Learning Tn5 Sequence Bias from ATAC-seq on Naked Chromatin

Meshal Ansari, David S. Fischer, Fabian J. Theis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030616083
StatePublished - 2020
Event29th International Conference on Artificial Neural Networks, ICANN 2020 - Bratislava, Slovakia
Duration: 15 Sep 202018 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12396 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on Artificial Neural Networks, ICANN 2020


  • Convolutional neural networks
  • Deep learning
  • Regulatory element discovery
  • Sequence preference bias
  • Single-cell ATAC-seq


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