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Various localized epigenetic marks predict expression across 54 samples and reveal underlying chromatin state enrichments

Lalita Devadas, Angela Yen, Manolis Kellis
doi: https://doi.org/10.1101/030478
Lalita Devadas
1Lexington High School, Lexington, Massachusetts 02421, USA
2Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
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Angela Yen
2Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
3MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts 02139, USA
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Manolis Kellis
2Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
3MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts 02139, USA
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Abstract

Here, we predict gene expression from epigenetic features based on public data available through the Epigenome Roadmap Project [1]. This rich new dataset includes samples from primary tissues, which to our knowledge have not previously been studied in this context. Specifically, we used computational machine learning algorithms on five histone modifications to predict gene expression in a variety of samples. Our models reveal a high predictive accuracy, especially in cell cultures, with predictive ability dependent on sample type and anatomy. The relative importance of each histone mark feature varied across samples. We localized each histone mark signal to its relevant region, revealing that chromatin state enrichment varies greatly between histone marks. Our results provide several novel insights into epigenetic regulation of transcription in new contexts.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 03, 2015.
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Various localized epigenetic marks predict expression across 54 samples and reveal underlying chromatin state enrichments
Lalita Devadas, Angela Yen, Manolis Kellis
bioRxiv 030478; doi: https://doi.org/10.1101/030478
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Various localized epigenetic marks predict expression across 54 samples and reveal underlying chromatin state enrichments
Lalita Devadas, Angela Yen, Manolis Kellis
bioRxiv 030478; doi: https://doi.org/10.1101/030478

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