Whole-genome prokaryotic phylogeny

Stefan R. Henz; Daniel H. Huson; Alexander F. Auch; Katja Nieselt-Struwe; Stephan C. Schuster

doi:10.1093/bioinformatics/bth324

Abstract

Current understanding of the phylogeny of prokaryotes is based on the comparison of the highly conserved small ssu-rRNA subunit and similar regions. Although such molecules have proved to be very useful phylogenetic markers, mutational saturation is a problem, due to their restricted lengths. Now, a growing number of complete prokaryotic genomes are available. This paper addresses the problem of determining a prokaryotic phylogeny utilizing the comparison of complete genomes. We introduce a new strategy, GBDP, 'genome blast distance phylogeny', and show that different variants of this approach robustly produce phylogenies that are biologically sound, when applied to 91 prokaryotic genomes. In this approach, first Blast is used to compare genomes, then a distance matrix is computed, and finally a tree- or network-reconstruction method such as UPGMA, Neighbor-Joining, BioNJ or Neighbor-Net is applied.

Keywords

PhylogeneticsGenomeComputational biologyBiologyEvolutionary biologyGeneticsGene

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Publication Info

Year: 2004
Type: article
Volume: 21
Issue: 10
Pages: 2329-2335
Citations: 248
Access: Closed

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APA Style

                            
                                
                                    Stefan R. Henz, 
                                
                                    Daniel H. Huson, 
                                
                                    Alexander F. Auch
                                
                                et al.
                            
                            (2004). 
                            Whole-genome prokaryotic phylogeny. 
                            Computer applications in the biosciences
                            , 21
                            (10)
                            , 2329-2335.
                            https://doi.org/10.1093/bioinformatics/bth324
                        

Identifiers

DOI: 10.1093/bioinformatics/bth324