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.
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The authors thank Julie Thompson for helpful feedback on the manuscript. CD is supported by SNSF advanced researcher fellowship #136461. This article started as assignment for the graduate course “Reviews in Computational Biology” at the Cambridge Computational Biology Institute, University of Cambridge.
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