Predicting effects of noncoding variants with deep learning–based sequence model


This week we will conclude our Deep Learning Theme with a loot at DeepSEA. Our meeting will be at noon Monday, September 28th in room 3160 of the Discovery building.

The title of the paper is Predicting effects of noncoding variants with deep learning–based sequence model, and it is available at http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3547.html. The abstract reads as follows:

Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning–based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.

We look forward to seeing all who can attend next Monday, and please feel free to start the discussion in the comments section below.

Sara and Debbie