{"id":20693435,"url":"https://github.com/nanxstats/ssw-r","last_synced_at":"2025-04-22T17:42:56.378Z","repository":{"id":142658962,"uuid":"270163835","full_name":"nanxstats/ssw-r","owner":"nanxstats","description":"🚀 R interface for SSW, a fast implementation of the Smith-Waterman algorithm using 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ssw-r \u003cimg src=\"man/figures/logo.png\" align=\"right\" width=\"120\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/nanxstats/ssw-r/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/nanxstats/ssw-r/actions/workflows/R-CMD-check.yaml)\n![CRAN status](https://www.r-pkg.org/badges/version/ssw)\n\u003c!-- badges: end --\u003e\n\nssw-r offers an R interface for\n[SSW](https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library),\na fast implementation of the Smith-Waterman algorithm for sequence alignment\nusing SIMD. ssw-r is currently built on the Python package\n[ssw-py](https://pypi.org/project/ssw-py/).\n\n## Installation\n\nYou can install ssw-r from CRAN once available:\n\n```r\ninstall.packages(\"ssw\")\n```\n\nOr try the development version on GitHub:\n\n```r\nremotes::install_github(\"nanxstats/ssw-r\")\n```\n\n## Install ssw-py\n\nA simple way to install the Python package ssw-py that ssw-r can discover\neasily, is to run the helper function `ssw::install_ssw_py()`.\nBy default, it installs ssw-py into an virtual environment named `r-ssw-py`.\n\n```r\nssw::install_ssw_py()\n```\n\nThis follows the best practices suggested by the reticulate vignette\n[Managing an R Package's Python\nDependencies](https://rstudio.github.io/reticulate/articles/python_dependencies.html).\nThere are also recommendations in the vignette on how to manage multiple\nR packages with different Python dependencies.\n\n## Usage\n\n```r\nlibrary(\"ssw\")\n```\n\n```r\n\"ACGT\" |\u003e align(\"TTTTACGTCCCCC\")\n```\n\n```text\nCIGAR start index 4: 4M\noptimal_score: 8\nsub-optimal_score: 0\ntarget_begin: 4\ttarget_end: 7\nquery_begin: 0\nquery_end: 3\n\nTarget:        4    ACGT    7\n                    ||||\nQuery:         0    ACGT    3\n```\n\n```r\n\"ACGT\" |\u003e align(\"TTTTACTCCCCC\", gap_open = 3)\n```\n\n```text\nCIGAR start index 4: 2M\noptimal_score: 4\nsub-optimal_score: 0\ntarget_begin: 4\ttarget_end: 5\nquery_begin: 0\nquery_end: 1\n\nTarget:        4    AC    5\n                    ||\nQuery:         0    AC    1\n```\n\n```r\n\"ACTG\" |\u003e force_align(\"TTTTCTGCCCCCACG\") |\u003e formatter(print = TRUE)\n```\n\n```text\nTTTTCTGCCCCCACG\n   ACTG\n```\n\nFor detailed usage, see the [vignette](https://nanx.me/ssw-r/articles/ssw.html).\n\n## Acknowledgements\n\nssw-r is built upon the work of two outstanding projects:\n\n1. [SSW](https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library) - Original C implementation. Author: Mengyao Zhao\n1. [ssw-py](https://pypi.org/project/ssw-py/) - Python binding for SSW. Author: Nick Conway\n\nWe extend our sincere gratitude to Mengyao Zhao for developing the original\nSSW library and to Nick Conway for maintaining the ssw-py package.\nTheir work forms the foundation of ssw-r.\nWhile ssw-r does not directly incorporate code from these projects,\nit serves as an R interface to their functionality. We encourage users to\nvisit the original repositories for more information about the underlying\nimplementation and to consider citing these works in publications that use ssw-r.\n\n## Code of Conduct\n\nPlease note that the ssw-r project is released with a\n[Contributor Code of Conduct](https://nanx.me/ssw-r/CODE_OF_CONDUCT.html).\nBy contributing to this project, you agree to abide by its terms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnanxstats%2Fssw-r","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnanxstats%2Fssw-r","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnanxstats%2Fssw-r/lists"}