https://github.com/nanxstats/ssw-r
🚀 R interface for SSW, a fast implementation of the Smith-Waterman algorithm using SIMD
https://github.com/nanxstats/ssw-r
bioinformatics reticulate sequence-alignment simd smith-waterman
Last synced: 29 days ago
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🚀 R interface for SSW, a fast implementation of the Smith-Waterman algorithm using SIMD
- Host: GitHub
- URL: https://github.com/nanxstats/ssw-r
- Owner: nanxstats
- License: other
- Created: 2020-06-07T02:08:20.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-09-18T12:45:56.000Z (8 months ago)
- Last Synced: 2025-04-18T20:31:55.441Z (about 1 month ago)
- Topics: bioinformatics, reticulate, sequence-alignment, simd, smith-waterman
- Language: R
- Homepage: https://nanx.me/ssw-r/
- Size: 1.36 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# ssw-r 
[](https://github.com/nanxstats/ssw-r/actions/workflows/R-CMD-check.yaml)
ssw-r offers an R interface for
[SSW](https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library),
a fast implementation of the Smith-Waterman algorithm for sequence alignment
using SIMD. ssw-r is currently built on the Python package
[ssw-py](https://pypi.org/project/ssw-py/).
## Installation
You can install ssw-r from CRAN once available:
```r
install.packages("ssw")
```
Or try the development version on GitHub:
```r
remotes::install_github("nanxstats/ssw-r")
```
## Install ssw-py
A simple way to install the Python package ssw-py that ssw-r can discover
easily, is to run the helper function `ssw::install_ssw_py()`.
By default, it installs ssw-py into an virtual environment named `r-ssw-py`.
```r
ssw::install_ssw_py()
```
This follows the best practices suggested by the reticulate vignette
[Managing an R Package's Python
Dependencies](https://rstudio.github.io/reticulate/articles/python_dependencies.html).
There are also recommendations in the vignette on how to manage multiple
R packages with different Python dependencies.
## Usage
```r
library("ssw")
```
```r
"ACGT" |> align("TTTTACGTCCCCC")
```
```text
CIGAR start index 4: 4M
optimal_score: 8
sub-optimal_score: 0
target_begin: 4 target_end: 7
query_begin: 0
query_end: 3
Target: 4 ACGT 7
||||
Query: 0 ACGT 3
```
```r
"ACGT" |> align("TTTTACTCCCCC", gap_open = 3)
```
```text
CIGAR start index 4: 2M
optimal_score: 4
sub-optimal_score: 0
target_begin: 4 target_end: 5
query_begin: 0
query_end: 1
Target: 4 AC 5
||
Query: 0 AC 1
```
```r
"ACTG" |> force_align("TTTTCTGCCCCCACG") |> formatter(print = TRUE)
```
```text
TTTTCTGCCCCCACG
ACTG
```
For detailed usage, see the [vignette](https://nanx.me/ssw-r/articles/ssw.html).
## Acknowledgements
ssw-r is built upon the work of two outstanding projects:
1. [SSW](https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library) - Original C implementation. Author: Mengyao Zhao
1. [ssw-py](https://pypi.org/project/ssw-py/) - Python binding for SSW. Author: Nick Conway
We extend our sincere gratitude to Mengyao Zhao for developing the original
SSW library and to Nick Conway for maintaining the ssw-py package.
Their work forms the foundation of ssw-r.
While ssw-r does not directly incorporate code from these projects,
it serves as an R interface to their functionality. We encourage users to
visit the original repositories for more information about the underlying
implementation and to consider citing these works in publications that use ssw-r.
## Code of Conduct
Please note that the ssw-r project is released with a
[Contributor Code of Conduct](https://nanx.me/ssw-r/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.