ATAC-seq signal processing and recurrent neural networks can identify RNA polymerase activity

Nascent transcription assays are the current gold standard for identifying regions of active transcription, including markers for functional transcription factor (TF) binding. Here we present a signal processing-based model to determine regions of active transcription genome-wide using the simpler assay for transposase-accessible chromatin, followed by high-throughput sequencing (ATAC-seq). The focus of this study is twofold: First, we perform a frequency space analysis of the "signal" generated from ATAC-seq experiments short reads, at a single-nucleotide resolution, using a discrete wavelet transform. Second, we explore different uses of neural networks to combine this signal with its underlying genome sequence in order to classify ATAC-seq peaks on the presence or absence of bidirectional transcription. We analyze the performance of different data encoding schemes and machine learning architectures, and show how a hybrid signal/sequence representation classified using recurrent neural networks (RNNs) yields the best performance across different cell types. Contactrobin.dowell@colorado.edu