Who Watches the Watchmen? An Appraisal of Benchmarks for Multiple Sequence Alignment

TL;DR

This paper reviews various benchmarking strategies for multiple sequence alignment, highlighting their strengths and limitations, and emphasizes the importance of choosing appropriate benchmarks based on specific application contexts.

Contribution

It provides a comprehensive overview of existing benchmarking methods for MSA, evaluates their effectiveness, and discusses the need for context-dependent benchmark selection.

Findings

No universal benchmark for MSA exists

Different benchmarks have distinct advantages and risks

Context-specific benchmark choice is recommended

Abstract

Multiple sequence alignment (MSA) is a fundamental and ubiquitous technique in bioinformatics used to infer related residues among biological sequences. Thus alignment accuracy is crucial to a vast range of analyses, often in ways difficult to assess in those analyses. To compare the performance of different aligners and help detect systematic errors in alignments, a number of benchmarking strategies have been pursued. Here we present an overview of the main strategies--based on simulation, consistency, protein structure, and phylogeny--and discuss their different advantages and associated risks. We outline a set of desirable characteristics for effective benchmarking, and evaluate each strategy in light of them. We conclude that there is currently no universally applicable means of benchmarking MSA, and that developers and users of alignment tools should base their choice of benchmark depending on the context of application--with a keen awareness of the assumptions underlying each benchmarking strategy.