Mercurial > repos > iuc > virhunter

annotate utils/preprocess.py @ 2:206c8054d74a draft default tip

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 58587e05f604590c70550e13fc51b7425e916ed4
author iuc
date Sat, 14 Jan 2023 21:08:13 +0000
parents 6052fcc0d113
children
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6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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1 #!/usr/bin/env python
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2 # -*- coding: utf-8 -*-
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3 # Credits: Grigorii Sukhorukov, Macha Nikolski
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4 import math
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5 import os
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6 import pathlib
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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7 import random
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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8
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9 import h5py
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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10 import numpy as np
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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11 from Bio import SeqIO
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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12 from Bio.Seq import Seq
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13 from Bio.SeqRecord import SeqRecord
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14 from sklearn.utils import shuffle
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15
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16
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17 def reverse_complement(fragment):
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18 """
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19 provides reverse complement to sequences
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20 Input:
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21 sequences - list with SeqRecord sequences in fasta format
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22 Output:
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23 complementary_sequences -
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24 list with SeqRecord complementary sequences in fasta format
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25 """
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26 # complementary_sequences = []
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27 # for sequence in sequences:
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28 # complementary_sequence = SeqRecord(
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29 # seq=Seq(sequence.seq).reverse_complement(),
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30 # id=sequence.id + "_reverse_complement",
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31 # )
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32 # complementary_sequences.append(complementary_sequence)
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33 fragment = fragment[::-1].translate(str.maketrans('ACGT', 'TGCA'))
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34 return fragment
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35
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36
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37 def introduce_mutations(seqs, mut_rate, rs=None):
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38 """
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39 Function that mutates sequences in the entering fasta file
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40 A proportion of nucleotides are changed to other nucleotide
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41 Not yet taking account of mutation for gaps
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42 mut_rate - proportion from 0.0 to 1.0, float
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43 """
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44 random.seed(a=rs)
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45 assert 0.0 <= mut_rate <= 1.0
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46 mutated_seqs = []
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47 for seq in seqs:
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48 mut_seq = list(str(seq.seq))
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49 l_ = len(mut_seq)
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50 mutated_sites_i = random.sample(range(l_), int(mut_rate * l_))
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51 for mut_site_i in mutated_sites_i:
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52 mut_site = mut_seq[mut_site_i]
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53 mutations = ["A", "C", "T", "G"]
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54 if mut_site in mutations:
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55 mutations.remove(mut_site)
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56 mut_seq[mut_site_i] = random.sample(mutations, 1)[0]
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57 mutated_seq = SeqRecord(
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58 seq=Seq("".join(mut_seq)),
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59 id=seq.id + f"mut_{mut_rate}",
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60 name="",
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61 description="",
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62 )
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63 mutated_seqs.append(mutated_seq)
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64 return mutated_seqs
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65
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66
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67 def separate_by_length(length_, seq_list, fold=None,):
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68 # TODO: add docs
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69 included = []
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70 to_process = []
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71 excluded = 0
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72 for seq_ in seq_list:
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73 l_ = len(seq_.seq)
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74 if l_ >= length_:
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75 if fold is None:
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76 included.append(seq_)
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77 elif l_ < length_ * fold:
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78 included.append(seq_)
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79 else:
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80 to_process.append(seq_)
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81 else:
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82 excluded += 1
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83 print(f"A total of {excluded} sequences was excluded due to being smaller than {length_}")
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84 return included, to_process
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85
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86
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87 def chunks(lst, n):
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88 """Yield successive n-sized chunks from lst.
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89 https://stackoverflow.com/questions/312443/how-do-you-split-a-list-into-evenly-sized-chunks"""
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90 for i in range(0, len(lst), n):
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91 yield lst[i:i + n]
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92
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93
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94 def correct(frag):
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95 """
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96 leaves only unambiguous DNA code (ACTG-)
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97 Input:
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98 frag - string of nucleotides
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99 Output:
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100 pr_frag - corrected string of nucleotides
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101 """
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102 pr_frag = frag.upper()
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103 pr_frag_s = set(pr_frag)
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104 if pr_frag_s != {"A", "C", "G", "T", "-"}:
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105 for letter in pr_frag_s - {"A", "C", "G", "T", "-"}:
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106 pr_frag = pr_frag.replace(letter, "-")
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107 return pr_frag
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108
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109
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110 def fragmenting(sequences, sl_wind_size, max_gap=0.05, sl_wind_step=None):
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111 """
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112 slices sequences in fragments by sliding window
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113 based on its size and step.
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114 last fragment is padded by '-'
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115 fragments have ambiguous bases replaced by '-'
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116 fragments with many '-' are discarded
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117 Input:
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118 sequences - list with SeqRecord sequences in fasta format
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119 max_gap - max allowed proportion of '-'
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120 sl_wind_size - sliding window step
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121 sl_wind_step - sliding window step, by default equals
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122 sliding window size (None is replaced by it)
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123 Output:
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124 fragments - list with sequence fragments
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125 """
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126 if sl_wind_step is None:
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127 sl_wind_step = sl_wind_size
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128 fragments = []
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129 fragments_rc = []
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130 out_sequences = []
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131 for sequence in sequences:
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132 seq = str(sequence.seq)
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133 n_fragments = 1 + max(0, math.ceil((len(seq) - sl_wind_size) / sl_wind_step))
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134 for n in range(n_fragments):
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135 if n + 1 != n_fragments:
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136 frag = seq[n * sl_wind_step: n * sl_wind_step + sl_wind_size]
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137 elif n_fragments == 1:
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138 # padding the shorter fragment to sl_wind_size
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139 frag_short = seq[n * sl_wind_step: n * sl_wind_step + sl_wind_size]
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140 frag = frag_short + (sl_wind_size - len(frag_short)) * "-"
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141 else:
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142 frag = seq[(len(seq) - sl_wind_size):]
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143 # replace ambiguous characters
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144 frag = correct(frag)
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145 assert len(frag) == sl_wind_size, f"{len(frag)} vs {sl_wind_size}"
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146 # skipping sequences with many gaps
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147 if frag.count("-") / sl_wind_size <= max_gap:
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148 fragments.append(frag)
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149 # generating reverse complement
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150 fragments_rc.append(reverse_complement(frag))
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151 fr_seq = SeqRecord(
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152 seq=Seq(frag),
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153 id=f"{sequence.id}_{n*sl_wind_step}_{sl_wind_size}",
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154 name="",
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155 description="",
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156 )
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157 out_sequences.append(fr_seq)
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158 return fragments, fragments_rc, out_sequences
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159
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160
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161 def label_fasta_fragments(sequences, label):
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162 """
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163 Provides labels to generated fragments stored in fasta
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164 Input:
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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165 sequences - list with SeqRecord sequences
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166 label - type of label (bacteria, virus, plant)
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167 Output:
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168 labeled_fragments - list with labeled SeqRecord sequences
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169 """
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170 assert label in ["virus", "plant", "bacteria"]
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171 labeled_fragments = []
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172 for sequence in sequences:
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173 sequence.id = sequence.id + f"_{label}"
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174 labeled_fragments.append(sequence)
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175 return labeled_fragments
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176
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177
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178 def one_hot_encode(fragments):
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179 """
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180 produces one-hot matrices from fragments and labels
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181 '-' is given all zeros
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182 Input:
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183 fragments - list with sequence fragments
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184 label - type of label (int <= depth)
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185 label_depth - number of possible labels
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186 Output:
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187 encoded_fragments - list with one-hot encoded fragments
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188 labels - list with one-hot encoded labels
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189 """
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190 import tensorflow as tf
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191 encoded_fragments = []
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192 map_dict = {"A": 0, "C": 1, "G": 2, "T": 3, "-": -1}
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193 for frag in fragments:
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194 frag_array = np.array(list(frag))
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195 integer_encoded = np.int8(np.vectorize(map_dict.get)(frag_array))
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196 one_hot_encoded = tf.one_hot(integer_encoded, depth=4, dtype=tf.int8).numpy()
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197 encoded_fragments.append(one_hot_encoded)
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198 encoded_fragments = np.stack(encoded_fragments)
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199 return encoded_fragments
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200
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201
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202 def prepare_labels(fragments, label, label_depth):
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203 """
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204 produces one-hot labels
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205 '-' is given all zeros
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206 Input:
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207 fragments - list with sequence fragments
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208 label - type of label (int <= depth)
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209 label_depth - number of possible labels
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210 Output:
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211 labels - list with one-hot encoded labels
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212 """
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213 import tensorflow as tf
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214 n_fragments = len(fragments)
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215 labels = np.int8(np.full(n_fragments, label))
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216 labels = tf.one_hot(labels, depth=label_depth).numpy()
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217 return labels
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218
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219
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220 # TODO: write docs for functions
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221 def calculate_total_length(seq_path):
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222 """
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223 Calculate total length of the sequences in the fasta file.
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224 Needed for weighted sampling
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225 Input:
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226 seq_path - path to the file with sequences
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227 Output:
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228 seq_length - total length of all sequences in the file
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229 """
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230 seqs = list(SeqIO.parse(seq_path, "fasta"))
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231 seq_length = 0
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232 for seq in seqs:
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233 seq_length += len(seq.seq)
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234 return seq_length
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235
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236
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237 def prepare_seq_lists(in_paths, n_fragments, weights=None,):
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238 """
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239 selects files with sequences based on extension
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240 and calculates number of fragments to be sampled
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241 Input:
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242 in_paths - list of paths to folder with sequence files. Can be a string also a string
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243 n_fragments - number of fragments to be sampled
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244 weights - upsampling of fragments. fractions should sum to one
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245 Output:
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246 seqs_list - list with path to files with sequences
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247 n_fragments_list - number of fragments to be sampled
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248 """
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249 # case when we recieve a single sequence file
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250 if type(in_paths) is str and in_paths.endswith(('.fna', '.fasta')):
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251 return [[in_paths, n_fragments]]
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252 else:
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253 # transform string to list
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254 if type(in_paths) is str or type(in_paths) is pathlib.PosixPath:
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255 in_paths = [in_paths]
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256
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257 if weights:
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258 assert len(weights) == len(in_paths)
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259 assert 1.01 > round(sum(weights), 2) > 0.99
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260 else:
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261 l_ = len(in_paths)
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262 weights = [1 / l_] * l_
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263 n_fragments_list_all = []
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264 seqs_list_all = []
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265 for in_paths, w_ in zip(in_paths, weights):
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266 seqs_list = []
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267 seq_length_list = []
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268 total_length = 0
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269 for file in os.listdir(in_paths):
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270 if file.endswith("fna") or file.endswith("fasta"):
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271 seq_path = (os.path.join(in_paths, file))
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272 seqs_length = calculate_total_length(seq_path)
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273 seqs_list.append(seq_path)
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274 seq_length_list.append(seqs_length)
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275 total_length += seqs_length
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276 # + 1 may lead to a slightly bigger number than desired
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277 n_fragments_list = [((seq_length / total_length) * n_fragments * w_ + 1) for seq_length in seq_length_list]
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278 n_fragments_list_all.extend(n_fragments_list)
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279 seqs_list_all.extend(seqs_list)
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280 print("list calculation done")
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281 return list(zip(seqs_list_all, n_fragments_list_all))
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282
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283
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284 def sample_fragments(seq_container, length, random_seed=1, limit=None, max_gap=0.05, sl_wind_step=None):
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285 """
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286 Randomly samples fragments from sequences in the list.
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287 Input:
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288 seq_container - list with each entry containing path to sequence,
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289 and n samples from this sequence.
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290 length - desired length of sampled fragments
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291 Output:
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292 fragments - list with sequence fragments
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293 """
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294 random.seed(a=random_seed)
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295 total_fragments = []
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296 total_fragments_rc = []
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297 total_seqs = []
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298 for entry in seq_container:
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299 seq = list(SeqIO.parse(entry[0], "fasta"))
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300 n_fragments = entry[1]
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301 seqs = []
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302 fragments = []
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303 fragments_rc = []
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304 counter_1 = 0
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305 counter_2 = 0
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306 while counter_1 < n_fragments:
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307 # select chromosomes if there are any
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308 fragment_full = random.choice(seq)
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309 r_end = len(fragment_full.seq) - length
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310 try:
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311 r_start = random.randrange(r_end)
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312 fragment = SeqRecord(
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313 seq=fragment_full.seq[r_start:(r_start + length)],
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314 id=f"{fragment_full.id}_{length}_{r_start}",
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315 name="",
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316 description="",
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317 )
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318 temp_, temp_rc, _ = fragmenting([fragment], length, max_gap, sl_wind_step=sl_wind_step)
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319 if temp_ and temp_rc:
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320 seqs.append(fragment)
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321 fragments.extend(temp_)
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322 fragments_rc.extend(temp_rc)
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323 counter_1 += 1
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324 except ValueError:
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325 # print(f"{fragment_full.id} has length {len(fragment_full.seq)} and is too short to be sampled")
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326 pass
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327 counter_2 += 1
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328 if limit:
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329 assert counter_2 <= limit * n_fragments, f"While cycle iterated more than {limit}, data is ambiguous." \
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330 f" Only {len(fragments)} fragments were sampled out of {n_fragments}"
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331 total_fragments.extend(fragments)
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332 total_fragments_rc.extend(fragments_rc)
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333 total_seqs.extend(seqs)
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334 # print("sequence sampling done")
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335 return total_fragments, total_fragments_rc, total_seqs
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336
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337
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338 def prepare_ds_fragmenting(in_seq, label, label_int, fragment_length, sl_wind_step, max_gap=0.05, n_cpus=1):
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339 if sl_wind_step is None:
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340 sl_wind_step = int(fragment_length / 2)
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341 # generating viral fragments and labels
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342 seqs = list(SeqIO.parse(in_seq, "fasta"))
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343 frags, frags_rc, seqs_ = fragmenting(seqs, fragment_length, max_gap=max_gap, sl_wind_step=sl_wind_step)
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344 encoded = one_hot_encode(frags)
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345 encoded_rc = one_hot_encode(frags_rc)
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346 labs = prepare_labels(frags, label=label_int, label_depth=3)
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347 seqs_ = label_fasta_fragments(seqs_, label=label)
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348 # subsetting to unique fragments
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349 u_encoded, indices = np.unique(encoded, axis=0, return_index=True)
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350 u_encoded_rc = encoded_rc[indices]
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351 u_labs = labs[indices]
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352 u_seqs = [seqs_[i] for i in indices]
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353 assert (np.shape(u_encoded)[0] == np.shape(u_encoded_rc)[0])
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354 print(f"Encoding {label} sequences finished")
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355 # print(f"{np.shape(u_encoded)[0]} forward fragments generated")
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356 n_frags = np.shape(u_encoded)[0]
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357 return u_encoded, u_encoded_rc, u_labs, u_seqs, n_frags
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358
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359
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360 def prepare_ds_sampling(in_seqs, fragment_length, n_frags, label, label_int, random_seed, n_cpus=1, limit=100):
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361 # generating plant fragments and labels
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362 seqs_list = prepare_seq_lists(in_seqs, n_frags)
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363 frags, frags_rc, seqs_ = sample_fragments.remote(seqs_list, fragment_length, random_seed, limit=limit, max_gap=0.05)
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364 frags, frags_rc, seqs_ = shuffle(frags, frags_rc, seqs_, random_state=random_seed, n_samples=int(n_frags))
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365 encoded = one_hot_encode(frags)
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366 encoded_rc = one_hot_encode(frags_rc)
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367 labs = prepare_labels(frags, label=label_int, label_depth=3)
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368 seqs_ = label_fasta_fragments(seqs_, label=label)
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369 assert (np.shape(encoded)[0] == np.shape(encoded_rc)[0])
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370 print(f"Encoding {label} sequences finished")
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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371 # print(f"{np.shape(encoded)[0]} forward fragments generated")
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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372 return encoded, encoded_rc, labs, seqs_, n_frags
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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373
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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374
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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375 def storing_encoded(encoded, encoded_rc, labs, out_path, ):
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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376 f = h5py.File(out_path, "w")
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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377 f.create_dataset("fragments", data=encoded)
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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378 f.create_dataset("fragments_rc", data=encoded_rc)
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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379 f.create_dataset("labels", data=labs)
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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380 f.close()
6052fcc0d113 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/VirHunter commit 628688c1302dbf972e48806d2a5bafe27847bdcc
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381 print(f"encoded fragments and labels stored in {out_path}")