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.") output = [] for result in results: for item in result: output.append(item) # Sort results based on prediction score (assuming score is at the 8th index) sorted_results = sorted(output, 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