A comprehensive whole genome bacterial phylogeny using correlated peptide motifs defined in a high dimensional vector space.
As whole genome sequences continue to expand in number and complexity, effective methods for comparing and categorizing both genes and species represented within extremely large datasets are required. Methods introduced to date have generally utilized incomplete and likely insufficient subsets of the available data. We have developed an accurate and efficient method for producing robust gene and species phylogenies using very large whole genome protein datasets. This method relies on multidimensional protein vector definitions supplied by the singular value decomposition (SVD) of a large sparse data matrix in which each protein is uniquely represented as a vector of overlapping tetrapeptide frequencies. Quantitative pairwise estimates of species similarity were obtained by summing the protein vectors to form species vectors, then determining the cosines of the angles between species vectors. Evolutionary trees produced using this method confirmed many accepted prokaryotic relationships. However, several unconventional relationships were also noted. In addition, we demonstrate that many of the SVD-derived right basis vectors represent particular conserved protein families, while many of the corresponding left basis vectors describe conserved motifs within these families as sets of correlated peptides (copeps). This analysis represents the most detailed simultaneous comparison of prokaryotic genes and species available to date.
Stuart
Gary W
GW
Department of Life Sciences, Indiana State University, Terre Haute, IN 47809, USA. G-Stuart@indstate.edu
Berry
Michael W
MW
D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000235:genetics*; D019295:Computational Biology; D030541:Databases, Genetic; D030562:Databases, Protein; D016680:Genome, Bacterial; D023281:Genomics / Q000706:statistics & numerical data*; D008969:Molecular Sequence Data; D010455:Peptides / Q000235:genetics; D010802:Phylogeny; D040901:Proteomics / Q000706:statistics & numerical data; D012984:Software
D001426:Bacterial Proteins; D010455:Peptides