Search papers, labs, and topics across Lattice.
This study leverages agentic AI and static analysis to translate legacy bioinformatics code into Rust, addressing the challenges posed by outdated programming languages like Perl and Fortran. The approach not only significantly reduces code size and build time but also enhances performance, demonstrating a more efficient and secure alternative for clinical applications. The resulting software, Bascet, stands out as the only single-cell pipeline capable of running natively on Windows, thus broadening accessibility for researchers.
Legacy bioinformatics code can be transformed into efficient Rust implementations, slashing size by 80x and build time by 10x while boosting performance over threefold.
The field of bioinformatics struggles with legacy code - old code that is commonly used but may no longer have a maintainer, or may be written in an now-unfamiliar language (e.g. Perl, Fortran). This incurs maintenance cost (technical debt), but dynamically typed languages also negatively impacts the environment and fail to make use of modern hardware. Legacy code may also have security or safety problems that make it unsuited for use in clinical settings. Here we show that agentic AI, combined with static analysis, can be used to translate legacy code to the modern language Rust. We provide prompts and supporting software to aid systematic translation, and evaluate it on common software for NGS and imaging. We showcase the result on our software Bascet: Size was reduced by ~80x, build time decreased by ~10x, and performance of key steps improved>3x. Unix dependencies were also removed, making Bascet the only single-cell pipeline able to run on native Windows, without a container. Large-scale refactoring of bioinformatics software is thus now possible at a limited budget, enabling more complex tools to be developed.
