Evolutionary studies on dengue virus have frequently focused on intra-serotype diversity or on specific epidemics. In this study, we compiled a comprehensive data set of the envelope gene of dengue virus serotypes and conducted an extensive comparative study of evolutionary molecular epidemiology. We found that substitution rates are homogeneous among dengue serotypes, although their population dynamics have differed over the past few years as inferred by Bayesian coalescent methods. On a global scale, DENV-2 is the serotype with the highest effective population size. The genealogies also showed geographical structure within the serotypes. Finally, we also explored the causes of dengue virus serotype diversification by investigating the plausibility that it was driven by adaptive changes. Our results suggest that the envelope gene is under significant purifying selection and the hypothesis that dengue virus serotype diversification was the result of stochastic events cannot be ruled out.

The cervical microbiota composition and diversity of HIV-positive women in the postpartum period is unknown. Using a high-throughput bacterial 16S rRNA gene sequencing, we identified four community state types (CSTs). CST III (Lactobacillusdominant) and CST IV (IV-A, IV-B.1, IV-B.2; high-diversity) were found in 41% and 59% of samples, respectively. We did not find association of any CST to postpartum period (six or twelve months), HPV infection or cytology (normal or lesion). However, five bacterial genera were associated with cervical lesions (Gardnerella, Aerococcus, Schlegelella, Moryella and Bifidobacterium), with significant odds ratio (OR) of 40 (2.28–706) for the presence of Moryella and 3.5 (1.36–8.9) for Schlegelella. Longitudinal analysis of samples at postpartum that regressed (lesion to normal), progressed (normal to lesion) and maintained the cytology (lesion or normal) evidenced Gardnerella with a significantly higher abundance in regressing lesions. In the current study, we report the first data on the cervical microbiota of HIV-positive women in the postpartum period. Consistent with previous studies of HIV-negative cohorts, HIV-positive women present a stable cervical microbiota of high-diversity in the postpartum period. Our results highlight that specific microbiota species may serve as sensors for changes in the cervical microenvironment associated with cervical lesions.

The human papillomavirus (HPV) is present in a significant fraction of head-and-neck squamous cell cancer (HNSCC). The main goal of this study was to identify distinct co-expression patterns between HPV+ and HPV− HNSCC and to provide insights into potential regulatory mechanisms/effects within the analyzed networks. We selected cases deposited in The Cancer Genome Atlas database comprising data of gene expression, methylation profiles and mutational patterns, in addition to clinical information. The intersection among differentially expressed and differentially methylated genes showed the negative correlations between the levels of methylation and expression, suggesting that these genes have their expression levels regulated by methylation alteration patterns in their promoter. Weighted correlation network analysis was used to identify co-expression modules and a systematic approach was applied to refine them and identify key regulatory elements integrating results from the other omics. Three distinct co-expression modules were associated with HPV status and molecular signatures. Validation using independent studies reporting biological experimental data converged for the most significant genes in all modules. This study provides insights into complex genetic and epigenetic particularities in the development and progression of HNSCC according to HPV status, and contribute to unveiling specific genes/pathways as novel therapeutic targets in HNSCC.

The unprecedented size of the human population, along with its associated economic activities, has an ever‐increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide resources. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision‐makers in ways that they can effectively use them. The development and deployment of tools and techniques to generate these indicators require having access to trustworthy data from biological collections, field surveys and automated sensors, molecular data, and historic academic literature. The transformation of these raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques applied to manage and analyze these data constitute an area usually called biodiversity informatics. Biodiversity data follow a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges. This article is categorized under: Algorithmic Development > Biological Data Mining

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There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.

The human papillomavirus (HPV) is present in a significant fraction of head-and-neck squamous cell cancer (HNSCC). However, a comprehensive understanding of disease progression profiles comparing HPV+ and HPV-HNSCC cases is still lacking. The main goal of this study was to identify distinct co-expression patterns between HPV+ and HPV-HNSCC and to provide insights into potential regulatory mechanisms/effects (such as methylation and mutation) within the analyzed networks. For conducting this, we selected 276 samples from The Cancer Genome Atlas database comprising data of gene expression, methylation profiles and mutational patterns, in addition to clinical information (HPV status and tumor staging). We further added external information such as the identification of transcription factors to the networks. Genes were selected as differentially expressed and differentially methylated based on HPV status, of which 12 genes were doubly selected, including SYCP2, GJB6, FLRT3, PITX2 and CCNA1. Weight correlation network analysis was used to identify co-expression modules and a systematic approach was applied to refine them and identify key regulatory elements integrating results from the other omics. Three main modules were associated with distinct co-expression patterns in HPV+ versus HPV-HNSCC. The molecular signatures found were mainly related to cell fate specification, keratinocyte differentiation, focal adhesion and regulation of protein oligomerization. This study provides comprehensive insights into complex genetic and epigenetic particularities in the development and progression of HNSCC in patients according to HPV status, identifying unseen gene interactions, and may contribute to unveiling specific genes/pathways as novel therapeutic targets for HNSCC.

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