An efficient numerical representation of genome sequence: natural vector with covariance component

Abstract

Background The characterization and comparison of microbial sequences, including archaea, bacteria, viruses and fungi, are very important to understand their evolutionary origin and the population relationship. Most methods are limited by the sequence length and lack of generality. The purpose of this study is to propose a general characterization method, and to study the classification and phylogeny of the existing datasets. Methods We present a new alignment-free method to represent and compare biological sequences. By adding the covariance between each two nucleotides, the new 18-dimensional natural vector successfully describes 24,250 genomic sequences and 95,542 DNA barcode sequences. The new numerical representation is used to study the classification and phylogenetic relationship of microbial sequences. Results First, the classification results validate that the six-dimensional covariance vector is necessary to characterize sequences. Then, the 18-dimensional natural vector is further used to conduct the similarity relationship between giant virus and archaea, bacteria, other viruses. The nearest distance calculation results reflect that the giant viruses are closer to bacteria in distribution of four nucleotides. The phylogenetic relationships of the three representative families, Mimiviridae, Pandoraviridae and Marsellieviridae from giant viruses are analyzed. The trees show that ten sequences of Mimiviridae are clustered with Pandoraviridae, and Mimiviridae is closer to the root of the tree than Marsellieviridae. The new developed alignment-free method can be computed very fast, which provides an effective numerical representation for the sequence of microorganisms.

In-depth investigation of the point mutation pattern of HIV-1

“…The other approach is based on alignment-free, which mainly include methods based on k-mer frequency ( Sims et al., 2009 ), the length of common substrings ( Leimeister and Morgenstern, 2014 ), graphical representation ( Jeffrey, 1990 ), micro-alignments ( Yi and Jin, 2013 ), and the number of word matches ( Bromberg et al., 2016 ). Our team proposed k-mer natural vector to compare genomic sequences ( Deng et al., 2011 ), which has been successfully applied to many classification and phylogenetic tasks ( Sun et al., 2021 ; Sun et al., 2022a ; Sun et al., 2022b ). K-mer natural vector characterizes the statistical distribution of k-mers.…”

MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data

“…Fourth, though applicable to any type of ordinal predictor, MANOCCA is currently restricted to continuous outcomes and unstructured observations. Other fields of application interested in changes in outcome relationships, such as the study of mutation mechanisms in genomic sequences [ 46 ], the study of longitudinal data (changes of covariances across different timepoints) or covariances across related individuals, would require further work to be studied using MANOCCA.…”