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
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()