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Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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Distributed gene expression modelling for exploring variability in epigenetic function.

David M Budden1,2, Edmund J Crampin3,4,5,6

  • 1Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, 02139, USA. budden@csail.mit.edu.

BMC Bioinformatics
|November 7, 2016
PubMed
Summary
This summary is machine-generated.

We developed a scalable gene expression model using distributed computing, improving performance with more processors. This model reveals consistent epigenetic relationships across diverse cell types, enabling accurate cancer expression prediction.

Keywords:
EpigeneticsGene expressionHistone modificationsMapReduce

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Area of Science:

  • Computational Biology
  • Genomics
  • Epigenetics

Background:

  • High-throughput sequencing generates vast biological data, necessitating advanced computational tools.
  • Previous gene expression modeling was limited by inability to process large, sharded datasets on distributed architectures.

Purpose of the Study:

  • To develop a distributed gene expression modeling framework for large datasets.
  • To investigate epigenetic variability across diverse cell lines using computational efficiency.

Main Methods:

  • Implemented a distributed gene expression modeling approach using the MapReduce paradigm.
  • Leveraged computational efficiency for analysis of fifty histone modification datasets.

Main Results:

  • Demonstrated linear performance improvement with increased processor cores in the distributed model.
  • Explored epigenetic function variability across cancerous and non-cancerous cell lines.

Conclusions:

  • Genome-wide relationships between histone modifications and mRNA transcription are invariant across lineage, tissue, and karyotype.
  • Models trained on non-cancerous data accurately predict cancerous gene expression with high fidelity.