Spaces:
Running
Running
File size: 9,023 Bytes
dc94424 f90af69 dc94424 ad9ec7b dc94424 f90af69 ad9ec7b dc94424 ad9ec7b dc94424 ad9ec7b dc94424 ad9ec7b dc94424 ad9ec7b dc94424 ad9ec7b f90af69 ad9ec7b f90af69 ad9ec7b 2e72fba ad9ec7b dc94424 ad9ec7b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 |
from keras import Model
from keras.layers import Input
from keras.layers import Multiply
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution1D, AveragePooling1D
import pandas as pd
import numpy as np
import keras
import requests
from functools import reduce
from operator import add
from Bio.SeqRecord import SeqRecord
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.Seq import Seq
from Bio import SeqIO
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))
def Seq_DeepCpf1_model(input_shape):
Seq_deepCpf1_Input_SEQ = Input(shape=input_shape)
Seq_deepCpf1_C1 = Convolution1D(80, 5, activation='relu')(Seq_deepCpf1_Input_SEQ)
Seq_deepCpf1_P1 = AveragePooling1D(2)(Seq_deepCpf1_C1)
Seq_deepCpf1_F = Flatten()(Seq_deepCpf1_P1)
Seq_deepCpf1_DO1 = Dropout(0.3)(Seq_deepCpf1_F)
Seq_deepCpf1_D1 = Dense(80, activation='relu')(Seq_deepCpf1_DO1)
Seq_deepCpf1_DO2 = Dropout(0.3)(Seq_deepCpf1_D1)
Seq_deepCpf1_D2 = Dense(40, activation='relu')(Seq_deepCpf1_DO2)
Seq_deepCpf1_DO3 = Dropout(0.3)(Seq_deepCpf1_D2)
Seq_deepCpf1_D3 = Dense(40, activation='relu')(Seq_deepCpf1_DO3)
Seq_deepCpf1_DO4 = Dropout(0.3)(Seq_deepCpf1_D3)
Seq_deepCpf1_Output = Dense(1, activation='linear')(Seq_deepCpf1_DO4)
Seq_deepCpf1 = Model(inputs=[Seq_deepCpf1_Input_SEQ], outputs=[Seq_deepCpf1_Output])
return Seq_deepCpf1
# seq-ca model (DeepCpf1)
def DeepCpf1_model(input_shape):
DeepCpf1_Input_SEQ = Input(shape=input_shape)
DeepCpf1_C1 = Convolution1D(80, 5, activation='relu')(DeepCpf1_Input_SEQ)
DeepCpf1_P1 = AveragePooling1D(2)(DeepCpf1_C1)
DeepCpf1_F = Flatten()(DeepCpf1_P1)
DeepCpf1_DO1 = Dropout(0.3)(DeepCpf1_F)
DeepCpf1_D1 = Dense(80, activation='relu')(DeepCpf1_DO1)
DeepCpf1_DO2 = Dropout(0.3)(DeepCpf1_D1)
DeepCpf1_D2 = Dense(40, activation='relu')(DeepCpf1_DO2)
DeepCpf1_DO3 = Dropout(0.3)(DeepCpf1_D2)
DeepCpf1_D3_SEQ = Dense(40, activation='relu')(DeepCpf1_DO3)
DeepCpf1_Input_CA = Input(shape=(1,))
DeepCpf1_D3_CA = Dense(40, activation='relu')(DeepCpf1_Input_CA)
DeepCpf1_M = Multiply()([DeepCpf1_D3_SEQ, DeepCpf1_D3_CA])
DeepCpf1_DO4 = Dropout(0.3)(DeepCpf1_M)
DeepCpf1_Output = Dense(1, activation='linear')(DeepCpf1_DO4)
DeepCpf1 = Model(inputs=[DeepCpf1_Input_SEQ, DeepCpf1_Input_CA], outputs=[DeepCpf1_Output])
return DeepCpf1
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, end, strand, transcript_id, exon_id, pam="TTTN", target_length=34):
targets = []
len_sequence = len(sequence)
complement = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'}
dnatorna = {'A': 'A', 'T': 'U', 'C': 'C', 'G': 'G'}
for i in range(len_sequence - target_length + 1):
target_seq = sequence[i:i + target_length]
if target_seq[4:7] == 'TTT':
if strand == -1:
tar_start = end - i - target_length + 1
tar_end = end -i
#seq_in_ref = ''.join([complement[base] for base in target_seq])[::-1]
else:
tar_start = start + i
tar_end = start + i + target_length - 1
#seq_in_ref = target_seq
gRNA = ''.join([dnatorna[base] for base in target_seq[8:28]])
targets.append([target_seq, gRNA, chr, str(tar_start), str(tar_end), str(strand), transcript_id, exon_id])
return targets
def format_prediction_output(targets, model_path):
# Loading weights for the model
Seq_deepCpf1 = Seq_DeepCpf1_model(input_shape=(34, 4))
Seq_deepCpf1.load_weights(model_path)
formatted_data = []
for target in targets:
# Predict
encoded_seq = get_seqcode(target[0])
prediction = float(list(Seq_deepCpf1.predict(encoded_seq)[0])[0])
if prediction > 100:
prediction = 100
# 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])
return formatted_data
def process_gene(gene_symbol, model_path):
transcripts = fetch_ensembl_transcripts(gene_symbol)
results = []
if transcripts:
for i in range(len(transcripts)):
Exons = transcripts[i]['Exon']
transcript_id = transcripts[i]['id']
for j in range(len(Exons)):
exon_id = Exons[j]['id']
gene_sequence = fetch_ensembl_sequence(exon_id)
if gene_sequence:
start = Exons[j]['start']
end = Exons[j]['end']
strand = Exons[j]['strand']
chr = Exons[j]['seq_region_name']
targets = find_crispr_targets(gene_sequence, chr, start, end, strand, transcript_id, exon_id)
if targets:
formatted_data = format_prediction_output(targets, model_path)
results.append(formatted_data)
# for data in formatted_data:
# print(f"Chr: {data[0]}, Start: {data[1]}, End: {data[2]}, Strand: {data[3]}, target: {data[4]}, gRNA: {data[5]}, pred_Score: {data[6]}")
else:
print("Failed to retrieve gene sequence.")
else:
print("Failed to retrieve transcripts.")
return results, gene_sequence, Exons
# def create_genbank_features(formatted_data):
# features = []
# for data in formatted_data:
# try:
# # Attempt to convert start and end positions to integers
# start = int(data[1])
# end = int(data[2])
# except ValueError as e:
# # Log the error and skip this iteration if conversion fails
# print(f"Error converting start/end to int: {data[1]}, {data[2]} - {e}")
# continue # Skip this iteration
#
# # Proceed as normal if conversion is successful
# strand = 1 if data[3] == '+' else -1
# location = FeatureLocation(start=start, end=end, strand=strand)
# feature = SeqFeature(location=location, type="misc_feature", qualifiers={
# 'label': data[5], # gRNA as label
# 'note': f"Prediction: {data[6]}" # Prediction score in note
# })
# features.append(feature)
# return features
#
# def generate_genbank_file_from_data(formatted_data, gene_sequence, gene_symbol, output_path):
# features = create_genbank_features(formatted_data)
# record = SeqRecord(Seq(gene_sequence), id=gene_symbol, name=gene_symbol,
# description='CRISPR Cas12 predicted targets', features=features)
# record.annotations["molecule_type"] = "DNA"
# SeqIO.write(record, output_path, "genbank")
#
# def create_csv_from_df(df, output_path):
# df.to_csv(output_path, index=False)
#
# def generate_bed_file_from_data(formatted_data, output_path):
# with open(output_path, 'w') as bed_file:
# for data in formatted_data:
# try:
# # Ensure data has the expected number of elements
# if len(data) < 7:
# raise ValueError("Incomplete data item")
#
# chrom = data[0]
# start = data[1]
# end = data[2]
# strand = '+' if data[3] == '+' else '-'
# gRNA = data[5]
# score = data[6] # Ensure this index exists
#
# bed_file.write(f"{chrom}\t{start}\t{end}\t{gRNA}\t{score}\t{strand}\n")
# except ValueError as e:
# print(f"Skipping an item due to error: {e}")
# continue |