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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE130205 | GEO | 2020/04/30

REPOSITORIES: GEO

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