OutbreakFinder: a visualization tool for rapid detection of bacterial strain clusters based on optimized multidimensional scaling.

With the evolution of next generation sequencing (NGS) technologies, whole-genome sequencing of bacterial isolates is increasingly employed to investigate epidemiology. Phylogenetic analysis is the common method for using NGS data, usually for comparing closeness between bacterial isolates to detect probable outbreaks. However, interpreting a phylogenetic tree is not easy without training in evolutionary biology. Therefore, developing an easy-to-use tool that can assist people who wish to use a phylogenetic tree to investigate epidemiological relatedness is crucial. In this paper, we present a tool called OutbreakFinder that can accept a distance matrix in csv format; alignment files from Lyve-SET, Parsnp, and ClustalOmega; and a tree file in Newick format as inputs to compute a cluster-labeled two-dimensional plot based on multidimensional-scaling dimension reduction coupled with affinity propagation clustering. OutbreakFinder can be downloaded for free at https://github.com/skypes/Newton-method-MDS.

A New Genome-to-Genome Comparison Approach for Large-Scale Revisiting of Current Microbial Taxonomy.

Microbial diversity has always presented taxonomic challenges. With the popularity of next-generation sequencing technology, more unculturable bacteria have been sequenced, facilitating the discovery of additional new species and complicated current microbial classification. The major challenge is to assign appropriate taxonomic names. Hence, assessing the consistency between taxonomy and genomic relatedness is critical. We proposed and applied a genome comparison approach to a large-scale survey to investigate the distribution of genomic differences among microorganisms. The approach applies a genome-wide criterion, homologous coverage ratio (HCR), for describing the homology between species. The survey included 7861 microbial genomes that excluded plasmids, and 1220 pairs of genera exhibited ambiguous classification. In this study, we also compared the performance of HCR and average nucleotide identity (ANI). The results indicated that HCR and ANI analyses yield comparable results, but a few examples suggested that HCR has a superior clustering effect. In addition, we used the Genome Taxonomy Database (GTDB), the gold standard for taxonomy, to validate our analysis. The GTDB offers 120 ubiquitous single-copy proteins as marker genes for species classification. We determined that the analysis of the GTDB still results in classification boundary blur between some genera and that the marker gene-based approach has limitations. Although the choice of marker genes has been quite rigorous, the bias of marker gene selection remains unavoidable. Therefore, methods based on genomic alignment should be considered for use for species classification in order to avoid the bias of marker gene selection. On the basis of our observations of microbial diversity, microbial classification should be re-examined using genome-wide comparisons.

PathoBacTyper: A Web Server for Pathogenic Bacteria Identification and Molecular Genotyping.

With the decline in the cost of whole-genome sequencing because of the introduction of next-generation sequencing (NGS) techniques, many public health and clinical laboratories have started to use bacterial whole genomes for epidemiological surveillance and clinical investigation. For epidemiological and clinical purposes in this "NGS era," whole-genome-scale single nucleotide polymorphism (wgSNP) analysis for genotyping is considered suitable. In this paper, we present an online service, PathoBacTyper (http://halst.nhri.org.tw/PathoBacTyper/), for pathogenic bacteria identification and genotyping based on wgSNP analysis. More than 400 pathogenic bacteria can be identified and genotyped through this service. Four data sets containing 59 Salmonella Heidelberg isolates from three outbreaks with the same pulsed-field gel electrophoresis pattern, 34 Salmonella Typhimurium isolates from six outbreaks, 103 isolates of hospital-associated vancomycin-resistant Enterococcus faecium and 15 Legionella pneumophila isolates from clinical and environmental samples in Israel were used for demonstrating the operation and testing the performance of the PathoBacTyper service. The test results reveal the applicability of this service for epidemiological typing and clinical investigation.

GEMSiRV: a software platform for GEnome-scale metabolic model simulation, reconstruction and visualization.

MOTIVATION: Genome-scale metabolic network models have become an indispensable part of the increasingly important field of systems biology. Metabolic systems biology studies usually include three major components-network model construction, objective- and experiment-guided model editing and visualization, and simulation studies based mainly on flux balance analyses. Bioinformatics tools are required to facilitate these complicated analyses. Although some of the required functions have been served separately by existing tools, a free software resource that simultaneously serves the needs of the three major components is not yet available. RESULTS: Here we present a software platform, GEMSiRV (GEnome-scale Metabolic model Simulation, Reconstruction and Visualization), to provide functionalities of easy metabolic network drafting and editing, amenable network visualization for experimental data integration and flux balance analysis tools for simulation studies. GEMSiRV comes with downloadable, ready-to-use public-domain metabolic models, reference metabolite/reaction databases and metabolic network maps, all of which can be input into GEMSiRV as the starting materials for network construction or simulation analyses. Furthermore, all of the GEMSiRV-generated metabolic models and analysis results, including projects in progress, can be easily exchanged in the research community. GEMSiRV is a powerful integrative resource that may facilitate the development of systems biology studies. AVAILABILITY: The software is freely available on the web at http://sb.nhri.org.tw/GEMSiRV.

ATIVS: analytical tool for influenza virus surveillance.

The WHO Global Influenza Surveillance Network has routinely performed genetic and antigenic analyses of human influenza viruses to monitor influenza activity. Although these analyses provide supporting data for the selection of vaccine strains, it seems desirable to have user-friendly tools to visualize the antigenic evolution of influenza viruses for the purpose of surveillance. To meet this need, we have developed a web server, ATIVS (Analytical Tool for Influenza Virus Surveillance), for analyzing serological data of all influenza viruses and hemagglutinin sequence data of human influenza A/H3N2 viruses so as to generate antigenic maps for influenza surveillance and vaccine strain selection. Functionalities are described and examples are provided to illustrate its usefulness and performance. The ATIVS web server is available at http://influenza.nhri.org.tw/ATIVS/.