Fast embedding methods for clustering tens of thousands of sequences

DOI10.1016/J.COMPBIOLCHEM.2008.03.005zbMath1158.92011DBLPjournals/candc/BlackshieldsLWWH08OpenAlexW2044543672WikidataQ43473840 ScholiaQ43473840MaRDI QIDQ1004933

Andreas Wilm, Iain M. Wallace, Desmond G. Higgins, Gordon Blackshields, Mark Larkin

Publication date: 12 March 2009

Published in: Computational Biology and Chemistry (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compbiolchem.2008.03.005

zbMATH Keywords

clusteringmultiple sequence alignmentedit distancedata embedding

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40) Computational methods for problems pertaining to biology (92-08)

Uses Software

• Rfam
• ClustalW
• PartTree
• OXBench
• Pfam

Cites Work

• On Lipschitz embedding of finite metric spaces in Hilbert space
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