Abstract
Many fields of biology rely on the inference of accurate multiple sequence alignments (MSA) of biological sequences. Unfortunately, the problem of assembling an MSA is NP-complete thus limiting computation to approximate solutions using heuristics solutions. The progressive algorithm is one of the most popular frameworks for the computation of MSAs. It involves pre-clustering the sequences and aligning them starting with the most similar ones. The scalability of this framework is limited, especially with respect to accuracy. We present here an alternative approach named regressive algorithm. In this framework, sequences are first clustered and then aligned starting with the most distantly related ones. This approach has been shown to greatly improve accuracy during scale-up, especially on datasets featuring 10,000 sequences or more. Another benefit is the possibility to integrate third-party clustering methods and third-party MSA aligners. The regressive algorithm has been tested on up to 1.5 million sequences, its implementation is available in the T-Coffee package.
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References
Hogeweg P, Hesper B (1984) The alignment of sets of sequences and the construction of phyletic trees: an integrated method. J Mol Evol 20:175–186. https://doi.org/10.1007/bf02257378
Garriga E, Di Tommaso P, Magis C et al (2019) Large multiple sequence alignments with a root-to-leaf regressive method. Nat Biotechnol 37(12):1466–1470
Sievers F, Wilm A, Dineen D et al (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal omega. Mol Syst Biol 7:539. https://doi.org/10.1038/msb.2011.75
Finn RD, Bateman A, Clements J et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222–D230. https://doi.org/10.1093/nar/gkt1223
Notredame C, Higgins DG, Heringa J (2000) T-coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302:205–217. https://doi.org/10.1006/jmbi.2000.4042
Blackshields G, Sievers F, Shi W et al (2010) Sequence embedding for fast construction of guide trees for multiple sequence alignment. Algorithms Mol Biol 5:21. https://doi.org/10.1186/1748-7188-5-21
Katoh K, Toh H (2007) PartTree: an algorithm to build an approximate tree from a large number of unaligned sequences. Bioinformatics 23:372–374. https://doi.org/10.1093/bioinformatics/btl592
Acknowledgments
We acknowledge Des Higgins and Olivier Gascuel for useful discussions and feedback.
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Garriga, E. et al. (2021). Multiple Sequence Alignment Computation Using the T-Coffee Regressive Algorithm Implementation. In: Katoh, K. (eds) Multiple Sequence Alignment. Methods in Molecular Biology, vol 2231. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1036-7_6
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DOI: https://doi.org/10.1007/978-1-0716-1036-7_6
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