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• W4214891019 abstract "Abstract Motivation Multiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. UPP (Ultra-large multiple sequence alignment using Phylogeny-aware Profiles) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets. Results We present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity, and is among the most accurate otherwise. Availability https://github.com/gillichu/sepp Contact warnow@illinois.edu" @default.
• W4214891019 created "2022-03-05" @default.
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• W4214891019 date "2022-03-01" @default.
• W4214891019 modified "2023-10-18" @default.
• W4214891019 title "UPP2: Fast and Accurate Alignment Estimation of Datasets with Fragmentary Sequences" @default.
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• W4214891019 doi "https://doi.org/10.1101/2022.02.26.482099" @default.
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