S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences. (September 2018)

• Record Type: Journal Article
• Authors: Kalesinskas, Laurynas; Cudone, Evan; Fofanov, Yuriy; Putonti, Catherine
• Is Part Of: Evolutionary bioinformatics online. Volume 14(2018)
• Publication Date: 2018-09
• Subjects: comparative genomics -- alignment-free -- gene transfer -- gene loss; Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.1177/1176934318797354
• Languages: English
• ISSNs: 1176-9343

Abstract

With the daily release of data from whole genome sequencing projects, tools to facilitate comparative studies are hard-pressed to keep pace. Graphical software solutions can readily recognize synteny by measuring similarities between sequences. Nevertheless, regions of dissimilarity can prove to be equally informative; these regions may harbor genes acquired via lateral gene transfer (LGT), signify gene loss or gain, or include coding regions under strong selection. Previously, we developed the software S-plot. This tool employed an alignment-free approach for comparing bacterial genomes and generated a heatmap representing the genomes' similarities and dissimilarities in nucleotide usage. In prior studies, this tool proved valuable in identifying genome rearrangements as well as exogenous sequences acquired via LGT in several bacterial species. Herein, we present the next generation of this tool, S-plot2. Similar to its predecessor, S-plot2 creates an interactive, 2-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). This new version, however, includes additional metrics for analysis, new reporting options, and integrated BLAST query functionality for the user to interrogate regions of interest. Furthermore, S-plot2 can evaluate larger sequences, including whole eukaryotic chromosomes. To illustrate some of the applications of the tool, 2 case studies are presented. The first examines strain-specific variation across the Pseudomonas aeruginosa genome and strain-specific LGT events. In the second case study, corresponding human, chimpanzee, and rhesus macaque autosomes were studied and lineage specific contributions to divergence were estimated. S-plot2 provides a means to both visually and quantitatively compare nucleotide sequences, from microbial genomes to eukaryotic chromosomes. The case studies presented illustrate just 2 potential applications of the tool, highlighting its capability to identify and investigate the variation in molecular divergence rates across sequences. S-plot2 is freely available throughhttps://bitbucket.org/lkalesinskas/splot and is supported on the Linux and MS Windows operating systems.