Corynebacterium pseudotuberculosis is a facultative intracellular pathogen and the causative agent of several infectious and contagious chronic diseases, including caseous lymphadenitis, ulcerative lymphangitis, mastitis, and edematous skin disease, in a broad spectrum of hosts. In addition, Corynebacterium pseudotuberculosis infections pose a rising worldwide economic problem in ruminants. The complete genome sequences of 15 C. pseudotuberculosis strains isolated from different hosts and countries were comparatively analyzed using a pan-genomic strategy. Phylogenomic, pan-genomic, core genomic, and singleton analyses revealed close relationships among pathogenic corynebacteria, the clonal-like behavior of C. pseudotuberculosis and slow increases in the sizes of pan-genomes. According to extrapolations based on the pan-genomes, core genomes and singletons, the C. pseudotuberculosis biovar ovis shows a more clonal-like behavior than the C. pseudotuberculosis biovar equi. Most of the variable genes of the biovar ovis strains were acquired in a block through horizontal gene transfer and are highly conserved, whereas the biovar equi strains contain great variability, both intra- and inter-biovar, in the 16 detected pathogenicity islands (PAIs). With respect to the gene content of the PAIs, the most interesting finding is the high similarity of the pilus genes in the biovar ovis strains compared with the great variability of these genes in the biovar equi strains. Concluding, the polymerization of complete pilus structures in biovar ovis could be responsible for a remarkable ability of these strains to spread throughout host tissues and penetrate cells to live intracellularly, in contrast with the biovar equi, which rarely attacks visceral organs. Intracellularly, the biovar ovis strains are expected to have less contact with other organisms than the biovar equi strains, thereby explaining the significant clonal-like behavior of the biovar ovis strains.

Vibrio cholerae is the causal organism of the cholera epidemic, which is mostly prevalent in developing and underdeveloped countries. However, incidences of cholera in developed countries are also alarming. Because of the emergence of new drug-resistant strains, even though several generic drugs and vaccines have been developed over time, Vibrio infections remain a global health problem that appeals for the development of novel drugs and vaccines against the pathogen. Here, applying comparative proteomic and reverse vaccinology approaches to the exoproteome and secretome of the pathogen, we have identified three candidate targets (ompU, uppP and yajC) for most of the pathogenic Vibrio strains. Two targets (uppP and yajC) are novel to Vibrio, and two targets (uppP and ompU) can be used to develop both drugs and vaccines (dual targets) against broad spectrum Vibrio serotypes. Using our novel computational approach, we have identified three peptide vaccine candidates that have high potential to induce both B- and T-cell-mediated immune responses from our identified two dual targets. These two targets were modeled and subjected to virtual screening against natural compounds derived from Piper betel. Seven compounds were identified first time from Piper betel to be highly effective to render the function of these targets to identify them as emerging potential drugs against Vibrio. Our preliminary validation suggests that these identified peptide vaccines and betel compounds are highly effective against Vibrio cholerae. Currently we are exhaustively validating these targets, candidate peptide vaccines, and betel derived lead compounds against a number of Vibrio species.

The number of genomes that have been deposited in databases has increased exponentially after the advent of Next-Generation Sequencing (NGS), which produces high-throughput sequence data; this circumstance has demanded the development of new bioinformatics software and the creation of new areas, such as comparative genomics. In comparative genomics, the genetic content of an organism is compared against other organisms, which helps in the prediction of gene function and coding region sequences, identification of evolutionary events and determination of phylogenetic relationships. However, expanding comparative genomics to a large number of related bacteria, we can infer their lifestyles, gene repertoires and minimal genome size. In this context, a powerful approach called Pan-genome has been initiated and developed. This approach involves the genomic comparison of different strains of the same species, or even genus. Its main goal is to establish the total number of non-redundant genes that are present in a determined dataset. Pan-genome consists of three parts: core genome; accessory or dispensable genome; and species-specific or strain-specific genes. Furthermore, pan-genome is considered to be “open” as long as new genes are added significantly to the total repertoire for each new additional genome and “closed” when the newly added genomes cannot be inferred to significantly increase the total repertoire of the genes. To perform all of the required calculations, a substantial amount of software has been developed, based on orthologous and paralogous gene identification.

Downstream analysis of genomic and transcriptomic sequence data is often executed by functional annotation that can be performed by various bioinformatics tools and biological databases. However, a full fast integrated tool is not available for such analysis. Besides, the current available software is not able to produce analytic lists of annotations and graphs to help users in evaluating the output results. Therefore, we present the Gene Ontology Functional Enrichment Annotation Tool (GO FEAT), a free web platform for functional annotation and enrichment of genomic and transcriptomic data based on sequence homology search. The analysis can be customized and visualized as per users’ needs and specifications. GO FEAT is freely available at http://computationalbiology.ufpa.br/gofeat/ and its source code is hosted at https://github.com/fabriciopa/gofeat.

A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four different cancer types from The Cancer Genome Atlas (TCGA) datasets using four autoencoder implementations. We also predicted the optimal number of subtypes in a cancer type using the silhouette score and found that the detected subtypes exhibit significant differences in survival profiles. Furthermore, we compared the effect of feature selection and similarity measures for subtype detection. For further evaluation, we used the Glioblastoma multiforme (GBM) dataset and identified the differentially expressed genes in each of the subtypes. The results obtained are consistent with other genomic studies and can be corroborated with the involved pathways and biological functions. Thus, it shows that the results from the autoencoders, obtained through the interaction of different datatypes of cancer, can be used for the prediction and characterization of patient subgroups and survival profiles.