Deciphering human non-coding DNA using machine learning approaches

author: Guillaume Bourque, McGill University and Génome Québec Innovation Centre
published: Feb. 17, 2015,   recorded: September 2014,   views: 1884
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Description

In this presentation we will present an overview of the functional genomics datasets and tools that have been made available by consortiums such as ENCODE, the NIH Roadmap and now the International Human Epigenome Consoritum (IHEC). These data have been generated in a collection of reference and disease cell-types and include information on protein-DNA interactions or on histone marks (ChIP-Seq), transcriptome (RNA-Seq), methylation (Mehyl-Seq) and open chromatin (DNase-Seq). We will explain how these data can used to interpret human non-coding DNA and help identify detrimental DNA variants or mutations. Finally, we will show how machine-approaches can be used to go beyond the simple annotation of non-coding DNA and to mine these functional genomics data even further.

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