ArticleBioinformatics (Oxford, England)2023
UPP2: fast and accurate alignment of datasets with fragmentary sequences.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 14 citations in OpenAlex.
- Fast and accurate multiple-protein-sequence alignment at scale with FAMSA2.Nature biotechnology · 2026Article
- Phylogenetic Placement Using SCAMPP and Batch-SCAMPP.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Augmenting microbial phylogenomic signal with tailored marker gene sets.Nature communications · 2025Article
- T-shaped alignments integrating HIV-1 near full-length genome and partial pol sequences can improve phylogenetic inference of transmission clusters.PLoS computational biology · 2025Article
- An nf-core framework for the systematic comparison of alternative modeling tools: the multiple sequence alignment case study.NAR genomics and bioinformatics · 2025Article
- learnMSA2: deep protein multiple alignments with large language and hidden Markov models.Bioinformatics (Oxford, England) · 2024Article
- Fast multiple sequence alignment via multi-armed bandits.Bioinformatics (Oxford, England) · 2024Article
- EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment.Algorithms for molecular biology : AMB · 2023Article
- SCAMPP+FastTree: improving scalability for likelihood-based phylogenetic placement.Bioinformatics advances · 2023Article
- WITCH-NG: efficient and accurate alignment of datasets with sequence length heterogeneity.Bioinformatics advances · 2023Article
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5 authors at 1 institution in 1 country.
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Abstract
motivationMultiple 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. Ultra-large multiple sequence alignment using Phylogeny-aware Profiles (UPP) 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.
resultsWe 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 that 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 AND IMPLEMENTATION: https://github.com/gillichu/sepp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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