title: CGAT: a model for immersive personalized training in computational genomics.
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.
authors.forename: David
authors.initials: D
authors.lastname: Sims
authors.forename: Chris P
authors.initials: CP
authors.lastname: Ponting
authors.forename: Andreas
authors.initials: A
authors.lastname: Heger
mesh_terms: D035843:Biomedical Research; D019295:Computational Biology / Q000193:education* / Q000379:methods; D023281:Genomics / Q000193:education* / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods*
keywords: bioinformatics; genomics; integrative biology; next-generation sequencing; training