import requests import tensorflow as tf import pandas as pd import numpy as np from operator import add from functools import reduce from Bio import SeqIO from Bio.SeqRecord import SeqRecord from Bio.SeqFeature import SeqFeature, FeatureLocation from Bio.Seq import Seq from keras.models import load_model import random # configure GPUs for gpu in tf.config.list_physical_devices('GPU'): tf.config.experimental.set_memory_growth(gpu, enable=True) if len(tf.config.list_physical_devices('GPU')) > 0: tf.config.experimental.set_visible_devices(tf.config.list_physical_devices('GPU')[0], 'GPU') 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)) from keras.models import load_model class DCModelOntar: def __init__(self, ontar_model_dir, is_reg=False): self.model = load_model(ontar_model_dir) def ontar_predict(self, x, channel_first=True): if channel_first: x = x.transpose([0, 2, 3, 1]) yp = self.model.predict(x) return yp.ravel() # Function to predict on-target efficiency and format output def format_prediction_output(gRNAs, model_path): dcModel = DCModelOntar(model_path) formatted_data = [] for gRNA in gRNAs: # Encode the gRNA sequence encoded_seq = get_seqcode(gRNA[0]).reshape(-1,4,1,23) # Predict on-target efficiency using the model prediction = dcModel.ontar_predict(encoded_seq) # Format output chr = gRNA[1] start = gRNA[2] end = gRNA[3] strand = gRNA[4] formatted_data.append([chr, start, end, strand, gRNA[0], prediction[0]]) return formatted_data 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, strand, pam="NGG", target_length=20): targets = [] len_sequence = len(sequence) for i in range(len_sequence - len(pam) + 1): if sequence[i + 1:i + 3] == pam[1:]: if i >= target_length: target_seq = sequence[i - target_length:i + 3] tar_start = start + i - target_length tar_end = start + i + 3 targets.append([target_seq, chr, tar_start, tar_end, strand]) return targets def process_gene(gene_symbol, model_path): transcripts = fetch_ensembl_transcripts(gene_symbol) all_data = [] gene_sequence = '' # Initialize an empty string for the gene sequence if transcripts: for transcript in transcripts: transcript_id = transcript['id'] chr = transcript.get('seq_region_name', 'unknown') start = transcript.get('start', 0) strand = transcript.get('strand', 'unknown') # Fetch the sequence here and concatenate if multiple transcripts gene_sequence += fetch_ensembl_sequence(transcript_id) or '' if gene_sequence: gRNA_sites = find_crispr_targets(gene_sequence, chr, start, strand) if gRNA_sites: formatted_data = format_prediction_output(gRNA_sites, model_path) all_data.extend(formatted_data) # Return both the data and the fetched sequence return all_data, gene_sequence def create_genbank_features(gRNAs, predictions): features = [] for gRNA, prediction in zip(gRNAs, predictions): location = FeatureLocation(start=gRNA[2], end=gRNA[3], strand=gRNA[4]) feature = SeqFeature(location=location, type="misc_feature", qualifiers={ 'label': gRNA[0], # Target sequence as label 'note': f"Prediction: {prediction}" # Prediction score in note }) features.append(feature) return features def generate_genbank_file_from_df(df, gene_sequence, gene_symbol, output_path): features = [] for index, row in df.iterrows(): location = FeatureLocation(start=int(row["Start Pos"]), end=int(row["End Pos"]), strand=int(row["Strand"])) feature = SeqFeature(location=location, type="gene", qualifiers={ 'locus_tag': row["Gene ID"], # Assuming Gene ID is equivalent to Chromosome here 'note': f"gRNA: {row['gRNA']}, Prediction: {row['Prediction']}" }) features.append(feature) record = SeqRecord(Seq(gene_sequence), id=gene_symbol, name=gene_symbol, description='CRISPR Cas9 predicted targets', features=features) # Add the missing molecule_type annotation 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["Gene ID"] start = int(row["Start Pos"]) end = int(row["End Pos"]) strand = '+' if int(row["Strand"]) > 0 else '-' gRNA = row["gRNA"] score = row["Prediction"] bed_file.write(f"{chrom}\t{start}\t{end}\t{gRNA}\t{score}\t{strand}\n")