TY - GEN
T1 - Learning Tn5 Sequence Bias from ATAC-seq on Naked Chromatin
AU - Ansari, Meshal
AU - Fischer, David S.
AU - Theis, Fabian J.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Convolutional neural networks
KW - Deep learning
KW - Regulatory element discovery
KW - Sequence preference bias
KW - Single-cell ATAC-seq
UR - https://www.scopus.com/pages/publications/85096575413
U2 - 10.1007/978-3-030-61609-0_9
DO - 10.1007/978-3-030-61609-0_9
M3 - Conference contribution
AN - SCOPUS:85096575413
SN - 9783030616083
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 105
EP - 114
BT - Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings
A2 - Farkaš, Igor
A2 - Masulli, Paolo
A2 - Wermter, Stefan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on Artificial Neural Networks, ICANN 2020
Y2 - 15 September 2020 through 18 September 2020
ER -