Learning Tn5 Sequence Bias from ATAC-seq on Naked Chromatin
Ansari M, Fischer DS, Theis FJ (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12396 LNCS
Pages Range: 105-114
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Bratislava, SVK
ISBN: 9783030616083
DOI: 10.1007/978-3-030-61609-0_9
Abstract
Technological advances in the last decade resulted in an explosion of biological data. Sequencing methods in particular provide large-scale data sets as resource for incorporation of machine learning in the biological field. By measuring DNA accessibility for instance, enzymatic hypersensitivity assays facilitate identification of regions of open chromatin in the genome, marking potential locations of regulatory elements. ATAC-seq is the primary method of choice to determine these footprints. It allows measurements on the cellular level, complementing the recent progress in single cell transcriptomics. However, as the method-specific enzymes tend to bind preferentially to certain sequences, the accessibility profile is confounded by binding specificity. The inference of open chromatin should be adjusted for this bias[1]. To enable such corrections, we built a deep learning model that learns the sequence specificity of ATAC-seq’s enzyme Tn5 on naked DNA. We found binding preferences and demonstrate that cleavage patterns specific to Tn5 can successfully be discovered by the means of convolutional neural networks. Such models can be combined with accessibility analysis in the future in order to predict bias on new sequences and furthermore provide a better picture of the regulatory landscape of the genome.
Involved external institutions
Germany (DE)How to cite
APA:
Ansari, M., Fischer, D.S., & Theis, F.J. (2020). Learning Tn5 Sequence Bias from ATAC-seq on Naked Chromatin. In Igor Farkaš, Paolo Masulli, Stefan Wermter (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 105-114). Bratislava, SVK: Springer Science and Business Media Deutschland GmbH.
MLA:
Ansari, Meshal, David S. Fischer, and Fabian J. Theis. "Learning Tn5 Sequence Bias from ATAC-seq on Naked Chromatin." Proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, Bratislava, SVK Ed. Igor Farkaš, Paolo Masulli, Stefan Wermter, Springer Science and Business Media Deutschland GmbH, 2020. 105-114.
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