GenomeFingerprinter: The Genome Fingerprint and the Universal Genome Fingerprint Analysis for Systematic Comparative Genomics

Abstract

BackgroundNo attention has been paid on comparing a set of genome sequences crossing genetic components and biological categories with far divergence over large size range. We define it as the systematic comparative genomics and aim to develop the methodology.ResultsFirst, we create a method, GenomeFingerprinter, to unambiguously produce a set of three-dimensional coordinates from a sequence, followed by one three-dimensional plot and six two-dimensional trajectory projections, to illustrate the genome fingerprint of a given genome sequence. Second, we develop a set of concepts and tools, and thereby establish a method called the universal genome fingerprint analysis (UGFA). Particularly, we define the total genetic component configuration (TGCC) (including chromosome, plasmid, and phage) for describing a strain as a systematic unit, the universal genome fingerprint map (UGFM) of TGCC for differentiating strains as a universal system, and the systematic comparative genomics (SCG) for comparing a set of genomes crossing genetic components and biological categories. Third, we construct a method of quantitative analysis to compare two genomes by using the outcome dataset of genome fingerprint analysis. Specifically, we define the geometric center and its geometric mean for a given genome fingerprint map, followed by the Euclidean distance, the differentiate rate, and the weighted differentiate rate to quantitatively describe the difference between two genomes of comparison. Moreover, we demonstrate the applications through case studies on various genome sequences, giving tremendous insights into the critical issues in microbial genomics and taxonomy.ConclusionsWe have created a method, GenomeFingerprinter, for rapidly computing, geometrically visualizing, intuitively comparing a set of genomes at genome fingerprint level, and hence established a method called the universal genome fingerprint analysis, as well as developed a method of quantitative analysis of the outcome dataset. These have set up the methodology of systematic comparative genomics based on the genome fingerprint analysis.

GenomeLandscaper: Landscape analysis of genome-fingerprints maps assessing chromosome architecture

“…To conduct landscape analysis of genome-fingerprints maps (GFM) and retrospectively assess the global architectures of assembled chromosomes, we establish the GenomeLandscaper method based on our GenomeFingerprinter algorithm 19 . The steps of working flow with key features are illustrated by using the primates mitochondria genomes (<17.0 Kbp, kilos of base pairs) including Homo sapiens , Pan troglodytes , Gorilla gorilla , Macaca fascicularis and Macaca mulatta (Fig.…”

“…Our GenomeFingerprinter algorithm circularises a linear sequence (Fig. 1a ) to avoid interference from arbitrary cut-off sites of the sequence 19 . It defines a set of functions for the three-dimensional coordinates ( Xn , Yn , Zn ) (Fig.…”

“…It defines a set of functions for the three-dimensional coordinates ( Xn , Yn , Zn ) (Fig. 1b ), where n is the order number of a base (A, T or U, G and C) of a sequence (in length of N ) 19 : …”

“…The GGFMs (Figs 2 and 3 ) present the effective genomes since our GenomeFingerprinter algorithm 19 only calculates non-N (A, T, G and C) bases, bypassing gaps and linking two non-N bases adjacent to the distal ends of a homopolymer of Ns. We deleted the homoploymers of Ns in total 34.19 Mbp to get the cleaned 25.65-Mbp sequence from 59.84-Mbp GRCh37p13.chrY.…”

“…Our hypotheses are: (1) there should be a landscape compatibility among relatives under comparison, and (2) the more the relatives were compared at a large scale, the easier the misassembled architectures could be detected in a big-picture view, thus overcoming the aforementioned central challenge. Based on our GenomeFingerprinter algorithm 19 , here we establish a method (called GenomeLandscaper ) to construct a galaxy of genome-fingerprints maps (GGFM), which comprises a set of genome-fingerprints maps (GFM) that are simultaneously constructed for a set of chromosomes under comparison. Hence we can compare a set of chromosomes in a big-picture view at a large scale.…”