cas9on.py

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()



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(formatted_data):
#     features = []
#     for data in formatted_data:
#         # Strand conversion to Biopython's convention
#         strand = 1 if data[3] == '+' else -1
#         location = FeatureLocation(start=int(data[1]), end=int(data[2]), strand=strand)
#         feature = SeqFeature(location=location, type="misc_feature", qualifiers={
#             'label': data[5],  # Use gRNA as the label
#             'target': data[4],  # Include the target sequence
#             'note': f"Prediction: {data[6]}"  # Include the prediction score
#         })
#         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():
#         # Use 'Transcript ID' if it exists, otherwise use a default value like 'Unknown'
#         transcript_id = row.get("Transcript ID", "Unknown")
#
#         # Make sure to use the correct column names for Start Pos, End Pos, and Strand
#         location = FeatureLocation(start=int(row["Start Pos"]),
#                                    end=int(row["End Pos"]),
#                                    strand=1 if row["Strand"] == '+' else -1)
#         feature = SeqFeature(location=location, type="gene", qualifiers={
#             'locus_tag': transcript_id,  # Now using the variable that holds the safe value
#             'note': f"gRNA: {row['gRNA']}, Prediction: {row['Prediction']}"
#         })
#         features.append(feature)
#
#     # The rest of the function remains unchanged
#     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():
#             # Adjust field names based on your actual formatted data
#             chrom = row["Chr"]
#             start = int(row["Start Pos"])
#             end = int(row["End Pos"])
#             strand = '+' if row["Strand"] == '+' else '-'  # Ensure strand is correctly interpreted
#             gRNA = row["gRNA"]
#             score = str(row["Prediction"])  # Ensure score is converted to string if not already
#             transcript_id = row["Transcript"]  # Extract transcript ID
#             bed_file.write(f"{chrom}\t{start}\t{end}\t{gRNA}\t{score}\t{strand}\t{transcript_id}\n")  # Include transcript ID in BED output
#
#
# def create_csv_from_df(df, output_path):
#     df.to_csv(output_path, index=False)