title: AST: an automated sequence-sampling method for improving the taxonomic diversity of gene phylogenetic trees.
abstract: A challenge in phylogenetic inference of gene trees is how to properly sample a large pool of homologous sequences to derive a good representative subset of sequences. Such a need arises in various applications, e.g. when (1) accuracy-oriented phylogenetic reconstruction methods may not be able to deal with a large pool of sequences due to their high demand in computing resources; (2) applications analyzing a collection of gene trees may prefer to use trees with fewer operational taxonomic units (OTUs), for instance for the detection of horizontal gene transfer events by identifying phylogenetic conflicts; and (3) the pool of available sequences is biased towards extensively studied species. In the past, the creation of subsamples often relied on manual selection. Here we present an Automated sequence-Sampling method for improving the Taxonomic diversity of gene phylogenetic trees, AST, to obtain representative sequences that maximize the taxonomic diversity of the sampled sequences. To demonstrate the effectiveness of AST, we have tested it to solve four problems, namely, inference of the evolutionary histories of the small ribosomal subunit protein S5 of E. coli, 16 S ribosomal RNAs and glycosyl-transferase gene family 8, and a study of ancient horizontal gene transfers from bacteria to plants. Our results show that the resolution of our computational results is almost as good as that of manual inference by domain experts, hence making the tool generally useful to phylogenetic studies by non-phylogeny specialists. The program is available at http://csbl.bmb.uga.edu/~zhouchan/AST.php.
authors.affiliation: Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America.
authors.forename: Chan
authors.initials: C
authors.lastname: Zhou
authors.affiliation: Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America.
authors.forename: Fenglou
authors.initials: F
authors.lastname: Mao
authors.affiliation: Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America.
authors.forename: Yanbin
authors.initials: Y
authors.lastname: Yin
authors.affiliation: Department of Biology, East Carolina University, Greenville, North Carolina, United States of America.
authors.forename: Jinling
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Molecular and Cell Biology, University of Connecticut, Storrs, Connecticut, United States of America.
authors.forename: Johann Peter
authors.initials: JP
authors.lastname: Gogarten
authors.affiliation: Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America; College of Computer Science and Technology, Jilin University, Changchun, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Xu
mesh_terms: D019295:Computational Biology / Q000379:methods*; D010802:Phylogeny; D012335:RNA, Ribosomal / Q000235:genetics; D017422:Sequence Analysis, DNA
chemical_list: D012335:RNA, Ribosomal