• 定義生成onehot類
• 每次讀入一行描述行和一行序列
• 生成csv文件

import pandas as pd from numpy import array from numpy import argmax from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder import re# fasta = ">description\nAAAAAAAAAAAAAAACCCCCCCCCCCGGGGGGGGGGGGGTTTTTTTTTTTTTTTT\n" class hot_dna:def __init__(self, fasta):# check for and grab sequence nameif re.search(">", fasta):name = re.split("\n", fasta)[0]sequence = re.split("\n", fasta)[1]else:name = 'unknown_sequence'sequence = fasta# get sequence into an arrayseq_array = array(list(sequence))# integer encode the sequencelabel_encoder = LabelEncoder()integer_encoded_seq = label_encoder.fit_transform(seq_array)# one hot the sequenceonehot_encoder = OneHotEncoder(sparse=False)# reshape because that's what OneHotEncoder likesinteger_encoded_seq = integer_encoded_seq.reshape(len(integer_encoded_seq), 1)onehot_encoded_seq = onehot_encoder.fit_transform(integer_encoded_seq)# add the attributes to selfself.name = nameself.sequence = fastaself.integer = integer_encoded_seqself.onehot = onehot_encoded_seqinputfile = "H_sapiens_acc_sample__len398_pos.fasta" savefile = "SpliceRover_H_sapiens_acc_pos.csv"with open(inputfile,"r") as f:data = f.readlines()for index,line in enumerate(data):# index 從0開始,if index % 2 == 0:fasta = data[index]+data[index+1]my_hottie = hot_dna(fasta)onehot = pd.DataFrame(my_hottie.onehot)onehot.to_csv(savefile,index=False,header=False,mode="a+") f.close()

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