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# -*- coding: utf-8 -*-
from Bio import Phylo
import pandas as pd
import numpy as np
from itertools import combinations
import random
import math
from copy import deepcopy
from utils.utils import make_sub_folder
def safe_int16(mat):
if mat.dtype == 'int16':
return mat
assert mat.min() >= -32768
assert mat.max() <= 32767
return mat.astype('int16')
def safe_int8(mat):
if mat.dtype == 'int8':
return mat
assert mat.min() >= -128
assert mat.max() <= 127
return mat.astype('int8')
def safe_int32(mat):
if mat.dtype == 'int32':
return mat
assert mat.min() >= -2147483648
assert mat.max() <= 2147483647
return mat.astype('int32')
def read_inputs(pfam, seed_folder, trees_folder):
raw_msa = {}
pfam_level_meta = {'pfam': pfam,
'clan': '',
'type': ''}
num_seqs = 0
with open(f'{seed_folder}/{pfam}.seed','r') as f:
for line in f:
if line.startswith('#=GF CL'):
pfam_level_meta['clan'] = line.strip().split()[-1]
elif line.startswith('#=GF TP'):
pfam_level_meta['type'] = line.strip().split()[-1]
if not line.startswith('#'):
num_seqs += 1
name, seq = line.strip().split()
seq = seq.upper()
raw_msa[name] = seq
pfam_level_meta['pfam_Nseqs'] = num_seqs
tree = Phylo.read(f'{trees_folder}/{pfam}.tree', 'newick')
return raw_msa, tree, pfam_level_meta
def dedup(tuple_list):
return list( set( tuple( sorted(t) ) for t in tuple_list ) )
def read_pairs_from_file(filename):
df = pd.read_csv(filename, sep='\t')
# added this bit to specifically handle my inputs
df = df[['seq1','seq2']]
pairs = df.itertuples(index=False, name=None)
pairs = dedup(pairs)
return pairs
def generate_random_pairs(seqnames, percent_of_pairs, filename_of_cherries):
cherries = read_pairs_from_file(filename_of_cherries)
reverse_cherries = []
for (seq1, seq2) in cherries:
reverse_cherries.append( (seq2, seq1) )
banned_tuples = set( cherries + reverse_cherries )
all_possible_pairs = [tup for tup in combinations(seqnames, 2) if
tup not in banned_tuples]
num_to_sample = math.ceil(percent_of_pairs * len(all_possible_pairs))
pairs = random.sample(all_possible_pairs, num_to_sample)
return pairs
def extract_alignment(ancestor, descendant, raw_msa):
anc_gapped = raw_msa[ancestor]
desc_gapped = raw_msa[descendant]
alignment = []
num_matches = 0
num_subs = 0
num_ins = 0
num_dels = 0
for tup in zip(anc_gapped, desc_gapped):
if tup != ('.','.'):
alignment.append(tup)
# ins
if tup[0] == '.' and tup[1] != '.':
num_ins += 1
# del
elif tup[0] != '.' and tup[1] == '.':
num_dels += 1
# exact match
elif tup[0] == tup[1]:
num_matches += 1
# subs
else:
num_subs += 1
anc_seq_len = len( anc_gapped.replace('.','') )
desc_seq_len = len( desc_gapped.replace('.','') )
alignment_len = len( alignment )
psi = num_matches/min(anc_seq_len, desc_seq_len)
out_dict = {'perc_seq_id': psi,
'anc_seq_len': anc_seq_len,
'desc_seq_len': desc_seq_len,
'alignment_len': alignment_len,
'num_matches': num_matches,
'num_subs': num_subs,
'num_ins': num_ins,
'num_dels': num_dels}
return alignment, out_dict
def get_alphabet():
special = ['<pad>', '<bos>', '<eos>']
aas = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M',
'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
mapping = {elem:i for i,elem in enumerate(special + aas)}
mapping['.'] = 43
return mapping
def reversible_featurizer(str_alignment, mapping, max_len):
"""
unaligned_seqs_matrix = (B, L_seq, 2)
- dim2=0: ancestor, unaligned
- dim2=1: descendant, unaligned
- L_seq INCLUDES <bos>, <eos>
aligned_seqs_matrix = (B, L_align, 4)
- dim2=0: ancestor GAPPED (aligned)
- dim2=1: descendant GAPPED (aligned)
- dim2=2: precomputed m indexes for neural models
- dim2=3: precomputed n indexes for neural models
"""
# dim0=0 is forward pair, dim0=1 is reverse pair
unaligned_seqs_matrix = np.zeros( (2, max_len+2, 2) )
aligned_seqs_matrix = np.zeros( (2, max_len+2, 4) )
# padding token for aligned_seqs_matrix[:,:,[2,3]] should be -9
aligned_seqs_matrix[:,:,[2,3]] = -9
################################
### initialize first positions #
################################
# <bos> to start each unaligned sequence
unaligned_seqs_matrix[:, 0, :] = 1
# <bos> to start each aligned sequence
aligned_seqs_matrix[:, 0, [0,1]] = 1
# precomputed counts start with (m=1, n=0)
aligned_seqs_matrix[:, 0, 2] = 1
aligned_seqs_matrix[:, 0, 3] = 0
##########################################################
### step through alignment to fill from string_alignment #
##########################################################
def update_buckets( which,
align_idx,
anc_char,
desc_char,
anc_pos,
desc_pos):
##############
### deletion #
##############
if (desc_char == '.') & (anc_char != '.'):
### add to unaligned seq features
# ancestor
unaligned_seqs_matrix[which, anc_pos, 0] = mapping[anc_char]
# (no descendant sequence to add)
### add to aligned seq features
# gapped ancestor
aligned_seqs_matrix[which, align_idx, 0] = mapping[anc_char]
# gapped descendant
aligned_seqs_matrix[which, align_idx, 1] =mapping['.']
# at delete site: (m+1, n)
# precomputed m for NEXT ALIGN IDX
prev_m = aligned_seqs_matrix[which, align_idx-1, 2]
aligned_seqs_matrix[which, align_idx, 2] = prev_m + 1
# precomputed n for NEXT ALIGN IDX
prev_n = aligned_seqs_matrix[which, align_idx-1, 3]
aligned_seqs_matrix[which, align_idx, 3] = prev_n
### update buckets for next iter
anc_pos += 1
###############
### insertion #
###############
elif (anc_char == '.') & (desc_char != '.'):
### add to unaligned seq features
# (no ancestor sequence to add)
# descendant
unaligned_seqs_matrix[which, desc_pos, 1] = mapping[desc_char]
### add to aligned seq features
# gapped ancestor
aligned_seqs_matrix[which, align_idx, 0] = mapping['.']
# gapped descendant
aligned_seqs_matrix[which, align_idx, 1] =mapping[desc_char]
# at insert site: (m, n+1)
# precomputed m for NEXT ALIGN IDX
prev_m = aligned_seqs_matrix[which, align_idx-1, 2]
aligned_seqs_matrix[which, align_idx, 2] = prev_m
# precomputed n for NEXT ALIGN IDX
prev_n = aligned_seqs_matrix[which, align_idx-1, 3]
aligned_seqs_matrix[which, align_idx, 3] = prev_n + 1
### update buckets for next iter
desc_pos += 1
###########
### match #
###########
elif (anc_char != '.') & (desc_char != '.'):
### add to unaligned seq features
# ancestor
unaligned_seqs_matrix[which, anc_pos, 0] = mapping[anc_char]
# descendant
unaligned_seqs_matrix[which, desc_pos, 1] = mapping[desc_char]
### add to aligned seq features
# gapped ancestor
aligned_seqs_matrix[which, align_idx, 0] = mapping[anc_char]
# gapped descendant
aligned_seqs_matrix[which, align_idx, 1] =mapping[desc_char]
# at match site: (m+1, n+1)
# precomputed m for NEXT ALIGN IDX
prev_m = aligned_seqs_matrix[which, align_idx-1, 2]
aligned_seqs_matrix[which, align_idx, 2] = prev_m + 1
# precomputed n for NEXT ALIGN IDX
prev_n = aligned_seqs_matrix[which, align_idx-1, 3]
aligned_seqs_matrix[which, align_idx, 3] = prev_n + 1
### update buckets for next iter
anc_pos += 1
desc_pos += 1
return anc_pos, desc_pos
assert len(str_alignment) <= max_len
fw_anc_pos = 1
fw_desc_pos = 1
rv_anc_pos = 1
rv_desc_pos = 1
for i, (seq1_char, seq2_char) in enumerate(str_alignment):
# increment up by one, since you've already initialized first
# positions
align_idx = i+1
# forward: (seq1, seq2)
fw_out = update_buckets(which = 0,
align_idx = align_idx,
anc_char = seq1_char,
desc_char = seq2_char,
anc_pos = fw_anc_pos,
desc_pos = fw_desc_pos)
fw_anc_pos, fw_desc_pos = fw_out
del fw_out
# reverse: (seq2, seq1)
rv_out = update_buckets(which = 1,
align_idx = align_idx,
anc_char = seq2_char,
desc_char = seq1_char,
anc_pos = rv_anc_pos,
desc_pos = rv_desc_pos)
rv_anc_pos, rv_desc_pos = rv_out
del rv_out
###################################
### Add <eos> to end of sequences #
###################################
### updated unaligned_seqs_matrix
# forward: fw_anc_pos, fw_desc_pos
unaligned_seqs_matrix[0, fw_anc_pos, 0] = 2
unaligned_seqs_matrix[0, fw_desc_pos, 1] = 2
# reverse: rv_anc_pos, rv_desc_pos
unaligned_seqs_matrix[1, rv_anc_pos, 0] = 2
unaligned_seqs_matrix[1, rv_desc_pos, 1] = 2
### update aligned_seqs_matrix at align_idx + 1
aligned_seqs_matrix[:, align_idx+1, [0,1]] = 2
### try encoding
unaligned_seqs_matrix = safe_int8(unaligned_seqs_matrix)
aligned_seqs_matrix = safe_int16(aligned_seqs_matrix)
return unaligned_seqs_matrix, aligned_seqs_matrix
def encode_one_pair(i, seq1, seq2, tree, raw_msa, pfam, max_len):
dist = tree.distance(seq1,seq2)
fw_pair_level_metadata = {'pairID': f'FW_{pfam}_p{i}',
'ancestor': seq1,
'descendant': seq2,
'TREEDIST_anc-to-desc': dist}
rv_pair_level_metadata = {'pairID': f'RV_{pfam}_p{i}',
'ancestor': seq2,
'descendant': seq1,
'TREEDIST_anc-to-desc': dist}
str_alignment, add_to_fw = extract_alignment(ancestor = seq1,
descendant = seq2,
raw_msa = raw_msa)
fw_pair_level_metadata = {**fw_pair_level_metadata, **add_to_fw}
# swap info between anc and desc for reverse pair
add_to_rv = {'perc_seq_id': add_to_fw['perc_seq_id'],
'anc_seq_len': add_to_fw['desc_seq_len'],
'desc_seq_len': add_to_fw['anc_seq_len'],
'alignment_len': add_to_fw['alignment_len'],
'num_matches': add_to_fw['num_matches'],
'num_subs': add_to_fw['num_subs'],
'num_ins': add_to_fw['num_dels'],
'num_dels': add_to_fw['num_ins']
}
rv_pair_level_metadata = {**rv_pair_level_metadata, **add_to_rv}
del add_to_fw, add_to_rv
# generate neural and hmm pair alignment inputs in one go
mapping = get_alphabet()
unaligned_seqs_matrix, aligned_seqs_matrix = reversible_featurizer(str_alignment = str_alignment,
mapping = mapping,
max_len = max_len)
return (fw_pair_level_metadata,
rv_pair_level_metadata,
unaligned_seqs_matrix,
aligned_seqs_matrix)
def featurize_one_pfam(pfam,
seed_folder,
trees_folder,
filename,
max_len,
pairs_from = 'file',
percent_of_pairs = None):
### read inputs, get pairs
raw_msa, tree, pfam_level_metadata = read_inputs(pfam = pfam,
seed_folder = seed_folder,
trees_folder = trees_folder)
pairs = read_pairs_from_file(filename = filename)
### if you don't find any, exit function
if len(pairs) == 0:
return None
### iterate through pairs
metadata = []
unaligned_outputs = []
aligned_outputs = []
for pair_id, (seq1, seq2) in enumerate(pairs):
out = encode_one_pair(i = pair_id,
seq1 = seq1,
seq2 = seq2,
tree = tree,
raw_msa = raw_msa,
pfam = pfam,
max_len = max_len)
metadata.append(out[0])
metadata.append(out[1])
unaligned_outputs.append(out[2])
aligned_outputs.append(out[3])
metadata = pd.DataFrame(metadata)
unaligned_outputs = np.concatenate(unaligned_outputs, axis=0)
aligned_outputs = np.concatenate(aligned_outputs, axis=0)
### add pfam level info to metadata
for key, val in pfam_level_metadata.items():
metadata[key] = val
unaligned_outputs = safe_int8(unaligned_outputs)
aligned_outputs = safe_int16(aligned_outputs)
return unaligned_outputs, aligned_outputs, metadata
##################################
### gather random pairs; combine #
##################################
def make_rand_samp(pfam,
seed_folder,
trees_folder,
percent_of_pairs,
file_of_cherries,
dset_prefix,
max_len):
out = featurize_one_pfam(pfam = pfam,
seed_folder = seed_folder,
trees_folder = trees_folder,
pairs_from = 'rand_samp',
percent_of_pairs = percent_of_pairs,
filename = file_of_cherries,
max_len = max_len)
if out != None:
unaligned_outputs = out[0]
aligned_outputs = out[1]
metadata = out[2]
with open(f'{dset_prefix}/{pfam}_seqs_unaligned.npy', 'wb') as g:
np.save(g, unaligned_outputs)
with open(f'{dset_prefix}/{pfam}_aligned_mats.npy', 'wb') as g:
np.save(g, aligned_outputs)
metadata.to_csv(f'{dset_prefix}/{pfam}_metadata.tsv', sep='\t')
##########################################################
### generate pairs from an input file (usually cherries) #
##########################################################
def samples_from_file(pfam,
seed_folder,
trees_folder,
filename,
dset_prefix,
max_len):
out = featurize_one_pfam(pfam = pfam,
seed_folder = seed_folder,
trees_folder = trees_folder,
pairs_from = 'file',
filename = filename,
max_len = max_len)
if out != None:
unaligned_outputs = out[0]
aligned_outputs = out[1]
metadata = out[2]
with open(f'{dset_prefix}_full_length/{pfam}_seqs_unaligned.npy', 'wb') as g:
np.save(g, unaligned_outputs)
with open(f'{dset_prefix}_full_length/{pfam}_aligned_mats.npy', 'wb') as g:
np.save(g, aligned_outputs)
metadata.to_csv(f'{dset_prefix}_all_metadata/{pfam}_metadata.tsv', sep='\t')
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