Deciphering human non-coding DNA using machine learning approaches
published: Feb. 17, 2015, recorded: September 2014, views: 1887
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Download slides: mlpmsummerschool2014_bourque_human_non_coding_DNA.pdf (6.6 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !
Write your own review or comment: