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) |
|---|---|
| Article number | 124 |
| Journal | Genome biology |
| Volume | 16 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jun 16 2015 |
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ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
Cite this
Ultra-large alignments using phylogeny-aware profiles. / Nguyen, Nam Phuong D.; Mirarab, Siavash; Kumar, Keerthana; Warnow, Tandy.
In: Genome biology, Vol. 16, No. 1, 124, 16.06.2015.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Ultra-large alignments using phylogeny-aware profiles
AU - Nguyen, Nam Phuong D.
AU - Mirarab, Siavash
AU - Kumar, Keerthana
AU - Warnow, Tandy
PY - 2015/6/16
Y1 - 2015/6/16
N2 - 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.
AB - 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.
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U2 - 10.1186/s13059-015-0688-z
DO - 10.1186/s13059-015-0688-z
M3 - Article
C2 - 26076734
AN - SCOPUS:84939168822
VL - 16
JO - Genome Biology
JF - Genome Biology
SN - 1465-6906
IS - 1
M1 - 124
ER -