SSW Aligner

Python implementation of Striped Smith-Waterman Algorithm

Comparison with other Smith-Waterman Implementation in Python as of 2018/10/18

	ssw_aligner	swalign	scikit-bio
version	0.0.7	0.3.4	0.4.2
Python2	○	○	✗
Python3	○	○	○
benchmark	1.049 seconds	2326.898 seconds	1.567 seconds
zipped package size	108 KB	9 KB	8.6 MB
Installable to Google Dataflow	○	○	✗

Dependencies

• numpy==1.12.0
• Cython==0.28.3

Installation

pip install numpy==1.12.0
pip install Cython==0.28.3
pip install ssw_aligner

Quick Start

from ssw_aligner import local_pairwise_align_ssw

query_seq = 'TTTTTAAAAA'
target_seq = 'GGGGTTTT'
alignment = local_pairwise_align_ssw(query_seq,
                                     target_seq,
                                     gap_open_penalty=11,
                                     gap_extend_penalty=1,
                                     match_score=2,
                                     mismatch_score=-3)

# get score
alignment.optimal_alignment_score

# get query start, end
alignment.query_begin
alignment.query_end

# get target start, end
alignment.target_begin
alignment.target_end_optimal

# get aligned sequence
alignment.aligned_query_sequence
alignment.aligned_target_sequence

# get cigar infomation
alignment.cigar

# check whether the index starts from 0 or not
alignment.is_zero_based()

# make the index start from n(0 or 1)
alignment.set_zero_based(0) # start from 0
alignment.set_zero_based(1) # start from 1

Benchmark script:

import random
import time

from skbio import DNA
import skbio
import swalign
import ssw_aligner


match = 2
mismatch = -1
scoring = swalign.NucleotideScoringMatrix(match, mismatch)
sw = swalign.LocalAlignment(scoring)

bases = ['A', 'T', 'C', 'G']
def generate_gene(length):
    return ''.join([random.choice(bases) for i in range(0, length)])


def benchmark(align_func):
    start = time.time()
    for i in range(0, 100):
        for seq_length in range(100, 2000, 500):
            seq1, seq2 = generate_gene(seq_length), generate_gene(seq_length)
            align_func(seq1, seq2)
    return time.time() - start


# input should be DNA type
def benchmark_skbio(align_func):
    start = time.time()
    for i in range(0, 100):
        for seq_length in range(100, 2000, 500):
            seq1, seq2 = generate_gene(seq_length), generate_gene(seq_length)
            align_func(DNA(seq1), DNA(seq2))
    return time.time() - start


print('ssw_aligner')
ssw_aligner_time = benchmark(ssw_aligner.local_pairwise_align_ssw)
print(ssw_aligner_time)

print('skbio')
skbio_time = benchmark_skbio(skbio.alignment.local_pairwise_align_ssw)
print(skbio_time)

print('swalign')
swalign_time = benchmark(sw.align)
print(swalign_time)

This benchmark script is executed by the environment below:

• MacBook Air (13-inch, Mid 2012)
• Processor: 2 GHz Intel Core i7
• Memory: 8GB

※This repository uses a part of codes fetched from scikit-bio