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import numpy as np
import pandas as pd
from typing import Callable
NTs = 'ACGT'
AAs = 'ACDEFGHIKLMNPQRSTVWY'
AA_TO_CODON = {
'*': ['TAA', 'TAG', 'TGA'], # Stop.
'A': ['GCT', 'GCC', 'GCA', 'GCG'], # Ala.
'C': ['TGT', 'TGC'], # Cys.
'D': ['GAT', 'GAC'], # Asp.
'E': ['GAA', 'GAG'], # Glu.
'F': ['TTT', 'TTC'], # Phe.
'G': ['GGU', 'GGC', 'GGA', 'GGG'], # Gly.
'H': ['CAT', 'CAC'], # His.
'I': ['ATT', 'ATC', 'ATA'], # Ile.
'K': ['AAA', 'AAG'], # Lys.
'L': ['TTA', 'TTG', 'CTT', 'CTC', 'CTA', 'CTG'], # Leu.
'M': ['ATG'], # Met.
'N': ['AAT', 'AAC'], # Asn.
'P': ['CCT', 'CCC', 'CCA', 'CCG'], # Pro.
'Q': ['CAA', 'CAG'], # Gln.
'R': ['CGT', 'CGC', 'CGA', 'CGG', 'AGA', 'AGG'], # Arg.
'S': ['TCT', 'TCC', 'TCA', 'TCG', 'AGT', 'AGC'], # Ser.
'T': ['ACT', 'ACC', 'ACA', 'ACG'], # Thr.
'V': ['GTT', 'GTC', 'GTA', 'GTG'], # Val.
'W': ['TGG'], # Trp.
'Y': ['TAT', 'TAC'], # Tyr.
}
CODON_TO_AA = {
codon: aa
for aa, codon_list in AA_TO_CODON.items()
for codon in codon_list
}
AA_3_TO_1 = {
"Ala": "A", # Alanine
"Arg": "R", # Arginine
"Asn": "N", # Asparagine
"Asp": "D", # Aspartic acid
"Cys": "C", # Cysteine
"Gln": "Q", # Glutamine
"Glu": "E", # Glutamic acid
"Gly": "G", # Glycine
"His": "H", # Histidine
"Ile": "I", # Isoleucine
"Leu": "L", # Leucine
"Lys": "K", # Lysine
"Met": "M", # Methionine
"Phe": "F", # Phenylalanine
"Pro": "P", # Proline
"Ser": "S", # Serine
"Thr": "T", # Threonine
"Trp": "W", # Tryptophan
"Tyr": "Y", # Tyrosine
"Val": "V" # Valine
}
def nucleotide_deep_mutational_scan(sequence: str, ignore_wt: bool = True):
for idx, wt in enumerate(sequence):
for mt in NTs:
if ignore_wt and wt == mt:
continue
yield (wt, mt, idx)
def parse_blast_output(output_path: str) -> pd.DataFrame:
"""
Parses standard blast output with `-outfmt 6`.
"""
# blast default format output fields.
blast_table_header = [
'qacc', 'sacc', 'pident', 'length', 'mismatch', 'gapopen', 'qstart',
'qend', 'sstart', 'send', 'evalue',
]
data = []
with open(output_path, 'r') as f:
for line in f:
if line.startswith("#"):
continue
if line.strip() == '':
continue
line = line.strip().split()
data.append(dict(zip(blast_table_header, line)))
df = pd.DataFrame(data)
if len(df) == 0:
return df
df['evalue'] = df['evalue'].astype(float)
return df
def parse_erpin_output(output_path: str, name: str) -> pd.DataFrame:
"""
Parses ERPIN output. For an example, see `eval/data/example_rho_output.txt`.
"""
# ERPIN format output fields.
output_fields = [ 'strand', 'index', 'interval', 'score', 'evalue' ]
data = []
with open(output_path, 'r') as f:
for line in f:
if line.startswith(f'>{name}'):
meta = dict(zip(output_fields, f.readline().rstrip().split()))
sequence = f.readline().rstrip()
start, end = meta['interval'].split('..')
data.append([
f"{name}_{meta['index']}_{meta['strand']}",
sequence,
int(start),
int(end),
'+' if meta['strand'] == 'FW' else '-',
meta['score'],
float(meta['evalue']),
])
return pd.DataFrame(
data,
columns=[
'id',
'seq',
'start',
'end',
'strand',
'score',
'evalue',
],
)
def parse_hmmsearch_output(output_path: str) -> pd.DataFrame:
"""
Parses standard hmmsearch output.
"""
# hmmsearch format output fields.
hmmsearch_table_header = [
'target', 'target_acc', 'tlen', 'query', 'query_acc', 'qlen',
'evalue', 'score', 'bias', 'num', 'of', 'cevalue', 'ievalue',
'dscore', 'dbias', 'hmm_from', 'hmm_to', 'ali_from', 'ali_to',
'env_from', 'env_to', 'acc', 'desc',
]
data = []
with open(output_path, 'r') as f:
for line in f:
if line.startswith("#"):
continue
line = line.strip().split()
data.append(dict(zip(hmmsearch_table_header, line)))
return pd.DataFrame(data)
def permutation_test(
score_func: Callable[[np.array, np.array], float],
x1: np.array,
x2: np.array,
n_permutations: int = 100_000,
) -> float:
"""
Returns a permutation-based P value. Computes the null distribution by
shuffling the provided data and recomputing the `score_func`.
"""
if n_permutations < 1:
raise ValueError('Number of permutations must be positive.')
x1, x2 = np.array(x1), np.array(x2)
observed_score = score_func(x1, x2)
null_distribution = np.array([
score_func(x1, np.random.permutation(x2))
for _ in range(n_permutations)
])
return np.mean(null_distribution >= observed_score)
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