In its original form, MAGUS is able to align up to around 40,000 sequences. MAGUS uses the GCM (Graph Clustering Merger) technique to combine an arbitrary number of subalignments, which allows MAGUS to align large numbers of sequences with highly competitive accuracy and speed. MAGUS (Multiple Sequence Alignment using Graph Clustering) was recently introduced as a new evolution of this family. As a consequence, a family of divide-and-conquer methods was developed to meet the demands of larger datasets. Accurate progressive alignment methods rely on heuristics whose runtimes scale very poorly, and early mistakes are compounded over large numbers of pairwise alignments. Unfortunately, datasets with more sequences and greater evolutionary diameters require a different approach. Most of these leading methods follow the paradigm of “progressive alignment”, and are able to show reasonable accuracy and speed on datasets of modest size (a few hundred to a few thousand sequences). This challenge is well-studied, and a good number of strong methods have been developed. One of the principal problems in computational biology is multiple sequence alignment (MSA), being necessary for a wide range of downstream applications. This is a PLOS Computational Biology Software paper. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. Tandy Warnow, which was funded by NSF grant ABI-1458652. VS was also funded by a research assistantship with Dr. įunding: This work was funded by the Ira & Debra Cohen Graduate Fellowship to VS. The datasets used in this study can be downloaded from the Illinois Data Bank at. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: MAGUS is open-source and freely available at. Received: ApAccepted: SeptemPublished: October 6, 2021Ĭopyright: © 2021 Vladimir Smirnov. Citation: Smirnov V (2021) Recursive MAGUS: Scalable and accurate multiple sequence alignment.
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