CGAT: a model for immersive personalized training in computational genomics.

A Heger,C P Ponting,D Sims

doi:10.1093/bfgp/elv021

Abstract

How should the next generation of genomics scientists be trained while simultaneously pursuing high quality and diverse research? CGAT, the Computational Genomics Analysis and Training programme, was set up in 2010 by the UK Medical Research Council to complement its investment in next-generation sequencing capacity. CGAT was conceived around the twin goals of training future leaders in genome biology and medicine, and providing much needed capacity to UK science for analysing genome scale data sets. Here we outline the training programme employed by CGAT and describe how it dovetails with collaborative research projects to launch scientists on the road towards independent research careers in genomics.

Highlights

The acute skills shortage in computational biology is widely acknowledged [1] and has been greatly magnified by the advent of more affordable genomic technologies, in particular nextgeneration sequencing
How should the generation of genomics scientists be trained while simultaneously pursuing high quality and diverse research? CGAT, the Computational Genomics Analysis and Training programme, was set up in 2010 by the UK Medical Research Council to complement its investment in next-generation sequencing capacity
We outline the training programme employed by CGAT and describe how it dovetails with collaborative research projects to launch scientists on the road towards independent research careers in genomics

Summary

Introduction

The acute skills shortage in computational biology is widely acknowledged [1] and has been greatly magnified by the advent of more affordable genomic technologies, in particular nextgeneration sequencing. The Computational Genomics Analysis and Training programme (CGAT, http://cgat.org) is founded on the premise that many genomic projects stall once standard processing of genomic data has completed This bottleneck is a direct consequence of a shortage of individuals who can draw both on their biological knowledge to ask the pertinent questions, and on their computational skills and statistical knowledge to perform the relevant analyses, thereby ensuring that the results of their analyses are interpreted appropriately. CGAT fellows are presented with the opportunity to design their own research project (Figure 1) They are required to contact potential collaborators and with them design a genomics experiment that addresses a key question in their field, or provides pilot data for a future fellowship or grant application. We keep them actively involved in developing the CGAT code repository and encouraging others to contribute to make it a growing, up-to-date resource for reproducible and open-access genomics analysis

Conclusions and future perspectives

Key points