BackgroundThe Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging.ResultsH3ABioNet has built four workflows to support (1) the calling of variants from high-throughput sequencing data; (2) the analysis of microbial populations from 16S rDNA sequence data; (3) genotyping and genome-wide association studies; and (4) single nucleotide polymorphism imputation. A week-long hackathon was organized in August 2016 with participants from six African bioinformatics groups, and US and European collaborators. Two of the workflows are built using the Common Workflow Language framework (CWL) and two using Nextflow. All the workflows are containerized for improved portability and reproducibility using Docker, and are publicly available for use by members of the H3Africa consortium and the international research community.ConclusionThe H3ABioNet workflows have been implemented in view of offering ease of use for the end user and high levels of reproducibility and portability, all while following modern state of the art bioinformatics data processing protocols. The H3ABioNet workflows will service the H3Africa consortium projects and are currently in use. All four workflows are also publicly available for research scientists worldwide to use and adapt for their respective needs. The H3ABioNet workflows will help develop bioinformatics capacity and assist genomics research within Africa and serve to increase the scientific output of H3Africa and its Pan-African Bioinformatics Network.

Up to 30% of men with normal semen parameters suffer from infertility and the reason for this is unknown. Altered expression of sperm proteins may be a major cause of infertility in these men. Proteomic profiling was performed on pooled semen samples from eight normozoospermic fertile men and nine normozoospermic infertile men using LC-MS/MS. Furthermore, key differentially expressed proteins (DEPs) related to the fertilization process were selected for validation using Western blotting. A total of 1139 and 1095 proteins were identified in normozoospermic fertile and infertile men, respectively. Of these, 162 proteins were identified as DEPs. The canonical pathway related to free radical scavenging was enriched with upregulated DEPs in normozoospermic infertile men. The proteins associated with reproductive system development and function, and the ubiquitination pathway were underexpressed in normozoospermic infertile men. Western blot analysis revealed the overexpression of annexin A2 (ANXA2) (2.03 fold change; P = 0.0243), and underexpression of sperm surface protein Sp17 (SPA17) (0.37 fold change; P = 0.0205) and serine protease inhibitor (SERPINA5) (0.32 fold change; P = 0.0073) in men with unexplained male infertility (UMI). The global proteomic profile of normozoospermic infertile men is different from that of normozoospermic fertile men. Our data suggests that SPA17, ANXA2, and SERPINA5 may potentially serve as non-invasive protein biomarkers associated with the fertilization process of the spermatozoa in UMI.

Background: Neuroblastoma is the most common extracranial solid tumor in childhood. Amplification of MYCN in neuroblastoma is a predictor of poor prognosis. Materials and methods: DNA methylation data from the TARGET data matrix were stratified into MYCN amplified and non-amplified groups. Differential methylation analysis, clustering, recursive feature elimination (RFE), machine learning (ML), Cox regression analysis and Kaplan–Meier estimates were performed. Results and Conclusion: 663 CpGs were differentially methylated between the two groups. A total of 25 CpGs were selected by RFE for clustering and ML, and a 100% clustering accuracy was obtained. ML validation on three external datasets produced high accuracy scores of 100%, 97% and 93%. Eight survival-associated CpGs were also identified. Therapeutic interventions may need to be targeted to patient subgroups.