Abstract
Multiple sequence alignment tools struggle to keep pace with rapidly growing sequence data, as few methods can handle large datasets while maintaining alignment accuracy. We recently introduced MAGUS, a new state-of-the-art method for aligning large numbers of sequences. In this paper, we present a comprehensive set of enhancements that allow MAGUS to align vastly larger datasets with greater speed. We compare MAGUS to other leading alignment methods on datasets of up to one million sequences. Our results demonstrate the advantages of MAGUS over other alignment software in both accuracy and speed. MAGUS is freely available in open-source form at https://github.com/vlasmirnov/MAGUS.
Highlights
One of the principal problems in computational biology is multiple sequence alignment (MSA), being necessary for a wide range of downstream applications
We present a new version of our MAGUS alignment tool, which has been massively scaled up to handle datasets of up to one million sequences, and demonstrate MAGUS’s excellent performance in aligning ultra-large datasets
The preliminary part of our study, which investigates the impacts of compression, guide tree selection, and node-parallelism, is available in the Supplementary Materials. These results provide us with two natural guide tree choices for MAGUS: using FastTree is the most accurate, while using Clustal Omega’s initial tree is the faster alternative
Summary
We compare the following methods in our study, taken from previous publications [4, 11]. To the best of our knowledge, these methods are presently the best-equipped to tackle very large multiple sequence alignments. Regressive T-Coffee [18] is another recent development, but we were unable to run it on Blue Waters. MAGUS 1 We use the original MAGUS as a baseline. This version does not use recursion or compression, uses a FastTree decomposition, and can only run on a single node. MAGUS The latest version takes advantage of the new features detailed above. We enable recursion and compress alignments above 100GB.
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