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- Title
Distributed gene expression modelling for exploring variability in epigenetic function.
- Authors
Budden, David M.; Crampin, Edmund J.
- Abstract
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.
- Subjects
GENE expression; EPIGENETICS; CANCER cell variation; CELL lines; HISTONE genetics
- Publication
BMC Bioinformatics, 2016, Vol 17, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/s12859-016-1313-1