Work

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

Year: 2026

Type: article

Abstract: Motivation: Modern computational biology relies heavily on command-line tools that underpin analytical workflows, yet integrating these tools into R-based analyses poses substantial challenges for reproducibility, especially for researchers with limited computational expertise. While R excels at statistical analysis and visualization, its native capabilities for executing external commands remain rudimentary, lacking structured integration, environment management, and reliable cross-platform support. Results: To address these limitations, BLIT, an R package, provides a unified framework for seamless command-line integration within R, enabling R users to invoke external tools in an intuitive manner. Rather than positioning itself as yet another workflow manager, BLIT emphasizes the robust encapsulation and coordination of command-line tools, enabling users to construct reproducible analysis pipelines without leaving the R environment. BLIT encapsulates command-line programs as R6 objects with dynamic validation, Micromamba-based environment management, and native piping support. This makes BLIT a direct bridge between R scripts and heterogeneous command-line ecosystems, replacing fragile system() calls with a robust, standardized framework that supports conditional execution and single-machine parallel computing, with the capability to hand off constructed command workflows to HPC schedulers or workflow engines for large-scale execution. Although designed for bioinformatics, BLIT offers a general, platform-agnostic solution for constructing reproducible analytical pipelines within R. Availability and implementation: BLIT is available on CRAN (https://cran.r-project.org/package=blit) and on GitHub (https://github.com/WangLabCSU/blit).

Source: Bioinformatics Advances

DOI: 10.1093/bioadv/vbag088

Authors: Jia Ding, Yun Peng, Ruochen Wei, Boquan Wang, Jian-Guo Zhou, Shixiang Wang

Institutions: Central South University, Peking University People's Hospital, Zunyi Medical University

Language: English

Topic: Scientific Computing and Data Management

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,726

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hunan Province