File size: 3,189 Bytes
34393ef | 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 | """Helpers for working with DNA/RNA data"""
import os
import numpy as np
# fix vocabulary
dna_vocab = {"A":0,
"C":1,
"G":2,
"T":3,
"*":4} # catch-all auxiliary token
rna_vocab = {"A":0,
"C":1,
"G":2,
"U":3,
"*":4}
dna_nt_only_vocab = {k:v for k,v in dna_vocab.items() if k in "ACGT"}
rna_nt_only_vocab = {k:v for k,v in dna_vocab.items() if k in "ACGU"}
rev_dna_vocab = {v:k for k,v in dna_nt_only_vocab.items()}
rev_rna_vocab = {v:k for k,v in rna_vocab.items()}
rev_dna_nt_only_vocab = {v:k for k,v in dna_vocab.items()}
rev_rna_nt_only_vocab = {v:k for k,v in rna_nt_only_vocab.items()}
def get_vocab(vocab_name, vocab_order=None):
if vocab_name=="dna":
charmap = dna_vocab
elif vocab_name=="rna":
charmap = rna_vocab
elif vocab_name=="dna_nt_only":
charmap = dna_nt_only_vocab
elif vocab_name=="rna_nt_only":
charmap = rna_nt_only_vocab
else:
raise Exception("Unknown vocabulary name.")
if vocab_order:
if set(vocab_order) != set(charmap):
raise ValueError("Provided `vocab` and `vocab_order` arguments are not compatible")
else:
charmap = {c: idx for idx, c in enumerate(vocab_order)}
rev_charmap = {v: k for k, v in charmap.items()}
return charmap, rev_charmap
def _process_line(line, max_len, charmap):
chars = line.strip()
I = np.eye(len(charmap))
try:
base = [I[charmap[c]] for c in chars]
if len(chars) < max_len:
extra = []
if "*" in charmap: extra = [I[charmap["*"]]] * (max_len - len(chars))
else:
extra = []
arr = np.array(base + extra)
except:
raise Exception("Unable to process line: {}".format(chars))
return np.expand_dims(arr, 0)
def load(data_loc, max_seq_len=None, vocab="dna", vocab_order=None, data_start_line=0, scores=False, valid=False, test=False, filenames=None, annotate=False):
charmap, _ = get_vocab(vocab, vocab_order)
if filenames:
if type(filenames)==list:
seq_filenames = filenames
else:
seq_filenames = [filenames]
else:
seq_filenames = ["train_data.txt"]
if valid:
seq_filenames.append("valid_data.txt")
if test:
seq_filenames.append("test_data.txt")
data = []
for name in seq_filenames:
with open(os.path.join(data_loc, name)) as f:
lines = f.readlines()
lines = lines[data_start_line:]
if not max_seq_len:
print("Warning: max_seq_len not provided. Inferring size from data.")
max_seq_len = len(max(lines, key=len)) - 1
lines = [_process_line(l, max_seq_len, charmap) for l in lines]
data.append(np.vstack(lines))
if scores:
score_filename = name.split("_")[0] + "_vals.txt"
scores_array = np.loadtxt(os.path.join(data_loc, score_filename), skiprows=data_start_line)
data.append(scores_array)
if annotate:
ann_filename = name.split("_")[0] + "_ann.txt"
ann_array = np.loadtxt(os.path.join(data_loc, ann_filename), skiprows=data_start_line)
ann_array = np.expand_dims(ann_array, 2)
data.append(ann_array)
if type(data) == list and len(data) == 1:
data = data[0]
return data
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