The African Human Microbiome Portal: a public web portal of curated metagenomic metadata.

There is growing evidence that comprehensive and harmonized metadata are fundamental for effective public data reusability. However, it is often challenging to extract accurate metadata from public repositories. Of particular concern is the metagenomic data related to African individuals, which often omit important information about the particular features of these populations. As part of a collaborative consortium, H3ABioNet, we created a web portal, namely the African Human Microbiome Portal (AHMP), exclusively dedicated to metadata related to African human microbiome samples. Metadata were collected from various public repositories prior to cleaning, curation and harmonization according to a pre-established guideline and using ontology terms. These metadata sets can be accessed at https://microbiome.h3abionet.org/. This web portal is open access and offers an interactive visualization of 14 889 records from 70 bioprojects associated with 72 peer reviewed research articles. It also offers the ability to download harmonized metadata according to the user's applied filters. The AHMP thereby supports metadata search and retrieve operations, facilitating, thus, access to relevant studies linked to the African Human microbiome. Database URL:  https://microbiome.h3abionet.org/.

Dense sampling of ethnic groups within African countries reveals fine-scale genetic structure and extensive historical admixture.

Previous studies have highlighted how African genomes have been shaped by a complex series of historical events. Despite this, genome-wide data have only been obtained from a small proportion of present-day ethnolinguistic groups. By analyzing new autosomal genetic variation data of 1333 individuals from over 150 ethnic groups from Cameroon, Republic of the Congo, Ghana, Nigeria, and Sudan, we demonstrate a previously underappreciated fine-scale level of genetic structure within these countries, for example, correlating with historical polities in western Cameroon. By comparing genetic variation patterns among populations, we infer that many northern Cameroonian and Sudanese groups share genetic links with multiple geographically disparate populations, likely resulting from long-distance migrations. In Ghana and Nigeria, we infer signatures of intermixing dated to over 2000 years ago, corresponding to reports of environmental transformations possibly related to climate change. We also infer recent intermixing signals in multiple African populations, including Congolese, that likely relate to the expansions of Bantu language-speaking peoples.

A View on Genomic Medicine Activities in Africa: Implications for Policy.

Genomics policy development involves assessing a wide range of issues extending from specimen collection and data sharing to whether and how to utilize advanced technologies in clinical practice and public health initiatives. A survey was conducted among African scientists and stakeholders with an interest in genomic medicine, seeking to evaluate: 1) Their knowledge and understanding of the field. 2) The institutional environment and infrastructure available to them. 3) The state and awareness of the field in their country. 4) Their perception of potential barriers to implementation of precision medicine. We discuss how the information gathered in the survey could instruct the policies of African institutions seeking to implement precision, and more specifically, genomic medicine approaches in their health care systems in the following areas: 1) Prioritization of infrastructures. 2) Need for translational research. 3) Information dissemination to potential users. 4) Training programs for specialized personnel. 5) Engaging political stakeholders and the public. A checklist with key requirements to assess readiness for implementation of genomic medicine programs is provided to guide the process from scientific discovery to clinical application.

African Genomic Medicine Portal: A Web Portal for Biomedical Applications.

Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.

The Extent and Impact of Variation in ADME Genes in Sub-Saharan African Populations.

Introduction: Investigating variation in genes involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs are key to characterizing pharmacogenomic (PGx) relationships. ADME gene variation is relatively well characterized in European and Asian populations, but data from African populations are under-studied-which has implications for drug safety and effective use in Africa. Results: We identified significant ADME gene variation in African populations using data from 458 high-coverage whole genome sequences, 412 of which are novel, and from previously available African sequences from the 1,000 Genomes Project. ADME variation was not uniform across African populations, particularly within high impact coding variation. Copy number variation was detected in 116 ADME genes, with equal ratios of duplications/deletions. We identified 930 potential high impact coding variants, of which most are discrete to a single African population cluster. Large frequency differences (i.e., >10%) were seen in common high impact variants between clusters. Several novel variants are predicted to have a significant impact on protein structure, but additional functional work is needed to confirm the outcome of these for PGx use. Most variants of known clinical outcome are rare in Africa compared to European populations, potentially reflecting a clinical PGx research bias to European populations. Discussion: The genetic diversity of ADME genes across sub-Saharan African populations is large. The Southern African population cluster is most distinct from that of far West Africa. PGx strategies based on European variants will be of limited use in African populations. Although established variants are important, PGx must take into account the full range of African variation. This work urges further characterization of variants in African populations including in vitro and in silico studies, and to consider the unique African ADME landscape when developing precision medicine guidelines and tools for African populations.

Design considerations for workflow management systems use in production genomics research and the clinic.

The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.

Data Management Plans in the genomics research revolution of Africa: Challenges and recommendations.

Drafting and writing a data management plan (DMP) is increasingly seen as a key part of the academic research process. A DMP is a document that describes how a researcher will collect, document, describe, share, and preserve the data that will be generated as part of a research project. The DMP illustrates the importance of utilizing best practices through all stages of working with data while ensuring accessibility, quality, and longevity of the data. The benefits of writing a DMP include compliance with funder and institutional mandates; making research more transparent (for reproduction and validation purposes); and FAIR (findable, accessible, interoperable, reusable); protecting data subjects and compliance with the General Data Protection Regulation (GDPR) and/or local data protection policies. In this review, we highlight the importance of a DMP in modern biomedical research, explaining both the rationale and current best practices associated with DMPs. In addition, we outline various funders' requirements concerning DMPs and discuss open-source tools that facilitate the development and implementation of a DMP. Finally, we discuss DMPs in the context of African research, and the considerations that need to be made in this regard.

H3ABioNet genomic medicine and microbiome data portals hackathon proceedings.

African genomic medicine and microbiome datasets are usually not well characterized in terms of their origin, making it difficult to find and extract data for specific African ethnic groups or even countries. The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for developing data portals for African genomic medicine and African microbiomes to address this and ran a hackathon to initiate their development. The two portals were designed and significant progress was made in their development during the hackathon. All the participants worked in a very synergistic and collaborative atmosphere in order to achieve the hackathon's goals. The participants were divided into content and technical teams and worked over a period of 6 days. In response to one of the survey questions of what the participants liked the most during the hackathon, 55% of the hackathon participants highlighted the familial and friendly atmosphere, the team work and the diversity of team members and their expertise. This paper describes the preparations for the portals hackathon and the interaction between the participants and reflects upon the lessons learned about its impact on successfully developing the two data portals as well as building scientific expertise of younger African researchers. Database URL: The code for developing the two portals was made publicly available in GitHub repositories: [https://github.com/codemeleon/Database; https://github.com/codemeleon/AfricanMicrobiomePortal].

Delivering blended bioinformatics training in resource-limited settings: a case study on the University of Khartoum H3ABioNet node.

MOTIVATION: Delivering high-quality distance-based courses in resource-limited settings is a challenging task. Besides the needed infrastructure and expertise, effective delivery of a bioinformatics course could benefit from hands-on sessions, interactivity and problem-based learning approaches. RESULTS: In this article, we discuss the challenges and best practices in delivering bioinformatics training in resource-limited settings taking the example of hosting and running a multiple-delivery online course, Introduction to Bioinformatics, that was developed by the H3ABioNet Education and Training working group and delivered in 27 remote classrooms across Africa in 2017. We take the case of the University of Khartoum classrooms. Believing that our local setting is similar to others in less-developed countries, we also reflect upon aspects like classroom environment and recruitment of students to maximize outcomes.

Managing genomic variant calling workflows with Swift/T.

Bioinformatics research is frequently performed using complex workflows with multiple steps, fans, merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data: hundreds or thousands of samples at a time. Scientific workflow management systems could help with that. Many are now being proposed, but is there yet the "best" workflow management system for bioinformatics? Such a system would need to satisfy numerous, sometimes conflicting requirements: from ease of use, to seamless deployment at peta- and exa-scale, and portability to the cloud. We evaluated Swift/T as a candidate for such role by implementing a primary genomic variant calling workflow in the Swift/T language, focusing on workflow management, performance and scalability issues that arise from production-grade big data genomic analyses. In the process we introduced novel features into the language, which are now part of its open repository. Additionally, we formalized a set of design criteria for quality, robust, maintainable workflows that must function at-scale in a production setting, such as a large genomic sequencing facility or a major hospital system. The use of Swift/T conveys two key advantages. (1) It operates transparently in multiple cluster scheduling environments (PBS Torque, SLURM, Cray aprun environment, etc.), thus a single workflow is trivially portable across numerous clusters. (2) The leaf functions of Swift/T permit developers to easily swap executables in and out of the workflow, which makes it easy to maintain and to request resources optimal for each stage of the pipeline. While Swift/T's data-level parallelism eliminates the need to code parallel analysis of multiple samples, it does make debugging more difficult, as is common for implicitly parallel code. Nonetheless, the language gives users a powerful and portable way to scale up analyses in many computing architectures. The code for our implementation of a variant calling workflow using Swift/T can be found on GitHub at https://github.com/ncsa/Swift-T-Variant-Calling, with full documentation provided at http://swift-t-variant-calling.readthedocs.io/en/latest/.

Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics.

BACKGROUND: The 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. RESULTS: H3ABioNet 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. CONCLUSION: The 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.

Hackathons as a means of accelerating scientific discoveries and knowledge transfer.

Scientific research plays a key role in the advancement of human knowledge and pursuit of solutions to important societal challenges. Typically, research occurs within specific institutions where data are generated and subsequently analyzed. Although collaborative science bringing together multiple institutions is now common, in such collaborations the analytical processing of the data is often performed by individual researchers within the team, with only limited internal oversight and critical analysis of the workflow prior to publication. Here, we show how hackathons can be a means of enhancing collaborative science by enabling peer review before results of analyses are published by cross-validating the design of studies or underlying data sets and by driving reproducibility of scientific analyses. Traditionally, in data analysis processes, data generators and bioinformaticians are divided and do not collaborate on analyzing the data. Hackathons are a good strategy to build bridges over the traditional divide and are potentially a great agile extension to the more structured collaborations between multiple investigators and institutions.

Organizing and running bioinformatics hackathons within Africa: The H3ABioNet cloud computing experience.

The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous compute environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. To address this need, in 2016 H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon, with the purpose of building key genomics analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on quay.io.

Development of Bioinformatics Infrastructure for Genomics Research.

BACKGROUND: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community. OBJECTIVES: H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. METHODS AND RESULTS: Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. CONCLUSIONS: For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa.

H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa.

The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet.

Constitutively overexpressed 21 kDa protein in Hodgkin lymphoma and aggressive non-Hodgkin lymphomas identified as cytochrome B5b (CYB5B).

BACKGROUND: We have previously reported a novel constitutively overexpressed 21 kDa protein in Hodgkin Lymphoma (HL) and aggressive Non-Hodgkin Lymphomas (NHL). The objective of the current study was to 1) identify this protein using two independent methods, 2) study the expression of the protein and its encoding mRNA in reactive lymph nodes, normal lymphocytes and CD34+ bone marrow precursor cells, 3) analyse patterns of expression of the protein in tissue microarrays assembled from a large number of diagnostic clinical biopsies from patients with HL, and 4) determine the copy number variation and mutation status of the encoding gene in HL cell lines. RESULTS: Peptide sequencing by LC-MS/MS and protein identification by protein array screening identified a single protein, CYB5B. No mutations were detected in the CYB5B gene in HL cell lines. Quantitative PCR showed CYB5B gene expression was increased in HL and NHL cell lines. Array CGH using a submegabase resolution tiling array revealed gains in the CYB5B locus in HL cell lines KMH2 and L428. Membrane expression was seen in Reed-Sternberg cells in clinical biopsies from patients with HL but not in reactive lymph nodes. Bone marrow CD34+ precursor cells were CYB5B negative on the cell surface. RT-PCR assays of RNA extracted from T and B cell enriched fractions obtained from normal peripheral blood mononuclear cells, reactive lymph nodes, tonsils and normal bone marrow samples showed no evidence of increased mRNA levels of CYB5B in comparison to housekeeping gene GAPDH. CONCLUSIONS: The 21 kDa protein overexpressed in HL and aggressive NHL is identical to CYB5B. CYB5B gene expression is increased in a subset of HL and NHL cell lines tested. This is associated with CYB5B gene amplification in HL cell lines KMH2 and L428. CYB5B may be a potential target for antibody-based therapy of HL and aggressive NHL as although cytoplasmic expression is present in reactive lymphocytes, it is not expressed on the cell surface of non-neoplastic lymphocytes or bone marrow precursor cells.

Sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization profiling reveals novel gains and losses of chromosomal regions in Hodgkin Lymphoma and Anaplastic Large Cell Lymphoma cell lines.

BACKGROUND: Hodgkin lymphoma (HL) and Anaplastic Large Cell Lymphoma (ALCL), are forms of malignant lymphoma defined by unique morphologic, immunophenotypic, genotypic, and clinical characteristics, but both overexpress CD30. We used sub-megabase resolution tiling (SMRT) array-based comparative genomic hybridization to screen HL-derived cell lines (KMH2 and L428) and ALCL cell lines (DEL and SR-786) in order to identify disease-associated gene copy number gains and losses. RESULTS: Significant copy number gains and losses were observed on several chromosomes in all four cell lines. Assessment of copy number alterations with 26,819 DNA segments identified an average of 20 genetic alterations. Of the recurrent minimally altered regions identified, 11 (55%) were within previously published regions of chromosomal alterations in HL and ALCL cell lines while 9 (45%) were novel alterations not previously reported. HL cell lines L428 and KMH2 shared gains in chromosome cytobands 2q23.1-q24.2, 7q32.2-q36.3, 9p21.3-p13.3, 12q13.13-q14.1, and losses in 13q12.13-q12.3, and 18q21.32-q23. ALCL cell lines SR-786 and DEL, showed gains in cytobands 5p15.32-p14.3, 20p12.3-q13.11, and 20q13.2-q13.32. Both pairs of HL and ALCL cell lines showed losses in 18q21.32-18q23. CONCLUSION: This study is considered to be the first one describing HL and ALCL cell line genomes at sub-megabase resolution. This high-resolution analysis allowed us to propose novel candidate target genes that could potentially contribute to the pathogenesis of HL and ALCL. FISH was used to confirm the amplification of all three isoforms of the trypsin gene (PRSS1/PRSS2/PRSS3) in KMH2 and L428 (HL) and DEL (ALCL) cell lines. These are novel findings that have not been previously reported in the lymphoma literature, and opens up an entirely new area of research that has not been previously associated with lymphoma biology. The findings raise interesting possibilities about the role of signaling pathways triggered by membrane associated serine proteases in HL and aggressive NHL, similar to those described in epithelial tumors.

Constitutive overexpression of a novel 21 kDa protein by Hodgkin lymphoma and aggressive non-Hodgkin lymphomas.

BACKGROUND: CD30, a 120 kDa surface phosphorylated protein is a member of tumour necrosis/nerve growth factor receptor (TNF/NGFR) family and constitutively expressed by Hodgkin and Reed-Sternberg (HRS) cells of Hodgkin lymphoma (HL) and the neoplastic cells of Anaplastic Large Cell Lymphoma (ALCL). A disease-specific protein marker is yet to be identified in Hodgkin lymphoma cells. In order to define HL-specific biomarkers, novel murine monoclonal antibodies were developed in our laboratory. RESULTS: Murine monoclonal antibodies (mabs) were raised against the B3 sub clone of HL-derived cell line KM-H2. Two of these mabs (clone R23.1 mab and clone R24.1 mab) are IgG1 class antibodies that recognize a 21 kDa protein present at the cell membrane and in the cytoplasm in HL-derived cell lines. Clone R24.1 mab recognizes a formalin-resistant epitope and labels HRS cells in tissue samples from patients with HL of the classical type, ALCL, and subsets of T and B cell aggressive Non-Hodgkin Lymphomas (NHL). The antigen recognized by the clone R23.1 mab and clone R24.1 mab does not share epitopes with CD30 cluster regions A, B, or C, and, unlike CD30, is not expressed by phytohemagglutinin (PHA) activated T cells. CONCLUSION: The 21 kDa protein detected by clone R23.1 and clone R24.1 mabs is a novel membrane-associated protein that may be a potential marker for the diagnosis and targeted therapy of HL and aggressive T and B cell NHL.