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()

# 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
        sgRNA = target[1]
        chr = target[2]
        start = target[3]
        end = target[4]
        strand = target[5]
        transcript_id = target[6]
        formatted_data.append([chr, start, end, strand, transcript_id, target[0], sgRNA, prediction[0]])

    return formatted_data

def fetch_ensembl_transcripts(gene_symbol):
    headers = {"Content-Type": "application/json"}
    url = f"https://rest.ensembl.org/lookup/symbol/homo_sapiens/{gene_symbol}?expand=1"
    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        gene_data = response.json()
        return gene_data.get('Transcript', [])
    else:
        print(f"Error fetching gene data from Ensembl: {response.text}")
        return None

def fetch_ensembl_sequence(transcript_id):
    headers = {"Content-Type": "application/json"}
    url = f"https://rest.ensembl.org/sequence/id/{transcript_id}"
    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        sequence_data = response.json()
        return sequence_data.get('seq', '')
    else:
        print(f"Error fetching sequence data from Ensembl for transcript {transcript_id}: {response.text}")
        return None

def fetch_ensembl_exons(transcript_id):
    headers = {"Content-Type": "application/json"}
    url = f"https://rest.ensembl.org/overlap/id/{transcript_id}?feature=exon"
    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error fetching exon data from Ensembl for transcript {transcript_id}: {response.text}")
        return None

def fetch_ensembl_cds(transcript_id):
    headers = {"Content-Type": "application/json"}
    url = f"https://rest.ensembl.org/overlap/id/{transcript_id}?feature=cds"
    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error fetching CDS data from Ensembl for transcript {transcript_id}: {response.text}")
        return None

def find_crispr_targets(sequence, chr, start, strand, transcript_id, pam="NGG", target_length=20):
    targets = []
    len_sequence = len(sequence)
    complement = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'}

    if strand == -1:
        sequence = ''.join([complement[base] for base in reversed(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
                sgRNA = sequence[i - target_length:i]
                targets.append([target_seq, sgRNA, chr, str(tar_start), str(tar_end), str(strand), transcript_id])

    return targets


def process_gene(gene_symbol, model_path):
    transcripts = fetch_ensembl_transcripts(gene_symbol)
    all_data = []

    if transcripts:
        cdslist = fetch_ensembl_cds(transcripts[0].get('id'))
        for transcript in transcripts:
            transcript_id = transcript.get('id')
            chr = transcript.get('seq_region_name', 'unknown')
            start = transcript.get('start', 0)
            strand = transcript.get('strand', 'unknown')
            # Fetch the gene sequence for each transcript
            gene_sequence = fetch_ensembl_sequence(transcript_id) or ''
            # Fetch exon and CDS information is not directly used here but you may need it elsewhere
            exons = fetch_ensembl_exons(transcript_id)

            if gene_sequence:
                # Now correctly passing transcript_id as an argument
                gRNA_sites = find_crispr_targets(gene_sequence, chr, start, strand, transcript_id)
                if gRNA_sites:
                    formatted_data = format_prediction_output(gRNA_sites, model_path)
                    all_data.extend(formatted_data)

    # Return the data and potentially any other information as needed
    return all_data, gene_sequence, exons, cdslist


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)