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Introduction to R01:11

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...

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BLIT: an R package for seamless integration of command-line bioinformatics tool universe.

Jia Ding1, Yun Peng2, Ruochen Wei1

  • 1Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, 410013, P. R. China.

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|May 21, 2026
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Summary

The BLIT R package simplifies command-line tool integration for reproducible computational biology workflows. It offers a robust, unified framework for R users, enhancing analytical pipeline development.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • Command-line tools are essential for computational biology workflows.
  • Integrating external command-line tools into R presents reproducibility challenges for users with limited computational expertise.
  • R's native capabilities for executing external commands are rudimentary, lacking structured integration and cross-platform support.

Purpose of the Study:

  • To provide a unified framework for seamless command-line tool integration within R.
  • To enable R users to invoke external tools intuitively and construct reproducible analysis pipelines.
  • To offer a robust, standardized alternative to fragile system() calls.

Main Methods:

  • Developed BLIT, an R package, as a unified framework for command-line integration.
  • Encapsulated command-line programs as R6 objects with dynamic validation.
  • Implemented Micromamba-based environment management and native piping support within BLIT.
  • Enabled conditional execution and single-machine parallel computing.
  • Facilitated handoff of workflows to HPC schedulers or workflow engines.

Main Results:

  • BLIT provides a direct bridge between R scripts and command-line ecosystems.
  • The package replaces fragile system() calls with a robust, standardized framework.
  • BLIT supports reproducible analysis pipeline construction entirely within the R environment.
  • It offers a general, platform-agnostic solution for R-based analytical pipelines.

Conclusions:

  • BLIT significantly enhances the reproducibility and ease of integrating command-line tools in R-based analyses.
  • The package empowers researchers, particularly those with limited computational expertise, to build complex analytical workflows.
  • BLIT serves as a versatile, cross-platform solution for modern computational biology and data science pipelines.