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import tensorflow as tf
from keras import regularizers
from keras.layers import Input, Dense, Dropout, Activation, Conv1D
from keras.layers import GlobalAveragePooling1D, AveragePooling1D
from keras.layers import Bidirectional, LSTM
from keras import Model
from keras.metrics import MeanSquaredError
import pandas as pd
import numpy as np
import requests
from functools import reduce
from operator import add
import tabulate
from difflib import SequenceMatcher
import cyvcf2
import parasail
import re
ntmap = {'A': (1, 0, 0, 0),
'C': (0, 1, 0, 0),
'G': (0, 0, 1, 0),
'T': (0, 0, 0, 1)
}
def get_seqcode(seq):
return np.array(reduce(add, map(lambda c: ntmap[c], seq.upper()))).reshape((1, len(seq), -1))
def BiLSTM_model(input_shape):
input = Input(shape=input_shape)
conv1 = Conv1D(128, 5, activation="relu")(input)
pool1 = AveragePooling1D(2)(conv1)
drop1 = Dropout(0.1)(pool1)
conv2 = Conv1D(128, 5, activation="relu")(drop1)
pool2 = AveragePooling1D(2)(conv2)
drop2 = Dropout(0.1)(pool2)
lstm1 = Bidirectional(LSTM(128,
dropout=0.1,
activation='tanh',
return_sequences=True,
kernel_regularizer=regularizers.l2(1e-4)))(drop2)
avgpool = GlobalAveragePooling1D()(lstm1)
dense1 = Dense(128,
kernel_regularizer=regularizers.l2(1e-4),
bias_regularizer=regularizers.l2(1e-4),
activation="relu")(avgpool)
drop3 = Dropout(0.1)(dense1)
dense2 = Dense(32,
kernel_regularizer=regularizers.l2(1e-4),
bias_regularizer=regularizers.l2(1e-4),
activation="relu")(drop3)
drop4 = Dropout(0.1)(dense2)
dense3 = Dense(32,
kernel_regularizer=regularizers.l2(1e-4),
bias_regularizer=regularizers.l2(1e-4),
activation="relu")(drop4)
drop5 = Dropout(0.1)(dense3)
output = Dense(1, activation="linear")(drop5)
model = Model(inputs=[input], outputs=[output])
return model
def fetch_ensembl_transcripts(gene_symbol):
url = f"https://rest.ensembl.org/lookup/symbol/homo_sapiens/{gene_symbol}?expand=1;content-type=application/json"
response = requests.get(url)
if response.status_code == 200:
gene_data = response.json()
if 'Transcript' in gene_data:
return gene_data['Transcript']
else:
print("No transcripts found for gene:", gene_symbol)
return None
else:
print(f"Error fetching gene data from Ensembl: {response.text}")
return None
def fetch_ensembl_sequence(transcript_id):
url = f"https://rest.ensembl.org/sequence/id/{transcript_id}?content-type=application/json"
response = requests.get(url)
if response.status_code == 200:
sequence_data = response.json()
if 'seq' in sequence_data:
return sequence_data['seq']
else:
print("No sequence found for transcript:", transcript_id)
return None
else:
print(f"Error fetching sequence data from Ensembl: {response.text}")
return None
def find_crispr_targets(sequence, chr, start, end, strand, transcript_id, exon_id, pam="TTTN", target_length=34):
targets = []
len_sequence = len(sequence)
#complement = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'}
dnatorna = {'A': 'A', 'T': 'U', 'C': 'C', 'G': 'G'}
for i in range(len_sequence - target_length + 1):
target_seq = sequence[i:i + target_length]
if target_seq[4:7] == 'TTT':
if strand == -1:
tar_start = end - i - target_length + 1
tar_end = end -i
#seq_in_ref = ''.join([complement[base] for base in target_seq])[::-1]
else:
tar_start = start + i
tar_end = start + i + target_length - 1
#seq_in_ref = target_seq
gRNA = ''.join([dnatorna[base] for base in target_seq[8:28]])
targets.append([target_seq, gRNA, chr, str(tar_start), str(tar_end), str(strand), transcript_id, exon_id])
#targets.append([target_seq, gRNA, chr, str(tar_start), str(tar_end), str(strand), transcript_id, exon_id, seq_in_ref])
return targets
def format_prediction_output(targets, model_path):
# Loading weights for the model
Crispr_BiLSTM = BiLSTM_model(input_shape=(34, 4))
Crispr_BiLSTM.load_weights(model_path)
formatted_data = []
for target in targets:
# Predict
encoded_seq = get_seqcode(target[0])
prediction = float(list(Crispr_BiLSTM.predict(encoded_seq, verbose=0)[0])[0])
if prediction > 100:
prediction = 100
# Format output
gRNA = target[1]
chr = target[2]
start = target[3]
end = target[4]
strand = target[5]
transcript_id = target[6]
exon_id = target[7]
#seq_in_ref = target[8]
#formatted_data.append([chr, start, end, strand, transcript_id, exon_id, target[0], gRNA, seq_in_ref, prediction])
formatted_data.append([chr, start, end, strand, transcript_id, exon_id, target[0], gRNA, prediction])
return formatted_data
def process_gene(gene_symbol, model_path):
transcripts = fetch_ensembl_transcripts(gene_symbol)
results = []
all_exons = [] # To accumulate all exons
all_gene_sequences = [] # To accumulate all gene sequences
if transcripts:
for transcript in transcripts:
Exons = transcript['Exon']
all_exons.extend(Exons) # Add all exons from this transcript to the list
transcript_id = transcript['id']
for Exon in Exons:
exon_id = Exon['id']
gene_sequence = fetch_ensembl_sequence(exon_id)
if gene_sequence:
all_gene_sequences.append(gene_sequence) # Add this gene sequence to the list
chr = Exon['seq_region_name']
start = Exon['start']
end = Exon['end']
strand = Exon['strand']
targets = find_crispr_targets(gene_sequence, chr, start, end, strand, transcript_id, exon_id)
if targets:
# Predict on-target efficiency for each gRNA site
formatted_data = format_prediction_output(targets, model_path)
results.extend(formatted_data) # Flatten the results
else:
print(f"Failed to retrieve gene sequence for exon {exon_id}.")
else:
print("Failed to retrieve transcripts.")
# Sort results based on prediction score (assuming score is at the 8th index)
sorted_results = sorted(results, key=lambda x: x[8], reverse=True)
# Return the sorted output, combined gene sequences, and all exons
return sorted_results, all_gene_sequences, all_exons
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