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| import requests | |
| import tensorflow as tf | |
| import pandas as pd | |
| import numpy as np | |
| from operator import add | |
| from functools import reduce | |
| 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 = [] | |
| if transcripts: | |
| for transcript in transcripts: | |
| transcript_id = transcript['id'] | |
| gene_sequence = fetch_ensembl_sequence(transcript_id) | |
| if gene_sequence: | |
| gRNA_sites = find_crispr_targets(gene_sequence) | |
| if gRNA_sites: | |
| formatted_data = format_prediction_output(gRNA_sites, transcript_id, model_path) | |
| all_data.extend(formatted_data) | |
| return all_data | |
| # Function to save results as CSV | |
| def save_to_csv(data, filename="crispr_results.csv"): | |
| df = pd.DataFrame(data, | |
| columns=["Gene ID", "Start Pos", "End Pos", "Strand", "gRNA", "Prediction"]) | |
| df.to_csv(filename, index=False) |