File size: 4,792 Bytes
5032722 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
from initial_cleaning.split_seed_file import split_seed_file
from initial_cleaning.remove_duplicates import (find_repeats,
parse_within_families_file,
parse_across_families_file,
remove_samples)
from initial_cleaning.clean_short_peps_invalid_chars import clean_short_peps_invalid_chars
from initial_cleaning.prune_trees_after_msa_clean import prune_trees_after_msa_clean
from initial_cleaning.stats_after_cleaning import (serially_pfam_level_metadata,
clan_level_metadata)
from utils.utils import find_missing_info
def main(pfam_seed_file: str,
header: str,
seed_alignment_dir: str = 'seed_alignments',
tree_dir: str = 'trees'):
"""
after downloading pfam seed alignments file, clean and split into .seed
and .tree files per acceptable PFam
inputs:
-------
- pfam_seed_file: the single Pfam seed file to split
- header: information about the general pfam set
- seed_alignment_dir (str): where individual .seed files should go
- tree_dir (str): where individual .tree files should go
returns:
--------
(None)
outputs:
--------
- cleaned .seed and .tree files per pfam, ready to be split
"""
### split the pfam seed file
split_seed_file(seed_alignment_dir = seed_alignment_dir,
pfam_seed_file = pfam_seed_file,
header = header)
### remove repeated sequences within and across families
find_repeats(seed_alignment_dir = seed_alignment_dir)
dict1 = parse_within_families_file()
dict2 = parse_across_families_file()
remove_samples(seed_alignment_dir = seed_alignment_dir,
to_remove_dict = dict1)
remove_samples(seed_alignment_dir = seed_alignment_dir,
to_remove_dict = dict2)
# start recording what pfams are removed
removed_pfams = list(dict1.keys()) + list(dict2.keys())
del dict1, dict2
### remove short peptides, sequences with invalid chars
removed_this_step = clean_short_peps_invalid_chars(seed_alignment_dir = seed_alignment_dir)
removed_pfams = removed_pfams + removed_this_step
del removed_this_step
### whatever samples/pfams were removed, do the same with the trees
prune_trees_after_msa_clean(tree_dir)
### last check for missing samples
missing_trees, missing_msas = find_missing_info(seed_alignment_dir = seed_alignment_dir,
tree_dir = tree_dir)
err_msg = f'Have tree files without matching seed alignments?\n{missing_msas}'
assert len(missing_msas) == 0, err_msg
# if any pfams were permanently removed, remove from missing_trees;
# won't need to calculate these trees!
missing_trees = list( set(missing_trees) - set(removed_pfams) )
# these need to manually be aligned in FastTree
if len(missing_trees) > 0:
with open(f'ALIGN_IN_FASTTREE.tsv', 'w') as g:
[g.write(elem + '\n') for elem in missing_trees]
### get finer-grained stats after cleaning
prefix_for_files = pfam_seed_file.split('.')[0]
# pfam-level metadata
pfam_meta_df = serially_pfam_level_metadata(pfam_seed_file = pfam_seed_file,
seed_alignment_dir = seed_alignment_dir)
pfam_meta_df = pfam_meta_df[~pfam_meta_df['pfam'].isin(removed_pfams)]
pfam_level_metadata_file = f'{prefix_for_files}_PFAM-METADATA.tsv'
pfam_meta_df.to_csv(pfam_level_metadata_file, sep='\t')
del pfam_level_metadata_file
# clan-level metadata
clan_meta_df = clan_level_metadata(pfam_seed_file = pfam_seed_file)
for bad_pfam in removed_pfams:
clan_meta_df = clan_meta_df[~clan_meta_df['pfams'].str.contains(bad_pfam)]
clan_level_metadata_file = f'{prefix_for_files}_CLAN-METADATA.tsv'
clan_meta_df.to_csv(clan_level_metadata_file, sep='\t')
del clan_level_metadata_file
# write the final stats
out_dict = {'Number of Pfams': len(pfam_meta_df),
'Number of Pfams in clans': clan_meta_df['num_pfams'].sum(),
'Number of Unique clans': len(clan_meta_df)}
with open(f'{prefix_for_files}_STATS-AFTER-CLEANING.tsv', 'w') as g:
g.write(f'{header}\n')
[g.write(f'{key}\t{val}\n') for key, val in out_dict.items()]
with open(f'{prefix_for_files}_ALL-REMOVED-PFAMS.tsv','w') as g:
[g.write(elem + '\n') for elem in removed_pfams]
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