On closing the inopportune gap with consistency transformation and iterative refinement

Mario João, Alexandre C Sena and Vinod E F Rebello

PLOS ONE, 2023, vol. 18, issue 7, 1-24

Abstract: The problem of aligning multiple biological sequences has fascinated scientists for a long time. Over the last four decades, tens of heuristic-based Multiple Sequence Alignment (MSA) tools have been proposed, the vast majority being built on the concept of Progressive Alignment. It is known, however, that this approach suffers from an inherent drawback regarding the inadvertent insertion of gaps when aligning sequences. Two well-known corrective solutions have frequently been adopted to help mitigate this: Consistency Transformation and Iterative Refinement. This paper takes a tool-independent technique-oriented look at the alignment quality benefits of these two strategies using problem instances from the HOMSTRAD and BAliBASE benchmarks. Eighty MSA aligners have been used to compare 4 classes of heuristics: Progressive Alignments, Iterative Alignments, Consistency-based Alignments, and Consistency-based Progressive Alignments with Iterative Refinement. Statistically, while both Consistency-based classes are better for alignments with low similarity, for sequences with higher similarity, the differences between the classes are less clear. Iterative Refinement has its own drawbacks resulting in there being statistically little advantage for Progressive Aligners to adopt this technique either with Consistency Transformation or without. Nevertheless, all 4 classes are capable of bettering each other, depending on the instance problem. This further motivates the development of MSA frameworks, such as the one being developed for this research, which simultaneously contemplate multiple classes and techniques in their attempt to uncover better solutions.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0287483

DOI: 10.1371/journal.pone.0287483

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