RT Journal Article
SR Electronic
T1 Predicting Transcriptional Regulatory Activities with Deep Convolutional Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 099879
DO 10.1101/099879
A1 Paggi, Joe
A1 Lamb, Andrew
A1 Tian, Kevin
A1 Hsu, Irving
A1 Cedoz, Pierre-Louis
A1 Kawthekar, Prasad
YR 2017
UL http://biorxiv.org/content/early/2017/01/12/099879.abstract
AB Massively parallel reporter assays (MPRAs) are a method to probe the effects of short sequences on transcriptional regulation activity. In a MPRA, short sequences are extracted from suspected regulatory regions, inserted into reporter plasmids, transfected into cell-types of interest, and the transcriptional activity of each reporter is assayed. Recently, Ernst et al. presented MPRA data covering 15750 putative regulatory regions. We trained a multitask convolutional neural network architecture using these sequence expression readouts which predicts as output the expression level outputs across four combinations of cell types and promoters. The model allows for the assigning of importance scores to each base through in silico mutagenesis, and the resulting importance scores correlated well with regions enriched for conservation and transcription factor binding.