Distributed gene expression modelling for exploring variability in epigenetic function

BMC Bioinformatics

Research Article

David M. Budden¹  Edmund J. Crampin² 

[1] Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, 02139, Cambridge, USA;Systems Biology Laboratory, Melbourne School of Engineering, the University of Melbourne, 3010, Parkville, Australia;Systems Biology Laboratory, Melbourne School of Engineering, the University of Melbourne, 3010, Parkville, Australia;ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, 3010, Parkville, Australia;Department of Mathematics and Statistics, the University of Melbourne, 3010, Parkville, Australia;School of Medicine, the University of Melbourne, 3010, Parkville, Australia;

关键词: Gene expression;    Epigenetics;    Histone modifications;    MapReduce;   

DOI  :  10.1186/s12859-016-1313-1

 received in 2016-08-03, accepted in 2016-10-25,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundPredictive gene expression modelling is an important tool in computational biology due to the volume of high-throughput sequencing data generated by recent consortia. However, the scope of previous studies has been restricted to a small set of cell-lines or experimental conditions due an inability to leverage distributed processing architectures for large, sharded data-sets.ResultsWe present a distributed implementation of gene expression modelling using the MapReduce paradigm and prove that performance improves as a linear function of available processor cores. We then leverage the computational efficiency of this framework to explore the variability of epigenetic function across fifty histone modification data-sets from variety of cancerous and non-cancerous cell-lines.ConclusionsWe demonstrate that the genome-wide relationships between histone modifications and mRNA transcription are lineage, tissue and karyotype-invariant, and that models trained on matched -omics data from non-cancerous cell-lines are able to predict cancerous expression with equivalent genome-wide fidelity.

【 授权许可】

CC BY   
© The Author(s) 2016

【 图 表 】

12864_2017_4030_Article_IEq35.gif

12864_2015_2174_Article_IEq1.gif

12864_2017_4030_Article_IEq33.gif

12864_2017_4132_Article_IEq14.gif

12864_2017_4030_Article_IEq31.gif

12864_2016_3098_Article_IEq70.gif

12864_2017_4030_Article_IEq29.gif

12864_2017_4132_Article_IEq10.gif

12864_2016_2793_Article_IEq13.gif

12864_2017_4132_Article_IEq7.gif

12864_2017_4030_Article_IEq26.gif

12864_2017_4186_Article_IEq31.gif

12864_2016_2821_Article_IEq60.gif

12864_2017_4186_Article_IEq29.gif

12864_2015_1945_Article_IEq3.gif

12864_2017_4186_Article_IEq27.gif

12864_2017_4186_Article_IEq26.gif

12864_2017_4179_Article_IEq12.gif

12864_2015_2055_Article_IEq5.gif

12864_2017_4179_Article_IEq10.gif