Proposed minimum information guideline for kidney disease—research and clinical data reporting: a cross-sectional study

Objective This project aimed to develop and propose a standardised reporting guideline for kidney disease research and clinical data reporting, in order to improve kidney disease data quality and integrity, and combat challenges associated with the management and challenges of ‘Big Data’. Methods A list of recommendations was proposed for the reporting guideline based on the systematic review and consolidation of previously published data collection and reporting standards, including PhenX measures and Minimal Information about a Proteomics Experiment (MIAPE). Thereafter, these recommendations were reviewed by domain-specialists using an online survey, developed in Research Electronic Data Capture (REDCap). Following interpretation and consolidation of the survey results, the recommendations were mapped to existing ontologies using Zooma, Ontology Lookup Service and the Bioportal search engine. Additionally, an associated eXtensible Markup Language schema was created for the REDCap implementation to increase user friendliness and adoption. Results The online survey was completed by 53 respondents; the majority of respondents were dual clinician-researchers (57%), based in Australia (35%), Africa (33%) and North America (22%). Data elements within the reporting standard were identified as participant-level, study-level and experiment-level information, further subdivided into essential or optional information. Conclusion The reporting guideline is readily employable for kidney disease research projects, and also adaptable for clinical utility. The adoption of the reporting guideline in kidney disease research can increase data quality and the value for long-term preservation, ensuring researchers gain the maximum benefit from their collected and generated data. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial

Genomic Research Data Generation, Analysis and Sharing – Challenges in the African Setting

International audienceGenomics is the study of the genetic material that constitutes the genomes of organisms. This genetic material can be sequenced and it provides a powerful tool for the study of human, plant and animal evolutionary history and diseases. Genomics research is becoming increasingly commonplace due to significant advances in and reducing costs of technologies such as sequencing. This has led to new challenges including increasing cost and complexity of data. There is, therefore, an increasing need for computing infrastructure and skills to manage, store, analyze and interpret the data. In addition, there is a significant cost associated with recruitment of participants and collection and processing of biological samples, particularly for large human genetics studies on specific diseases. As a result, researchers are often reluctant to share the data due to the effort and associated cost. In Africa, where researchers are most commonly at the study recruitment, determination of phenotypes and collection of biological samples end of the genomic research spectrum, rather than the generation of genomic data, data sharing without adequate safeguards for the interests of the primary data generators is a concern. There are substantial ethical considerations in the sharing of human genomics data. The broad consent for data sharing preferred by genomics researchers and funders does not necessarily align with the expectations of researchers, research participants, legal authorities and bioethicists. In Africa, this is complicated by concerns about comprehension of genomics research studies, quality of research ethics reviews and understanding of the implications of broad consent, secondary analyses of shared data, return of results and incidental findings. Additional challenges with genomics research in Africa include the inability to transfer, store, process and analyze large-scale genomics data on the continent, because this requires highly specialized skills and expensive computing infrastructure which are often unavailable. Recently initiatives such as H3Africa and H3ABioNet which aim to build capacity for large-scale genomics projects in Africa have emerged. Here we describe such initiatives, including the challenges faced in the generation, analysis and sharing of genomic data and how these challenges are being overcome

Equity, diversity, and inclusion at the Global Alliance for Genomics and Health

A lack of diversity in genomics for health continues to hinder equitable leadership and access to precision medicine approaches for underrepresented populations. To avoid perpetuating biases within the genomics workforce and genomic data collection practices, equity, diversity, and inclusion (EDI) must be addressed. This paper documents the journey taken by the Global Alliance for Genomics and Health (a genomics-based standard-setting and policy-framing organization) to create a more equitable, diverse, and inclusive environment for its standards and members. Initial steps include the creation of two groups: the Equity, Diversity, and Inclusion Advisory Group and the Regulatory and Ethics Diversity Group. Following a framework that we call "Reflected in our Teams, Reflected in our Standards," both groups address EDI at different stages in their policy development process. [Abstract copyright: © 2023 The Author(s).

Development of Bioinformatics Infrastructure for Genomics Research:

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

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

A Multimedia Capable Distributed Object Platform

Though distributed object computing has developed very rapidly over the past few years, the area is still  lacking in terms of support for distributed multimedia applications which are fast becoming a very important  part of the computing environment. This paper discusses the design and implementation of a  multimediacapable distributed object platform, using a low-level platform GOPI (General Object Platform Infrastructure). It involves the design of a minimal Interface Definition Language (IDL) compiler for the support of simple operations, using the specifications of the Object Management Group’s (OMG) IDL. Based on the computational model of the Reference Model for Open Distributed Processing (RM-ODP), the IDL is then  extended to support stream interfaces along with quality of service (QoS).Running Title: A Multimedia-capable Distributed Object PlatformKeywords: Multimedia; GOPI; IDL; CORBA; Quality of Service; Middleware; Distributed Systems

Coulson, G., Baichoo, S., "Experiences in Implementing a Distributed Object

Two current trends in distributed computing are the emergence of standardised distributed object platforms such as CORBA, and the increasing use of continuous media data types. This paper describes and evaluates a platform which supports both standard CORBA interactions and continuous media interactions in a fully integrated environment. The platform is implemented as a self-contained support infrastructure (GOPI core) and a separate `personality' that sits on top of the infrastructure and provides a CORBA API. The platform user can create both request/ reply oriented and stream oriented bindings with quality of service specifications that are honoured (as far as possible) by the infrastructure. A framework for quality of service management which involves the monitoring and adaptation of quality levels is also supported. The level of performance attained by the platform provides evidence of the feasibility of natively supporting continuous media in a distributed object platform environment

The tumor mutational landscape of BRCA2-deficient primary and metastatic prostate cancer

Abstract Carriers of germline BRCA2 pathogenic sequence variants have elevated aggressive prostate cancer risk and are candidates for precision oncology treatments. We examined whether BRCA2-deficient (BRCA2 d ) prostate tumors have distinct genomic alterations compared with BRCA2-intact (BRCA2 i ) tumors. Among 2536 primary and 899 metastatic prostate tumors from the ICGC, GENIE, and TCGA databases, we identified 138 primary and 85 metastatic BRCA2 d tumors. Total tumor mutation burden (TMB) was higher among primary BRCA2 d tumors, although pathogenic TMB did not differ by tumor BRCA2 status. Pathogenic and total single nucleotide variant (SNV) frequencies at KMT2D were higher in BRCA2 d primary tumors, as was the total SNV frequency at KMT2D in BRCA2 d metastatic tumors. Homozygous deletions at NEK3, RB1, and APC were enriched in BRCA2 d primary tumors, and RB1 deletions in metastatic BRCA2 d tumors as well. TMPRSS2-ETV1 fusions were more common in BRCA2 d tumors. These results identify somatic alterations that hallmark etiological and prognostic differences between BRCA2 d and BRCA2 i prostate tumors