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 from Bio import SeqIO from Bio.SeqRecord import SeqRecord from Bio.SeqFeature import SeqFeature, FeatureLocation from Bio.Seq import Seq 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) # Return the sorted output, combined gene sequences, and all exons return results, all_gene_sequences, all_exons def create_genbank_features(data): features = [] # If the input data is a DataFrame, convert it to a list of lists if isinstance(data, pd.DataFrame): formatted_data = data.values.tolist() elif isinstance(data, list): formatted_data = data else: raise TypeError("Data should be either a list or a pandas DataFrame.") for row in formatted_data: try: start = int(row[1]) end = int(row[2]) except ValueError as e: print(f"Error converting start/end to int: {row[1]}, {row[2]} - {e}") continue strand = 1 if row[3] == '+' else -1 location = FeatureLocation(start=start, end=end, strand=strand) feature = SeqFeature(location=location, type="misc_feature", qualifiers={ 'label': row[7], # Use gRNA as the label 'note': f"Prediction: {row[8]}" # Include the prediction score }) features.append(feature) return features def generate_genbank_file_from_df(df, gene_sequence, gene_symbol, output_path): # Ensure gene_sequence is a string before creating Seq object if not isinstance(gene_sequence, str): gene_sequence = str(gene_sequence) features = create_genbank_features(df) # Now gene_sequence is guaranteed to be a string, suitable for Seq seq_obj = Seq(gene_sequence) record = SeqRecord(seq_obj, id=gene_symbol, name=gene_symbol, description=f'CRISPR Cas12 predicted targets for {gene_symbol}', features=features) record.annotations["molecule_type"] = "DNA" SeqIO.write(record, output_path, "genbank") def create_bed_file_from_df(df, output_path): with open(output_path, 'w') as bed_file: for index, row in df.iterrows(): chrom = row["Chr"] start = int(row["Start Pos"]) end = int(row["End Pos"]) strand = '+' if row["Strand"] == '1' else '-' gRNA = row["gRNA"] score = str(row["Prediction"]) # transcript_id is not typically part of the standard BED columns but added here for completeness transcript_id = row["Transcript"] # Writing only standard BED columns; additional columns can be appended as needed bed_file.write(f"{chrom}\t{start}\t{end}\t{gRNA}\t{score}\t{strand}\n") def create_csv_from_df(df, output_path): df.to_csv(output_path, index=False)