Distributed gene expression modelling for exploring variability in epigenetic function
AuthorBudden, DM; Crampin, EJ
Source TitleBMC Bioinformatics
AffiliationSchool of Mathematics and Statistics
Document TypeJournal Article
CitationsBudden, D. M. & Crampin, E. J. (2016). Distributed gene expression modelling for exploring variability in epigenetic function. BMC BIOINFORMATICS, 17 (1), https://doi.org/10.1186/s12859-016-1313-1.
Access StatusOpen Access
BACKGROUND: Predictive 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. RESULTS: We 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. CONCLUSIONS: We 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.
- Click on "Export Reference in RIS Format" and choose "open with... Endnote".
- Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References