Background and objectives 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 researchers to analyse 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. The network 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 enrolment, 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. Since 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. Conclusion 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 programmes. Here, we describe the infrastructure and how it has impacted genomics and bioinformatics research in Africa.

In spite of the prominence of extreme learning machine model, as well as its excellent features such as insignificant intervention for learning and model tuning, the simplicity of implementation, and high learning speed, which makes it a fascinating alternative method for Artificial Intelligence, including Big Data Analytics, it is still limited in certain aspects. These aspects must be treated to achieve an effective and cost-sensitive model. This review discussed the major drawbacks of ELM, which include difficulty in determination of hidden layer structure, prediction instability and Imbalanced data distributions, the poor capability of sample structure preserving (SSP), and difficulty in accommodating lateral inhibition by direct random feature mapping. Other drawbacks include multi-graph complexity, global memory size, one-by-one or chuck-by-chuck (a block of data), global memory size limitation, and challenges with big data. The recent trend proposed by experts for each drawback is discussed in detail towards achieving an effective and cost-sensitive model.

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so.PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi

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