fasta2onehot.py

from numpy import array
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import re

class hot_dna:
 def __init__(self,fasta):
   
  #check for and grab sequence name
  if 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 array
  seq_array = array(list(sequence))
    
  #integer encode the sequence
  label_encoder = LabelEncoder()
  integer_encoded_seq = label_encoder.fit_transform(seq_array)
    
  #one hot the sequence
  onehot_encoder = OneHotEncoder(sparse=False)
  #reshape because that's what OneHotEncoder likes
  integer_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 self 
  self.name = name
  self.sequence = fasta
  self.integer = integer_encoded_seq
  self.onehot = onehot_encoded_seq