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import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.optim import AdamW
import torch.utils.data
import transformers as T
import Bio.Seq
import numpy as np
import h5py
import copy
import pandas as pd
def load_model(tokenizer, model_path):
model = Bert4Coverage(tokenizer)
state_dict = torch.load(model_path, map_location = "cpu")
model.load_state_dict(state_dict, strict = False)
if torch.cuda.is_available():
model.cuda()
model.eval()
return model
def snp_2_seq(snp, strand, fasta):
chromosome = snp.split(":")[0]
position = int(snp.split(":")[1])
ref = snp.split(":")[2].split(">")[0]
mut = snp.split(":")[2].split(">")[1]
start = position - 256
end = position + 256
seq = fasta.fetch(chromosome, start, end)
ref_seq = copy.copy(seq)
assert ref_seq[255] == ref
mut_seq = [i for i in seq]
mut_seq[255] = mut
mut_seq = "".join(mut_seq)
if strand == "-":
ref_seq = str(Bio.Seq.Seq(ref_seq).reverse_complement())
mut_seq = str(Bio.Seq.Seq(mut_seq).reverse_complement())
return ref_seq, mut_seq
def tokenize_seq(ss,prefix,tokenizer):
prefix_code = pd.read_csv('./data/prefix_codes.csv')
prefix_code_dic = {a:b for a,b in zip(prefix_code.prefix, prefix_code.code_prefix)}
ss = [ss[i:int(i+3)] for i in range(int(len(ss)-2))]# 3 mer
seq = [prefix_code_dic[prefix]]
seq.extend(ss[:-1])
inputs = tokenizer(seq, is_split_into_words=True, add_special_tokens=True, return_tensors='pt')
return inputs['input_ids']
def calc_mutation_effect(wt_coverage, mt_coverage):
# the mutation effect was evaluate as the changes in predicted binding affinity before and after mutation
if len(mt_coverage.shape) == 1:
mt_coverage = mt_coverage.reshape(1,-1)
if len(wt_coverage.shape) == 1:
wt_coverage = wt_coverage.reshape(1,-1)
peak_mutpos_diff = np.abs(wt_coverage[:,202:302].sum(axis=1) - mt_coverage[:,202:302].sum(axis=1))/wt_coverage[:,202:302].sum(axis=1) # the binding affinity was measured as the coverage summation of 100 bp around the mutated nucleotide
return peak_mutpos_diff
def plot_tracks(tracks, interval=None, height=1.5):
# tracks : {"track1":np.array([...])}
# interval : "chr1:1-10000"
from matplotlib import pyplot as plt
import seaborn as sns
import kipoiseq
import numpy as np
if interval == None:
plot_interval = False
n = [i for i in tracks.values()][0]
interval= kipoiseq.Interval('xx', 0,len(n))
else:
plot_interval = True
start=interval.split(":")[1].split("-")[0]
end=interval.split(":")[1].split("-")[1]
chr_=interval.split(":")[0]
interval = kipoiseq.Interval(chr_, start,end)
fig, axes = plt.subplots(len(tracks), 1, figsize=(20, height * len(tracks)), sharex=True)
if len(tracks)>=2:
for ax, (title, y) in zip(axes, tracks.items()):
ax.fill_between(np.linspace(interval.start, interval.end, num=len(y)), y)
ax.set_title(title)
sns.despine(top=True, right=True, bottom=True)
if plot_interval == True:
ax.set_xlabel(str(interval))
plt.tight_layout()
else:
ax = axes
for (title, y) in tracks.items():
ax.fill_between(np.linspace(interval.start, interval.end, num=len(y)), y)
ax.set_title(title)
sns.despine(top=True, right=True, bottom=True)
if plot_interval == True:
ax.set_xlabel(str(interval))
plt.tight_layout()
def plot_tracks_comparision(tracks, interval=None, height=1.5):
# tracks : {"track1":np.array([...])}
# interval : "chr1:1-10000"
from matplotlib import pyplot as plt
import seaborn as sns
import kipoiseq
import numpy as np
if interval == None:
plot_interval = False
n = [i for i in tracks.values()][0]
interval= kipoiseq.Interval('xx', 0,len(n))
else:
plot_interval = True
start=interval.split(":")[1].split("-")[0]
end=interval.split(":")[1].split("-")[1]
chr_=interval.split(":")[0]
interval = kipoiseq.Interval(chr_, start,end)
fig, axes = plt.subplots(1, 1, figsize=(20, height * len(tracks)), sharex=True)
ax = axes
for (title, y) in tracks.items():
ax.fill_between(np.linspace(interval.start, interval.end, num=len(y)), y,alpha=0.5, label = title)
# ax.set_title(title)
sns.despine(top=True, right=True, bottom=True)
if plot_interval == True:
ax.set_xlabel(str(interval))
plt.tight_layout()
plt.legend()
class SequenceDataset(torch.utils.data.Dataset):
def __init__(self, h5file, tokenizer, max_length=512, train=False):
df = h5py.File(h5file)
self.tokenizer = tokenizer
self.max_length = max_length
self.sequence = df['seq']
self.barcode = df['code_prefix']
self.strand = np.array(df['strand'])
self.n = len(self.sequence)
def __len__(self):
return self.n
def __getitem__(self, i):
ss = self.sequence[i].decode()
if self.strand[i] == b'-':
ss = Bio.Seq.reverse_complement(ss)
ss = [ss[i:int(i+3)] for i in range(int(len(ss)-2))]# 3 mer data
seq = [self.barcode[i].decode()]
seq.extend(ss[:-1])
inputs = self.tokenizer(seq, is_split_into_words=True, add_special_tokens=True, return_tensors='pt')
return inputs['input_ids']
class SequenceDataset4train(torch.utils.data.Dataset):
def __init__(self, h5file, tokenizer, max_length=512, train=False):
df = h5py.File(h5file)
self.tokenizer = tokenizer
self.max_length = max_length
if train:
self.sequence = df['trn_seq']
self.label = df['trn_label']
self.barcode = df['trn_code_prefix']
self.coverage = np.array(df['trn_coverage'])
self.strand = np.array(df['trn_strand'])
else:
self.sequence = df['val_seq']
self.label = df['val_label']
self.barcode = df['val_code_prefix']
self.coverage = np.array(df['val_coverage'])
self.strand = np.array(df['val_strand'])
self.n = len(self.label)
def __len__(self):
return self.n
def __getitem__(self, i):
experiment_coverage = torch.tensor(self.label[i] * 1e4 / self.coverage[i]) # do cpm normalization
ss = self.sequence[i].decode()
if self.strand[i] == b'-':
ss = Bio.Seq.reverse_complement(ss)
experiment_coverage = experiment_coverage.flipud() # flip up-to-down
experiment_coverage.abs_() # scores on minus strand is negative
ss = [ss[i:int(i+3)] for i in range(int(len(ss)-2))]# 3 mer data
seq = [self.barcode[i].decode()]
seq.extend(ss[:-1])
inputs = self.tokenizer(seq, is_split_into_words=True, add_special_tokens=True, return_tensors='pt')
experiment_coverage = torch.tensor(experiment_coverage)[1:-2] # 3 mer label
return inputs['input_ids'], torch.as_tensor(experiment_coverage, dtype=torch.float32)
class Bert4Coverage(nn.Module):
def __init__(self,tokenizer, model_path = None):
super(Bert4Coverage, self).__init__()
if model_path == None:
config = T.BertConfig('./model/config.json')
config.vocab_size = np.max([len(tokenizer),512])
self.model = T.BertModel(config)
self.model.resize_token_embeddings(len(tokenizer))
else:
self.model = T.BertModel.from_pretrained(model_path)
hidden_size = self.model.config.hidden_size #768
self.dropout = nn.Dropout(0.2)
self.lin = nn.Linear(hidden_size, 1, bias=False)
def forward(self, input_ids, output_attentions = False):
output = self.model(input_ids=input_ids.squeeze(1), output_attentions=output_attentions)
hidden = output.last_hidden_state[:,2:-1,:]
hidden = self.dropout(hidden)
score = self.lin(hidden).squeeze()
if output_attentions == True:
return score.relu(), output['attentions']
else:
return score.relu()
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