 Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics networkC Victor Jongeneel, Ovokeraye Achinike-Oduaran, Ezekiel Adebiyi, Marion Adebiyi, Seun Adeyemi, Bola Akanle, Shaun Aron, Efejiro Ashano, Hocine Bendou, Gerrit Botha, Emile Chimusa, Ananyo Choudhury, Ravikiran Donthu, Jenny Drnevich, Oluwadamila Falola, Christopher J Fields, Scott Hazelhurst, Liesl Hendry, Itunuoluwa Isewon, Radhika S Khetani, Judit Kumuthini, Magambo Phillip Kimuda, Lerato Magosi, Liudmila Sergeevna Mainzer, Suresh Maslamoney, Mamana Mbiyavanga, Ayton Meintjes, Danny Mugutso, Phelelani Mpangase, Richard Munthali, Victoria Nembaware, Andrew Ndhlovu, Trust Odia, Adaobi Okafor, Olaleye Oladipo, Sumir Panji, Venesa Pillay, Gloria Rendon, Dhriti Sengupta and Nicola Mulder PLOS Computational Biology, 2017, vol. 13, issue 6, 1-10 Abstract: 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.Author summary: Many programs have been developed to boost the technical and computational skills of scientists working in low to medium income countries (LMIC), who often struggle to remain competitive with their peers in more developed parts of the world. Typically, these programs rely on intensive workshops where students acquire and exercise these skills under the supervision of experienced trainers. However, when trainees return to their home institutions, even after extensive exposure to state of the art techniques, they often find it difficult to put the skills they have acquired into practice and to establish themselves as fully independent practitioners. We have attempted to build a framework through which teams of scientists in African research groups can demonstrate that they have acquired the necessary skills to analyze different types of genomic datasets. Three teams of scientists who have successfully submitted to this assessment exercise report their positive experiences. Many potential participants have so far declined the opportunity, and we discuss the reasons for their reluctance as well as possible ways to facilitate their engagement and provide them with incentives. We argue that assessments such as this could be part of any program aiming to develop technical skills in scientists wishing to support genomic research programs. Date: 2017 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005419 DOI: 10.1371/journal.pcbi.1005419 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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