Exploration of phylogenetic data using a global sequence analysis method
1 Equipe de Bioinformatique Génomique et Moléculaire, INSERM U 726, Case 7113, Tour 53-54, 2 place Jussieu, 75005 Paris, France
2 Inserm U494, 91 bd de l'Hopital 75634 Paris CEDEX 13, France
3 Dept. of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138 USA
4 Current address: Dept. of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138 USA
Citation and License
BMC Evolutionary Biology 2005, 5:63 doi:10.1186/1471-2148-5-63Published: 9 November 2005
Molecular phylogenetic methods are based on alignments of nucleic or peptidic sequences. The tremendous increase in molecular data permits phylogenetic analyses of very long sequences and of many species, but also requires methods to help manage large datasets.
Here we explore the phylogenetic signal present in molecular data by genomic signatures, defined as the set of frequencies of short oligonucleotides present in DNA sequences. Although violating many of the standard assumptions of traditional phylogenetic analyses – in particular explicit statements of homology inherent in character matrices – the use of the signature does permit the analysis of very long sequences, even those that are unalignable, and is therefore most useful in cases where alignment is questionable. We compare the results obtained by traditional phylogenetic methods to those inferred by the signature method for two genes: RAG1, which is easily alignable, and 18S RNA, where alignments are often ambiguous for some regions. We also apply this method to a multigene data set of 33 genes for 9 bacteria and one archea species as well as to the whole genome of a set of 16 γ-proteobacteria. In addition to delivering phylogenetic results comparable to traditional methods, the comparison of signatures for the sequences involved in the bacterial example identified putative candidates for horizontal gene transfers.
The signature method is therefore a fast tool for exploring phylogenetic data, providing not only a pretreatment for discovering new sequence relationships, but also for identifying cases of sequence evolution that could confound traditional phylogenetic analysis.