AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees

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Standard

AncestralClust : clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees. / Pipes, Lenore; Nielsen, Rasmus.

In: Bioinformatics, Vol. 38, No. 3, 2022, p. 663-670.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Pipes, L & Nielsen, R 2022, 'AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees', Bioinformatics, vol. 38, no. 3, pp. 663-670. https://doi.org/10.1093/bioinformatics/btab723

APA

Pipes, L., & Nielsen, R. (2022). AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees. Bioinformatics, 38(3), 663-670. https://doi.org/10.1093/bioinformatics/btab723

Vancouver

Pipes L, Nielsen R. AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees. Bioinformatics. 2022;38(3):663-670. https://doi.org/10.1093/bioinformatics/btab723

Author

Pipes, Lenore ; Nielsen, Rasmus. / AncestralClust : clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees. In: Bioinformatics. 2022 ; Vol. 38, No. 3. pp. 663-670.

Bibtex

@article{5ee23ff1e90e4b6f8638211863b458e1,
title = "AncestralClust: clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees",
abstract = "Motivation: Clustering is a fundamental task in the analysis of nucleotide sequences. Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster divergent sequences. Traditional clustering methods have mostly focused on optimizing high speed clustering of highly similar sequences. We develop a phylogenetic clustering method which infers ancestral sequences for a set of initial clusters and then uses a greedy algorithm to cluster sequences.Results: We describe a clustering program AncestralClust, which is developed for clustering divergent sequences. We compare this method with other state-of-the-art clustering methods using datasets of homologous sequences from different species. We show that, in divergent datasets, AncestralClust has higher accuracy and more even cluster sizes than current popular methods.",
keywords = "SEARCH",
author = "Lenore Pipes and Rasmus Nielsen",
year = "2022",
doi = "10.1093/bioinformatics/btab723",
language = "English",
volume = "38",
pages = "663--670",
journal = "Bioinformatics (Online)",
issn = "1367-4811",
publisher = "Oxford University Press",
number = "3",

}

RIS

TY - JOUR

T1 - AncestralClust

T2 - clustering of divergent nucleotide sequences by ancestral sequence reconstruction using phylogenetic trees

AU - Pipes, Lenore

AU - Nielsen, Rasmus

PY - 2022

Y1 - 2022

N2 - Motivation: Clustering is a fundamental task in the analysis of nucleotide sequences. Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster divergent sequences. Traditional clustering methods have mostly focused on optimizing high speed clustering of highly similar sequences. We develop a phylogenetic clustering method which infers ancestral sequences for a set of initial clusters and then uses a greedy algorithm to cluster sequences.Results: We describe a clustering program AncestralClust, which is developed for clustering divergent sequences. We compare this method with other state-of-the-art clustering methods using datasets of homologous sequences from different species. We show that, in divergent datasets, AncestralClust has higher accuracy and more even cluster sizes than current popular methods.

AB - Motivation: Clustering is a fundamental task in the analysis of nucleotide sequences. Despite the exponential increase in the size of sequence databases of homologous genes, few methods exist to cluster divergent sequences. Traditional clustering methods have mostly focused on optimizing high speed clustering of highly similar sequences. We develop a phylogenetic clustering method which infers ancestral sequences for a set of initial clusters and then uses a greedy algorithm to cluster sequences.Results: We describe a clustering program AncestralClust, which is developed for clustering divergent sequences. We compare this method with other state-of-the-art clustering methods using datasets of homologous sequences from different species. We show that, in divergent datasets, AncestralClust has higher accuracy and more even cluster sizes than current popular methods.

KW - SEARCH

U2 - 10.1093/bioinformatics/btab723

DO - 10.1093/bioinformatics/btab723

M3 - Journal article

C2 - 34668516

VL - 38

SP - 663

EP - 670

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

SN - 1367-4811

IS - 3

ER -

ID: 291295087