In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at https://hihisiv.github.io , provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.

The increasing volumes of data produced by high-throughput instruments coupled with advanced computational infrastructures for scientific computing have enabled what is often called a Fourth Paradigm for scientific research based on the exploration of large datasets. Current scientific research is often interdisciplinary, making data integration a critical technique for combining data from different scientific domains. Research data management is a critical part of this paradigm, through the proposition and development of methods, techniques, and practices for managing scientific data through their life cycle. Research on microbial communities follows the same pattern of production of large amounts of data obtained, for instance, from sequencing organisms present in environmental samples. Data on microbial communities can come from a multitude of sources and can be stored in different formats. For example, data from metagenomics, metatranscriptomics, metabolomics, and biological imaging are often combined in studies. In this article, we describe the design and current state of implementation of an integrative research data management framework for the Cluster of Excellence Balance of the Microverse aiming to allow for data on microbial communities to be more easily discovered, accessed, combined, and reused. This framework is based on research data repositories and best practices for managing workflows used in the analysis of microbial communities, which includes recording provenance information for tracking data derivation.

Reproducibility is a topic that has received significant attention in recent years. Despite being considered a fundamental factor in the scientific process, recent surveys have shown the difficulty of reproducing already published works, which impacts scientists’ ability to verify, validate, and reuse research findings. Recording provenance data is one of the approaches that can help to mitigate the challenges involved in the reproducibility process. When semantically well defined, provenance can describe the entire process involved in producing a given result. Additionally, the use of semantic web technologies can allow for the provenance data to be machine-actionable. With a focus on computational experiments, this work presents a package for collecting and describing provenance data from R scripts using the REPRODUCE-ME ontology to describe the path taken to produce results. We describe the package implementation process and demonstrate how it can help describe the story of experiments defined as R scripts to support reproducibility.

Science gateways bring out the possibility of reproducible science as they are integrated into reusable techniques, data and workflow management systems, security mechanisms, and high performance computing (HPC). We introduce BioinfoPortal, a science gateway that integrates a suite of different bioinformatics applications using HPC and data management resources provided by the Brazilian National HPC System (SINAPAD). BioinfoPortal follows the Software as a Service (SaaS) model and the web server is freely available for academic use. The goal of this paper is to describe the science gateway and its usage, addressing challenges of designing a multiuser computational platform for parallel/distributed executions of large-scale bioinformatics applications using the Brazilian HPC resources. We also present a study of performance and scalability of some bioinformatics applications executed in the HPC environments and perform machine learning analyses for predicting features for the HPC allocation/usage that could better perform the bioinformatics applications via BioinfoPortal.

Monitoring the execution of distributed tasks within the workflow execution is not easy and is frequently controlled manually. This work presents a lightweight middleware monitor to design and control the parallel execution of tasks from a distributed scientific workflow. This middleware can be connected into a workflow management system. This middleware implementation is evaluated with the Kepler workflow management system, by including new modules to control and monitor the distributed execution of the tasks. These middleware modules were added to a bio informatics workflow to monitor parallel BLAST executions. Results show potential to high performance process execution while preserving the original features of the workflow.

Secure provenance techniques are essential in generating trustworthy provenance records, where one is interested in protecting their integrity, confidentiality, and availability. In this work, we suggest an architecture to provide protection of authorship and temporal information in grid-enabled provenance systems. It can be used in the resolution of conflicting intellectual property claims, and in the reliable chronological reconstitution of scientific experiments. We observe that some techniques from public key infrastructures can be readily applied for this purpose. We discuss the issues involved in the implementation of such architecture and describe some experiments realized with the proposed techniques.