title: Descriptive Statistics of the Genome: Phylogenetic Classification of Viruses.
abstract: The typical process for classifying and submitting a newly sequenced virus to the NCBI database involves two steps. First, a BLAST search is performed to determine likely family candidates. That is followed by checking the candidate families with the pairwise sequence alignment tool for similar species. The submitter's judgment is then used to determine the most likely species classification. The aim of this article is to show that this process can be automated into a fast, accurate, one-step process using the proposed alignment-free method and properly implemented machine learning techniques. We present a new family of alignment-free vectorizations of the genome, the generalized vector, that maintains the speed of existing alignment-free methods while outperforming all available methods. This new alignment-free vectorization uses the frequency of genomic words (k-mers), as is done in the composition vector, and incorporates descriptive statistics of those k-mers' positional information, as inspired by the natural vector. We analyze five different characterizations of genome similarity using k-nearest neighbor classification and evaluate these on two collections of viruses totaling over 10,000 viruses. We show that our proposed method performs better than, or as well as, other methods at every level of the phylogenetic hierarchy. The data and R code is available upon request.
authors.affiliation: 1 Mathematical Sciences Center, Tsinghua University , Beijing, China .
authors.forename: Troy
authors.initials: T
authors.lastname: Hernandez
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D016679:Genome, Viral; D010802:Phylogeny; D016415:Sequence Alignment / Q000379:methods*; D012984:Software; D014780:Viruses / Q000145:classification* / Q000235:genetics
keywords: alignment-free; classification; machine learning; phylogenetics; virology