This study developed a computational tool with a graphical interface and a web-service that allows the identification of phage regions through homology search and gene clustering. It uses G+C content variation evaluation and tRNA prediction sites as evidence to reinforce the presence of prophages in indeterminate regions. Also, it performs the functional characterization of the prophages regions through data integration of biological databases. The performance of PhageWeb was compared to other available tools (PHASTER, Prophinder, and PhiSpy) using Sensitivity (Sn) and Positive Predictive Value (PPV) tests. As a reference for the tests, more than 80 manually annotated genomes were used. In the PhageWeb analysis, the Sn index was 86.1% and the PPV was approximately 87%, while the second best tool presented Sn and PPV values of 83.3 and 86.5%, respectively. These numbers allowed us to observe a greater precision in the regions identified by PhageWeb while compared to other prediction tools submitted to the same tests. Additionally, PhageWeb was much faster than the other computational alternatives, decreasing the processing time to approximately one-ninth of the time required by the second best software. PhageWeb is freely available at http://computationalbiology.ufpa.br/phageweb.

With increased production of genomic data since the advent of next-generation sequencing (NGS), there has been a need to develop new bioinformatics tools and areas, such as comparative genomics. In comparative genomics, the genetic material of an organism is directly compared to that of another organism to better understand biological species. Moreover, the exponentially growing number of deposited prokaryote genomes has enabled the investigation of several genomic characteristics that are intrinsic to certain species. Thus, a new approach to comparative genomics, termed pan-genomics, was developed. In pan-genomics, various organisms of the same species or genus are compared. Currently, there are many tools that can perform pan-genomic analyses, such as PGAP (Pan-Genome Analysis Pipeline), Panseq (Pan-Genome Sequence Analysis Program) and PGAT (Prokaryotic Genome Analysis Tool). Among these software tools, PGAP was developed in the Perl scripting language and its reliance on UNIX platform terminals and its requirement for an extensive parameterized command line can become a problem for users without previous computational knowledge. Thus, the aim of this study was to develop a web application, known as PanWeb, that serves as a graphical interface for PGAP. In addition, using the output files of the PGAP pipeline, the application generates graphics using custom-developed scripts in the R programming language. PanWeb is freely available at http://www. computationalbiology.ufpa.br/panweb.

Seven genomes of Corynebacterium pseudotuberculosis biovar equi were sequenced on the Ion Torrent PGM platform, generating high-quality scaffolds over 2.35 Mbp. This bacterium is the causative agent of disease known as “pigeon fever” which commonly affects horses worldwide. The pangenome of biovar equi was calculated and two phylogenomic approaches were used to identify clustering patterns within Corynebacterium genus. Furthermore, other comparative analyses were performed including the prediction of genomic islands and prophages, and SNP-based phylogeny. In the phylogenomic tree, C. pseudotuberculosis was divided into two distinct clades, one formed by nitrate non-reducing species (biovar ovis) and another formed by nitrate-reducing species (biovar equi). In the latter group, the strains isolated from California were more related to each other, while the strains CIP 52.97 and 1/06-A formed the outermost clade of the biovar equi. A total of 1,355 core genes were identified, corresponding to 42.5% of the pangenome. This pangenome has one of the smallest core genomes described in the literature, suggesting a high genetic variability of biovar equi of C. pseudotuberculosis. The analysis of the similarity between the resistance islands identified a higher proximity between the strains that caused more severe infectious conditions (infection in the internal organs). Pathogenicity islands were largely conserved between strains. Several genes that modulate the pathogenicity of C. pseudotuberculosis were described including peptidases, recombination enzymes, micoside synthesis enzymes, bacteriocins with antimicrobial activity and several others. Finally, no genotypic differences were observed between the strains that caused the three different types of infection (external abscess formation, infection with abscess formation in the internal organs, and ulcerative lymphangitis). Instead, it was noted that there is a higher phenetic correlation between strains isolated at California compared to the other strains. Additionally, high variability of resistance islands suggests gene acquisition through several events of horizontal gene transfer.

Even high-accuracy sequencing technologies are subject to the influence of quality filters when evaluating RNA-Seq data using the reference approach.

Corynebacterium pseudotuberculosis causes significant loss to goat and sheep farmers because it is the causal agent of the infectious disease caseous lymphadenitis, which may lead to outcomes ranging from skin injury to animal death (Ruiz et al., 2011) [1]. This bacterium was grown under osmotic (2 M), acid (pH) and heat (50 °C) stress and under control (Normal-BHI brain heart infusion) conditions, which simulate the conditions faced by the bacteria during the infectious process. Subsequently, cDNA of each condition was sequenced by the SOLiD3 Plus platform using the RNA-Seq technique [2], [3], [4]. The data produced was processed to evaluate the differential gene expression, which is helpful to understand the adaptation mechanisms during the infection in the host. The sequencing data of all conditions are available in the European Bioinformatics Institute (EBI) repository under accession number E-MTAB-2017.