sorted_gene[x-1]
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132 intergene_2 = sorted_gene[x]
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133 gene = intergene_1 + "-#-" + intergene_2
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134 data = (chr, str(intergene_start), str(intergene_end), gene, "", strand, "intergenic")
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135 gtf[strand]["intergenic"].append(data)
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136
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137 import sqlite3
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138 # conn = sqlite3.connect('gtf_database.db')
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139 conn = sqlite3.connect(":memory:")
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140 c = conn.cursor()
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141 # c.execute("DROP TABLE IF EXISTS gtf_data;")
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142 # c.execute("CREATE TABLE IF NOT EXISTS gtf_data(chr text, start int, end int, gene text, transcript text, strand text, type text)")
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143 c.execute("CREATE TABLE gtf_data(chr text, start int, end int, gene text, transcript text, strand text, type text)")
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144
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145 for strand in gtf.keys():
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146 if strand == "+":
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147 st = "positive"
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148 elif strand == "-":
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149 st = "negative"
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150 else:
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151 print "Something fishy in writing . . ."
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152
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153 for type in gtf[strand].keys():
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154 data = gtf[strand][type]
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155 c.executemany('INSERT INTO gtf_data VALUES (?,?,?,?,?,?,?)', data)
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156
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157 conn.commit()
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158
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159 infh = open(inputFile[2], "r")
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160 # infh = open("Mouse_Data_All_peptides_withNewDBs.txt", "r")
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161 data = infh.readlines()
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162 # output file
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163 outfh = open(inputFile[3], 'w')
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164 # outfh = open("classified_1_Mouse_Data_All_peptides_withNewDBs.txt", "w")
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165
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166 for each in data:
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167 a = each.split("\t")
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168 chr = a[0].strip()
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169 pep_start = a[1].strip()
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170 pep_end = a[2].strip()
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171 strand = a[5].strip()
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172 c.execute("select * from gtf_data where type = 'CDS' and chr = '"+chr+"' and start <= "+pep_start+" and end >= "+pep_end+" and strand = '"+strand+"' ")
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173 rows = c.fetchall()
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174 if len(rows) > 0:
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175 outfh.write(each.strip() + "\tCDS\n")
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176 else:
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177 c.execute("select * from gtf_data where type = 'five_prime_utr' and chr = '"+chr+"' and start <= "+pep_start+" and end >= "+pep_end+" and strand = '"+strand+"' ")
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178 rows = c.fetchall()
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179 if len(rows) > 0:
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180 outfh.write(each.strip() + "\tfive_prime_utr\n")
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181 else:
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182 c.execute("select * from gtf_data where type = 'three_prime_utr' and chr = '"+chr+"' and start <= "+pep_start+" and end >= "+pep_end+" and strand = '"+strand+"' ")
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183 rows = c.fetchall()
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184 if len(rows) > 0:
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185 outfh.write(each.strip() + "\tthree_prime_utr\n")
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186 else:
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187 c.execute("select * from gtf_data where type = 'exon' and chr = '"+chr+"' and start <= "+pep_start+" and end >= "+pep_end+" and strand = '"+strand+"' ")
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188 rows = c.fetchall()
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189 if len(rows) > 0:
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190 outfh.write(each.strip() + "\texon\n")
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191 else:
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192 c.execute("select * from gtf_data where type = 'intron' and chr = '"+chr+"' and start <= "+pep_start+" and end >= "+pep_end+" and strand = '"+strand+"' ")
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193 rows = c.fetchall()
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194 if len(rows) > 0:
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195 outfh.write(each.strip() + "\tintron\n")
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196 else:
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197 c.execute("select * from gtf_data where type = 'gene' and chr = '"+chr+"' and start <= "+pep_start+" and end >= "+pep_end+" and strand = '"+strand+"' ")
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198 rows = c.fetchall()
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199 if len(rows) > 0:
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200 outfh.write(each.strip() + "\tgene\n")
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pravs
parents:
diff changeset
201 else:
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pravs
parents:
diff changeset
202 c.execute("select * from gtf_data where type = 'intergenic' and chr = '"+chr+"' and start <= "+pep_start+" and end >= "+pep_end+" and strand = '"+strand+"' ")
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pravs
parents:
diff changeset
203 rows = c.fetchall()
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pravs
parents:
diff changeset
204 if len(rows) > 0:
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pravs
parents:
diff changeset
205 outfh.write(each.strip() + "\tintergene\n")
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pravs
parents:
diff changeset
206 else:
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pravs
parents:
diff changeset
207 outfh.write(each.strip() + "\tOVERLAPPING_ON_TWO_REGIONS: PLEASE_LOOK_MANUALLY (Will be updated in next version)\n")
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pravs
parents:
diff changeset
208
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pravs
parents:
diff changeset
209 conn.close()
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pravs
parents:
diff changeset
210 outfh.close()
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pravs
parents:
diff changeset
211 else:
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pravs
parents:
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212 print "USAGE: python pep_pointer.py <input GTF file> <input tblastn file> <name of output file>"
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pravs
parents:
diff changeset
213 return None
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pravs
parents:
diff changeset
214
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pravs
parents:
diff changeset
215 if __name__ == "__main__":
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pravs
parents:
diff changeset
216 main()
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pravs
parents:
diff changeset
217
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pravs
parents:
diff changeset
218
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pravs
parents:
diff changeset
219
032f1ee171ed planemo upload
pravs
parents:
diff changeset
220
032f1ee171ed planemo upload
pravs
parents:
diff changeset
221