A hybrid signal/sequence model to detect RNA polymerase activity in ATAC-seq

We explore different uses of machine learning classifiers, including neural networks, to combine the “signal” from ATAC-seq with its underlying genome sequence in order to classify ATAC-seq peaks on the presence or absence of transcription. We show how a hybrid signal/sequence representation, classified using recurrent neural networks (RNNs), yields the best performance across different cell types. Overall design: HCT116 treated with 10 ul DMSO (control) or 10 µM Nutlin-3A (inhibitor of p53) in DMSO [Sigma SML0580]