Lenore Pipes1, Rasmus Nielsen1,2,3. 1. Department of Integrative Biology, University of California-Berkeley, Berkeley, 94707, USA. 2. Department of Statistics, University of California-Berkeley, Berkeley, CA 94707, USA. 3. Globe Institute, University of Copenhagen, 1350 København K, Denmark.
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. AVAILABILITY AND IMPLEMENTATION: AncestralClust is an Open Source program available at https://github.com/lpipes/ancestralclust. SUPPLEMENTARY INFORMATION: Supplementary figures and table are available online.
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. AVAILABILITY AND IMPLEMENTATION: AncestralClust is an Open Source program available at https://github.com/lpipes/ancestralclust. SUPPLEMENTARY INFORMATION: Supplementary figures and table are available online.
Authors: Douglas B Rusch; Aaron L Halpern; Granger Sutton; Karla B Heidelberg; Shannon Williamson; Shibu Yooseph; Dongying Wu; Jonathan A Eisen; Jeff M Hoffman; Karin Remington; Karen Beeson; Bao Tran; Hamilton Smith; Holly Baden-Tillson; Clare Stewart; Joyce Thorpe; Jason Freeman; Cynthia Andrews-Pfannkoch; Joseph E Venter; Kelvin Li; Saul Kravitz; John F Heidelberg; Terry Utterback; Yu-Hui Rogers; Luisa I Falcón; Valeria Souza; Germán Bonilla-Rosso; Luis E Eguiarte; David M Karl; Shubha Sathyendranath; Trevor Platt; Eldredge Bermingham; Victor Gallardo; Giselle Tamayo-Castillo; Michael R Ferrari; Robert L Strausberg; Kenneth Nealson; Robert Friedman; Marvin Frazier; J Craig Venter Journal: PLoS Biol Date: 2007-03 Impact factor: 8.029