Abstract
Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, a multiple sequence alignment method that uses a new machine learning technique, the ensemble of hidden Markov models, which we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at https://github.com/smirarab/sepp.
Original language | English (US) |
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Article number | 124 |
Journal | Genome biology |
Volume | 16 |
Issue number | 1 |
DOIs | |
State | Published - Jun 16 2015 |
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
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Data for Ultra-Large Alignments Using Phylogeny-Aware Profiles
Nguyen, N.-P. (Creator), Mirarab, S. (Creator), Kumar, K. (Creator) & Warnow, T. (Creator), University of Illinois Urbana-Champaign, Dec 16 2015
DOI: 10.13012/B2IDB-3174395_V1
Dataset