Spaces:
Running
Running
| 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 | |
| import pyBigWig | |
| # 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() | |
| 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, transcript_id, exon_id, pam="NGG", target_length=20): | |
| targets = [] | |
| len_sequence = len(sequence) | |
| complement = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'} | |
| dnatorna = {'A': 'A', 'T': 'U', 'C': 'C', 'G': 'G'} | |
| if strand == -1: | |
| sequence = ''.join([complement[base] for base in 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 | |
| gRNA = ''.join([dnatorna[base] for base in sequence[i - target_length:i]]) | |
| targets.append([target_seq, gRNA, chr, str(tar_start), str(tar_end), str(strand), transcript_id, exon_id]) | |
| return targets | |
| # Function to predict on-target efficiency and format output | |
| def format_prediction_output(targets, model_path): | |
| dcModel = DCModelOntar(model_path) | |
| formatted_data = [] | |
| for target in targets: | |
| # Encode the gRNA sequence | |
| encoded_seq = get_seqcode(target[0]).reshape(-1,4,1,23) | |
| # Predict on-target efficiency using the model | |
| prediction = dcModel.ontar_predict(encoded_seq) | |
| # 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] | |
| formatted_data.append([chr, start, end, strand, transcript_id, exon_id, target[0], gRNA, prediction[0]]) | |
| 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 | |
| start = exon['start'] | |
| strand = exon['strand'] | |
| chr = exon['seq_region_name'] | |
| targets = find_crispr_targets(gene_sequence, chr, start, 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) | |
| else: | |
| print(f"Failed to retrieve gene sequence for exon {exon_id}.") | |
| else: | |
| print("Failed to retrieve transcripts.") | |
| # 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): | |
| # features = create_genbank_features(df) | |
| # record = SeqRecord(Seq(gene_sequence), id=gene_symbol, name=gene_symbol, | |
| # description=f'CRISPR Cas9 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"]) # Assuming 'Start Pos' is the column name in the df | |
| # end = int(row["End Pos"]) # Assuming 'End Pos' is the column name in the df | |
| # strand = '+' if row["Strand"] == '1' else '-' # Assuming 'Strand' is the column name in the df | |
| # gRNA = row["gRNA"] | |
| # score = str(row["Prediction"]) | |
| # transcript_id = row["Transcript"] # Assuming 'Transcript' is the column name in the df | |
| # | |
| # bed_file.write(f"{chrom}\t{start}\t{end}\t{gRNA}\t{score}\t{strand}\t{transcript_id}\n") | |
| # | |
| # | |
| # def create_csv_from_df(df, output_path): | |
| # df.to_csv(output_path, index=False) | |
| def create_bigwig(df, bigwig_path): | |
| # Check for required columns in the DataFrame | |
| required_columns = ["Chr", "Start Pos", "End Pos", "Prediction"] | |
| if not all(column in df.columns for column in required_columns): | |
| raise ValueError(f"DataFrame must contain {required_columns} columns.") | |
| # Convert columns to the correct types | |
| df['Start Pos'] = df['Start Pos'].astype(int) | |
| df['End Pos'] = df['End Pos'].astype(int) | |
| df['Prediction'] = df['Prediction'].astype(float) | |
| # Sort the DataFrame by chromosome and start position to ensure order | |
| df = df.sort_values(by=['Chr', 'Start Pos']) | |
| # Calculate chromosome sizes for the BigWig header | |
| chr_sizes = df.groupby('Chr')['End Pos'].max().to_dict() | |
| header = [(chr, size) for chr, size in chr_sizes.items()] | |
| # Create the BigWig file and add the header | |
| bw = pyBigWig.open(bigwig_path, "w") | |
| bw.addHeader(header) | |
| # Create a list of tuples with (chromosome, start, end, value) | |
| entries = [(row['Chr'], row['Start Pos'], row['End Pos'], row['Prediction']) | |
| for _, row in df.iterrows()] | |
| # Add entries to the BigWig file | |
| bw.addEntries(entries) | |
| # Close the BigWig file | |
| bw.close() | |