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Distributed gene expression modelling for exploring variability in epigenetic function.
- Source :
-
BMC Bioinformatics . 11/5/2016, Vol. 17, p1-8. 8p. 3 Color Photographs, 2 Charts. - Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *GENE expression
*EPIGENETICS
*CANCER cell variation
*CELL lines
*HISTONE genetics
Subjects
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 17
- Database :
- Academic Search Index
- Journal :
- BMC Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 119357230
- Full Text :
- https://doi.org/10.1186/s12859-016-1313-1