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Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics network

Authors :
Shaun Aron
Mamana Mbiyavanga
Lerato E Magosi
Efejiro Ashano
Christopher J. Fields
C. Victor Jongeneel
Danny Mugutso
Phelelani T. Mpangase
Sumir Panji
Venesa Pillay
Seun Adeyemi
Adaobi Okafor
Oluwadamila Falola
Hocine Bendou
Ananyo Choudhury
Olaleye Oladipo
Ezekiel Adebiyi
Radhika S. Khetani
Ovokeraye Achinike-Oduaran
Bola Akanle
Richard J. Munthali
Suresh Maslamoney
Ayton Meintjes
Gloria Rendon
Nicola Mulder
Trust Odia
Andrew Ndhlovu
Ravikiran Donthu
Itunuoluwa Isewon
Liesl M. Hendry
Emile R. Chimusa
Jenny Drnevich
Judit Kumuthini
Magambo Phillip Kimuda
Scott Hazelhurst
Liudmila Sergeevna Mainzer
Marion O. Adebiyi
Victoria Nembaware
Dhriti Sengupta
Gerrit Botha
Source :
PLoS Computational Biology, PLoS Computational Biology, Vol 13, Iss 6, p e1005419 (2017)
Publication Year :
2017
Publisher :
Public Library of Science, 2017.

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.<br />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.

Details

Language :
English
ISSN :
15537358 and 1553734X
Volume :
13
Issue :
6
Database :
OpenAIRE
Journal :
PLoS Computational Biology
Accession number :
edsair.doi.dedup.....fae71229ad9e76efd968b19f21ce4433