https://github.com/kyu999/ssw_aligner
Python implementation of Striped Smith-Waterman Algorithm
https://github.com/kyu999/ssw_aligner
bio bioinformatics local-alignment smith-waterman-alignment
Last synced: 5 months ago
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Python implementation of Striped Smith-Waterman Algorithm
- Host: GitHub
- URL: https://github.com/kyu999/ssw_aligner
- Owner: kyu999
- Created: 2018-10-16T01:39:32.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-03-04T05:32:01.000Z (over 2 years ago)
- Last Synced: 2025-12-05T13:19:26.091Z (6 months ago)
- Topics: bio, bioinformatics, local-alignment, smith-waterman-alignment
- Language: C
- Homepage: https://pypi.org/project/ssw_aligner/
- Size: 886 KB
- Stars: 24
- Watchers: 3
- Forks: 4
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# SSW Aligner
Python implementation of [Striped Smith-Waterman Algorithm](https://academic.oup.com/bioinformatics/article/23/2/156/205631)
### Comparison with other Smith-Waterman Implementation in Python as of 2018/10/18
|| ssw_aligner | [swalign](https://github.com/mbreese/swalign) | [scikit-bio](https://github.com/biocore/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](http://www.numpy.org/)
- [Cython==0.28.3](https://cython.org/)
### 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](https://github.com/biocore/scikit-bio)