ArticleEvolutionary bioinformatics online2018
S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences.
Article in Evolutionary bioinformatics online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 10 citations in OpenAlex.
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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
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