Mercurial > repos > petr-novak > repeat_annotation_pipeline3

annotate reAnnotate.py @ 11:5366d5ea04bc draft

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author petr-novak
date Fri, 04 Aug 2023 12:35:32 +0000
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11
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1 #!/usr/bin/env python
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2 """
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3 parse blast output table to gff file
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4 """
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5 import argparse
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6 import itertools
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7 import os
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8 import re
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9 import shutil
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10 import subprocess
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11 import sys
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12 import tempfile
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13 from collections import defaultdict
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14
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15 # check version of python, must be at least 3.7
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16 if sys.version_info < (3, 10):
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17 sys.exit("Python 3.10 or a more recent version is required.")
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18
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19 def make_temp_files(number_of_files):
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20 """
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21 Make named temporary files, file will not be deleted upon exit!
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22 :param number_of_files:
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23 :return:
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24 filepaths
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25 """
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26 temp_files = []
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27 for i in range(number_of_files):
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28 temp_files.append(tempfile.NamedTemporaryFile(delete=False).name)
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29 os.remove(temp_files[-1])
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30 return temp_files
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31
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32
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33 def split_fasta_to_chunks(fasta_file, chunk_size=100000000, overlap=100000):
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34 """
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35 Split fasta file to chunks, sequences longe than chuck size are split to overlaping
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36 peaces. If sequences are shorter, chunck with multiple sequences are created.
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37 :param fasta_file:
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38
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39 :param fasta_file:
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40 :param chunk_size:
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41 :param overlap:
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42 :return:
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43 fasta_file_split
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44 matching_table (list of lists [header,chunk_number, start, end, new_header])
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45 """
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46 min_chunk_size = chunk_size * 2
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47 fasta_sizes_dict = read_fasta_sequence_size(fasta_file)
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48 # calculate size of items in fasta_dist dictionary
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49 fasta_size = sum(fasta_sizes_dict.values())
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50
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51 # calculates ranges for splitting of fasta files and store them in list
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52 matching_table = []
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53 fasta_file_split = tempfile.NamedTemporaryFile(delete=False).name
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54 for header, size in fasta_sizes_dict.items():
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55 print(header, size, min_chunk_size)
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56
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57 if size > min_chunk_size:
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58 number_of_chunks = int(size / chunk_size)
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59 print("number_of_chunks", number_of_chunks)
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60 print("size", size)
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61 print("chunk_size", chunk_size)
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62 print("-----------------------------------------")
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63 adjusted_chunk_size = int(size / number_of_chunks)
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64 for i in range(number_of_chunks):
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65 start = i * adjusted_chunk_size
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66 end = ((i + 1) *
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67 adjusted_chunk_size
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68 + overlap) if i + 1 < number_of_chunks else size
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69 new_header = header + '_' + str(i)
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70 matching_table.append([header, i, start, end, new_header])
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71 else:
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72 new_header = header + '_0'
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73 matching_table.append([header, 0, 0, size, new_header])
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74 # read sequences from fasta files and split them to chunks according to matching table
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75 # open output and input files, use with statement to close files
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76 number_of_temp_files = len(matching_table)
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77 print('number of temp files', number_of_temp_files)
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78 fasta_dict = read_single_fasta_to_dictionary(open(fasta_file, 'r'))
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79 with open(fasta_file_split, 'w') as fh_out:
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80 for header in fasta_dict:
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81 matching_table_part = [x for x in matching_table if x[0] == header]
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82 for header2, i, start, end, new_header in matching_table_part:
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83 fh_out.write('>' + new_header + '\n')
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84 fh_out.write(fasta_dict[header][start:end] + '\n')
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85 temp_files_fasta = make_temp_files(number_of_temp_files)
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86 fasta_seq_size = read_fasta_sequence_size(fasta_file_split)
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87 seq_id_size_sorted = [i[0] for i in sorted(
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88 fasta_seq_size.items(), key=lambda x: int(x[1]), reverse=True
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89 )]
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90 seq_id_file_dict = dict(zip(seq_id_size_sorted, itertools.cycle(temp_files_fasta)))
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91 # write sequences to temporary files
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92 with open(fasta_file_split, 'r') as f:
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93 first = True
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94 for line in f:
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95 if line[0] == '>':
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96 # close previous file if it is not the first sequence
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97 if not first:
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98 fout.close()
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99 first = False
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100 header = line.strip().split(' ')[0][1:]
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101 fout = open(seq_id_file_dict[header],'a')
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102 fout.write(line)
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103 else:
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104 fout.write(line)
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105 os.remove(fasta_file_split)
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106 return temp_files_fasta, matching_table
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107
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108
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109 def read_fasta_sequence_size(fasta_file):
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110 """Read size of sequence into dictionary"""
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111 fasta_dict = {}
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112 with open(fasta_file, 'r') as f:
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113 for line in f:
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114 if line[0] == '>':
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115 header = line.strip().split(' ')[0][1:] # remove part of name after space
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116 fasta_dict[header] = 0
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117 else:
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118 fasta_dict[header] += len(line.strip())
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119 return fasta_dict
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120
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121
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122 def read_single_fasta_to_dictionary(fh):
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123 """
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124 Read fasta file into dictionary
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125 :param fh:
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126 :return:
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127 fasta_dict
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128 """
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129 fasta_dict = {}
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130 for line in fh:
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131 if line[0] == '>':
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132 header = line.strip().split(' ')[0][1:] # remove part of name after space
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133 fasta_dict[header] = []
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134 else:
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135 fasta_dict[header] += [line.strip()]
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136 fasta_dict = {k: ''.join(v) for k, v in fasta_dict.items()}
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137 return fasta_dict
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138
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139
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140 def overlap(a, b):
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141 """
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142 check if two intervals overlap
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143 """
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144 return max(a[0], b[0]) <= min(a[1], b[1])
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145
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146
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147 def blast2disjoint(
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148 blastfile, seqid_counts=None, start_column=6, end_column=7, class_column=1,
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149 bitscore_column=11, pident_column=2, canonical_classification=True
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150 ):
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151 """
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152 find all interval beginning and ends in blast file and create bed file
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153 input blastfile is tab separated file with columns:
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154 'qaccver saccver pident length mismatch gapopen qstart qend sstart send
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155 evalue bitscore' (default outfmt 6
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156 blast must be sorted on qseqid and qstart
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157 """
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158 # assume all in one chromosome!
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159 starts_ends = {}
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160 intervals = {}
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161 if canonical_classification:
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162 # make regular expression for canonical classification
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163 # to match: Name#classification
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164 # e.g. "Name_of_sequence#LTR/Ty1_copia/Angela"
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165 regex = re.compile(r"(.*)[#](.*)")
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166 group = 2
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167 else:
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168 # make regular expression for non-canonical classification
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169 # to match: Classification__Name
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170 # e.g. "LTR/Ty1_copia/Angela__Name_of_sequence"
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171 regex = re.compile(r"(.*)__(.*)")
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172 group = 1
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173
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174 # identify continuous intervals
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175 with open(blastfile, "r") as f:
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176 for seqid in sorted(seqid_counts.keys()):
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177 n_lines = seqid_counts[seqid]
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178 starts_ends[seqid] = set()
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179 for i in range(n_lines):
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180 items = f.readline().strip().split()
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181 # note 1s and 2s labels are used to distinguish between start and end and
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182 # guarantee that with same coordinated start will be before end when
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183 # sorting (1s < 2e)
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184 starts_ends[seqid].add((int(items[start_column]), '1s'))
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185 starts_ends[seqid].add((int(items[end_column]), '2e'))
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186 intervals[seqid] = []
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187 for p1, p2 in itertools.pairwise(sorted(starts_ends[seqid])):
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188 if p1[1] == '1s':
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189 sp = 0
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190 else:
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191 sp = 1
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192 if p2[1] == '2e':
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193 ep = 0
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194 else:
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195 ep = 1
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196 intervals[seqid].append((p1[0] + sp, p2[0] - ep))
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197 # scan each blast hit against continuous region and record hit with best score
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198 with open(blastfile, "r") as f:
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199 disjoint_regions = []
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200 for seqid in sorted(seqid_counts.keys()):
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201 n_lines = seqid_counts[seqid]
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202 idx_of_overlaps = {}
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203 best_pident = defaultdict(lambda: 0.0)
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204 best_bitscore = defaultdict(lambda: 0.0)
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205 best_hit_name = defaultdict(lambda: "")
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206 i1 = 0
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207 for i in range(n_lines):
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208 items = f.readline().strip().split()
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209 start = int(items[start_column])
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210 end = int(items[end_column])
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211 pident = float(items[pident_column])
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212 bitscore = float(items[bitscore_column])
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213 classification = items[class_column]
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214 j = 0
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215 done = False
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216 while True:
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217 # beginning of searched region - does it overlap?
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218 c_ovl = overlap(intervals[seqid][i1], (start, end))
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219 if c_ovl:
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220 # if overlap is detected, add to dictionary
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221 idx_of_overlaps[i] = [i1]
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222 if best_bitscore[i1] < bitscore:
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223 best_pident[i1] = pident
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224 best_bitscore[i1] = bitscore
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225 best_hit_name[i1] = classification
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226 # add search also downstream
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diff changeset
227 while True:
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228 j += 1
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229 if j + i1 >= len(intervals[seqid]):
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230 done = True
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231 break
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232 c_ovl = overlap(intervals[seqid][i1 + j], (start, end))
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233 if c_ovl:
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234 idx_of_overlaps[i].append(i1 + j)
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235 if best_bitscore[i1 + j] < bitscore:
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236 best_pident[i1 + j] = pident
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237 best_bitscore[i1 + j] = bitscore
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238 best_hit_name[i1 + j] = classification
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239 else:
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240 done = True
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241 break
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242
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243 else:
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244 # does no overlap - search next interval
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245 i1 += 1
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246 if done or i1 >= (len(intervals[seqid]) - 1):
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247 break
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248
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249 for i in sorted(best_pident.keys()):
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250 try:
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251 classification = re.match(regex, best_hit_name[i]).group(group)
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252 except AttributeError:
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253 classification = best_hit_name[i]
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254 record = (
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255 seqid, intervals[seqid][i][0], intervals[seqid][i][1], best_pident[i],
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256 classification)
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257 disjoint_regions.append(record)
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258 return disjoint_regions
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259
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260
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261 def remove_short_interrupting_regions(regions, min_len=10, max_gap=2):
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262 """
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263 remove intervals shorter than min_len which are directly adjacent to other
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264 regions on both sides which are longer than min_len and has same classification
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265 """
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266 regions_to_remove = []
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267 for i in range(1, len(regions) - 1):
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268 if regions[i][2] - regions[i][1] < min_len:
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269 c1 = regions[i - 1][2] - regions[i - 1][1] > min_len
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270 c2 = regions[i + 1][2] - regions[i + 1][1] > min_len
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271 c3 = regions[i - 1][4] == regions[i + 1][4] # same classification
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272 c4 = regions[i + 1][4] != regions[i][4] # different classification
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273 c5 = regions[i][1] - regions[i - 1][2] < max_gap # max gap between regions
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274 c6 = regions[i + 1][1] - regions[i][2] < max_gap # max gap between regions
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275 if c1 and c2 and c3 & c4 and c5 and c6:
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276 regions_to_remove.append(i)
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277 for i in sorted(regions_to_remove, reverse=True):
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278 del regions[i]
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279 return regions
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280
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281
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282 def remove_short_regions(regions, min_l_score=600):
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283 """
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284 remove intervals shorter than min_len
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285 min_l_score is the minimum score for a region to be considered
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286 l_score = length * PID
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287 """
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288 regions_to_remove = []
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289 for i in range(len(regions)):
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290 l_score = (regions[i][3] - 50) * (regions[i][2] - regions[i][1])
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291 if l_score < min_l_score:
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292 regions_to_remove.append(i)
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293 for i in sorted(regions_to_remove, reverse=True):
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294 del regions[i]
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295 return regions
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296
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297
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298 def join_disjoint_regions_by_classification(disjoint_regions, max_gap=0):
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299 """
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300 merge neighboring intervals with same classification and calculate mean weighted score
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301 weight correspond to length of the interval
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302 """
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303 merged_regions = []
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304 for seqid, start, end, score, classification in disjoint_regions:
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305 score_length = (end - start + 1) * score
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306 if len(merged_regions) == 0:
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307 merged_regions.append([seqid, start, end, score_length, classification])
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308 else:
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diff changeset
309 cond_same_class = merged_regions[-1][4] == classification
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310 cond_same_seqid = merged_regions[-1][0] == seqid
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311 cond_neighboring = start - merged_regions[-1][2] + 1 <= max_gap
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312 if cond_same_class and cond_same_seqid and cond_neighboring:
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313 # extend region
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314 merged_regions[-1] = [merged_regions[-1][0], merged_regions[-1][1], end,
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315 merged_regions[-1][3] + score_length,
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diff changeset
316 merged_regions[-1][4]]
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diff changeset
317 else:
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318 merged_regions.append([seqid, start, end, score_length, classification])
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319 # recalculate length weighted score
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diff changeset
320 for record in merged_regions:
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321 record[3] = record[3] / (record[2] - record[1] + 1)
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322 return merged_regions
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323
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diff changeset
324
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325 def write_merged_regions_to_gff3(merged_regions, outfile):
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326 """
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diff changeset
327 write merged regions to gff3 file
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328 """
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329 with open(outfile, "w") as f:
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330 # write header
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331 f.write("##gff-version 3\n")
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332 for seqid, start, end, score, classification in merged_regions:
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333 attributes = "Name={};score={}".format(classification, score)
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334 f.write(
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335 "\t".join(
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336 [seqid, "blast_parsed", "repeat_region", str(start), str(end),
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337 str(round(score,2)), ".", ".", attributes]
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338 )
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339 )
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340 f.write("\n")
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341
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342
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343 def sort_blast_table(
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344 blastfile, seqid_column=0, start_column=6, cpu=1
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345 ):
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346 """
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347 split blast table by seqid and sort by start position
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348 stores output in temp files
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349 columns are indexed from 0
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350 but cut uses 1-based indexing!
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351 """
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352 blast_sorted = tempfile.NamedTemporaryFile().name
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353 # create sorted dictionary seqid counts
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354 seq_id_counts = {}
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355 # sort blast file on disk using sort on seqid and start (numeric) position columns
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356 # using sort command as blast output could be very large
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357 cmd = "sort -k {0},{0} -k {1},{1}n --parallel {4} {2} > {3}".format(
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358 seqid_column + 1, start_column + 1, blastfile, blast_sorted, cpu
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359 )
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360 subprocess.check_call(cmd, shell=True)
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361
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362 # count seqids using uniq command
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363 cmd = "cut -f {0} {1} | uniq -c > {2}".format(
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364 seqid_column + 1, blast_sorted, blast_sorted + ".counts"
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365 )
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366 subprocess.check_call(cmd, shell=True)
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367 # read counts file and create dictionary
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368 with open(blast_sorted + ".counts", "r") as f:
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369 for line in f:
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370 line = line.strip().split()
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371 seq_id_counts[line[1]] = int(line[0])
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372 # remove counts file
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373 subprocess.call(["rm", blast_sorted + ".counts"])
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374 # return sorted dictionary and sorted blast file
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375 return seq_id_counts, blast_sorted
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376
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377
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378 def run_blastn(
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379 query, db, blastfile, evalue=1e-3, max_target_seqs=999999999, gapopen=2,
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380 gapextend=1, reward=1, penalty=-1, word_size=9, num_threads=1, outfmt="6"
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381 ):
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382 """
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383 run blastn
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384 """
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385 # create temporary blast database:
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386 db_formated = tempfile.NamedTemporaryFile().name
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387 cmd = "makeblastdb -in {0} -dbtype nucl -out {1}".format(db, db_formated)
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388 subprocess.check_call(cmd, shell=True)
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389 # if query is smaller than 1GB, run blast on single file
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390 size = os.path.getsize(query)
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391 print("query size: {} bytes".format(size))
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392 max_size = 1e6
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393 overlap = 50000
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394 if size < max_size:
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395 cmd = ("blastn -task rmblastn -query {0} -db {1} -out {2} -evalue {3} "
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396 "-max_target_seqs {4} "
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397 "-gapopen {5} -gapextend {6} -word_size {7} -num_threads "
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398 "{8} -outfmt '{9}' -reward {10} -penalty {11} -dust no").format(
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399 query, db_formated, blastfile, evalue, max_target_seqs, gapopen, gapextend,
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400 word_size, num_threads, outfmt, reward, penalty
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401 )
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402 subprocess.check_call(cmd, shell=True)
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403 # if query is larger than 1GB, split query in chunks and run blast on each chunk
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404 else:
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405 print(f"query is larger than {max_size}, splitting query in chunks")
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406 query_parts, matching_table = split_fasta_to_chunks(query, max_size, overlap)
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407 print(query_parts)
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408 for i, part in enumerate(query_parts):
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409 print(f"running blast on chunk {i}")
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410 print(part)
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411 cmd = ("blastn -task rmblastn -query {0} -db {1} -out {2} -evalue {3} "
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412 "-max_target_seqs {4} "
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413 "-gapopen {5} -gapextend {6} -word_size {7} -num_threads "
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414 "{8} -outfmt '{9}' -reward {10} -penalty {11} -dust no").format(
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415 part, db_formated, f'{blastfile}.{i}', evalue, max_target_seqs, gapopen,
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416 gapextend,
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417 word_size, num_threads, outfmt, reward, penalty
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418 )
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419 subprocess.check_call(cmd, shell=True)
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420 print(cmd)
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421 # remove part file
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422 # os.unlink(part)
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423 # merge blast results and recalculate start, end positions and header
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424 merge_blast_results(blastfile, matching_table, n_parts=len(query_parts))
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425
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426 # remove temporary blast database
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427 os.unlink(db_formated + ".nhr")
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428 os.unlink(db_formated + ".nin")
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429 os.unlink(db_formated + ".nsq")
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430
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431 def merge_blast_results(blastfile, matching_table, n_parts):
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432 """
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433 Merge blast tables and recalculate start, end positions based on
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434 matching table
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435 """
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436 with open(blastfile, "w") as f:
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437 matching_table_dict = {i[4]: i for i in matching_table}
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438 print(matching_table_dict)
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439 for i in range(n_parts):
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440 with open(f'{blastfile}.{i}', "r") as f2:
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441 for line in f2:
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442 line = line.strip().split("\t")
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443 # seqid (header) is in column 1
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444 seqid = line[0]
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445 line[0] = matching_table_dict[seqid][0]
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446 # increase coordinates by start position of chunk
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447 line[6] = str(int(line[6]) + matching_table_dict[seqid][2])
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448 line[7] = str(int(line[7]) + matching_table_dict[seqid][2])
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449 f.write("\t".join(line) + "\n")
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450 # remove temporary blast file
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451 # os.unlink(f'{blastfile}.{i}')
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452
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453 def main():
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454 """
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455 main function
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456 """
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457 # get command line arguments
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458 parser = argparse.ArgumentParser(
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459 description="""This script is used to parse blast output table to gff file""",
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460 formatter_class=argparse.RawTextHelpFormatter
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461 )
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462 parser.add_argument(
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463 '-i', '--input', default=None, required=True, help="input file", type=str,
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464 action='store'
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465 )
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466 parser.add_argument(
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467 '-d', '--db', default=None, required=False,
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468 help="Fasta file with repeat database", type=str, action='store'
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469 )
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470 parser.add_argument(
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471 '-o', '--output', default=None, required=True, help="output file name", type=str,
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472 action='store'
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473 )
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474 parser.add_argument(
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475 '-a', '--alternative_classification_coding', default=False,
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476 help="Use alternative classification coding", action='store_true'
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477 )
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478 parser.add_argument(
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479 '-f', '--fasta_input', default=False,
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480 help="Input is fasta file instead of blast table", action='store_true'
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481 )
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482 parser.add_argument(
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483 '-c', '--cpu', default=1, help="Number of cpu to use", type=int
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484 )
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485
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486 args = parser.parse_args()
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487
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488 if args.fasta_input:
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489 # run blast using blastn
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490 blastfile = tempfile.NamedTemporaryFile().name
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491 if args.db:
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492 run_blastn(args.input, args.db, blastfile, num_threads=args.cpu)
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493 else:
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494 sys.exit("No repeat database provided")
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495 else:
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496 blastfile = args.input
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497
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498 # sort blast table
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499 seq_id_counts, blast_sorted = sort_blast_table(blastfile, cpu=args.cpu)
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500 disjoin_regions = blast2disjoint(
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501 blast_sorted, seq_id_counts,
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502 canonical_classification=not args.alternative_classification_coding
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503 )
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504
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505 # remove short regions
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506 disjoin_regions = remove_short_interrupting_regions(disjoin_regions)
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507
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508 # join neighboring regions with same classification
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509 merged_regions = join_disjoint_regions_by_classification(disjoin_regions)
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510
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511 # remove short regions again
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512 merged_regions = remove_short_interrupting_regions(merged_regions)
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513
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514 # merge again neighboring regions with same classification
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515 merged_regions = join_disjoint_regions_by_classification(merged_regions, max_gap=10)
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516
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517 # remove short weak regions
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518 merged_regions = remove_short_regions(merged_regions)
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519
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520 # last merge
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521 merged_regions = join_disjoint_regions_by_classification(merged_regions, max_gap=20)
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522 write_merged_regions_to_gff3(merged_regions, args.output)
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523 # remove temporary files
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524 os.remove(blast_sorted)
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525
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526
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527 if __name__ == "__main__":
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528 main()