An nf-core framework for the systematic comparison of alternative modeling tools: the multiple sequence alignment case study
The computational complexity of many key bioinformatics problems has resulted in numerous alternative heuristic solutions, where no single approach consistently outperforms all others. This creates difficulties for users trying to identify the most suitable tool for their dataset and for developers managing and evaluating alternative methods. As data volumes grow, deploying these methods becomes increasingly difficult, highlighting the need for standardized frameworks for seamless tool deployment and comparison in HPC environments. Multiple sequence aligners (MSAs) rank among the most commonly employed modeling techniques in bioinformatics, playing a crucial role in applications such as protein structure prediction, phylogenetic reconstruction, and variant effect prediction. The NP-hardness of MSAs makes them a major example of problems where heuristics stand central, as no optimal solution can be currently obtained, within the limits of operational computational requirements. Here, we present a pilot design of an nf-core framework for streamlined tool deployment and rigorous performance evaluation focusing on the MSAs software ecosystem. By integrating the most popular MSA tools and focusing on a modular, and extensible architecture, we aspire to provide a key platform supporting MSA deployment, evaluation, and algorithmics development to the MSA community, and a proof-of-principle to the wider bioinformatics community.