PT - JOURNAL ARTICLE AU - Joe Paggi AU - Andrew Lamb AU - Kevin Tian AU - Irving Hsu AU - Pierre-Louis Cedoz AU - Prasad Kawthekar TI - Predicting Transcriptional Regulatory Activities with Deep Convolutional Networks AID - 10.1101/099879 DP - 2017 Jan 01 TA - bioRxiv PG - 099879 4099 - http://biorxiv.org/content/early/2017/01/12/099879.short 4100 - http://biorxiv.org/content/early/2017/01/12/099879.full 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.