Function: cross_encoding

def cross_encoding(sequences, pad_type= "after", pad_length= 0, n_jobs= 1) -> list[np.ndarray] | np.ndarray:

Description

Encodes sequences converting each nucleotide as follows:

• A= [0,-1]
• C= [-1,0]
• G= [1,0]
• T/U= [0,1]
• Other characters or gaps = [0,0]

Parameters

Parameter	Type	Default	Description
sequences	any iterable containing str	Required	Sequences to encode, can also be dna-parser SequenceReader, pysam FastxFile or Biopython SeqIO.read objects.
pad_type	str	after	Whether to trim/pad at the front or at the back of the sequences. Options are before and after. This paramater is ignored if there is no padding
pad_length	int	0	Length for padding/trimming. -2 to pad to the longest sequence, -1 to trim to the shortest sequence, any positive integer for a fixed length.
n_jobs	int	1	number of threads used to encode the sequences in parallel. 0 to use all CPUs available.

Returns

Type	Description
list[numpy.ndarray] or numpy.ndarray	The encoded sequences. Returns a list of 2D numpy arrays if there is no trimming/padding, otherwise returns a 3D numpy array with the outer dimension representing sequences.

Examples

import dna_parser as dps

sequences= ["agt","acc"]
encoding= dps.cross_encoding(sequences)
print(encoding)

# Output:
#[array([[ 1,  1],
#        [-1,  1],
#        [ 1, -1]], dtype=int32), 
# array([[ 1,  1],
#        [-1, -1],
#        [-1, -1]], dtype=int32)]


reader= dps.SequenceReader("path/to/file") # file with sequences agt, acc, attagc
encoding= dps.cross_encoding(reader, pad_type="after", pad_length=-2) #pad after the sequences to the longest one
print(encoding)
print(encoding.shape)

# Output:
#[[[ 1  1]
#  [-1  1]
#  [ 1 -1]
#  [ 0  0]
#  [ 0  0]
#  [ 0  0]]
#
# [[ 1  1]
#  [-1 -1]
#  [-1 -1]
#  [ 0  0]
#  [ 0  0]
#  [ 0  0]]
#
# [[ 1  1]
#  [ 1 -1]
#  [ 1 -1]
#  [ 1  1]
#  [-1  1]
#  [-1 -1]]]
#(3, 6, 2)