Deep learning: new computational modelling techniques for genomics

doi:10.1038/s41576-019-0122-6

Review

. 2019 Jul;20(7):389-403.

doi: 10.1038/s41576-019-0122-6.

Deep learning: new computational modelling techniques for genomics

Gökcen Eraslan^#^{1

2}, Žiga Avsec^#³, Julien Gagneur⁴, Fabian J Theis^{5

6

7}

Affiliations

¹ Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
² School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
³ Department of Informatics, Technical University of Munich, Garching, Germany.
⁴ Department of Informatics, Technical University of Munich, Garching, Germany. gagneur@in.tum.de.
⁵ Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany. fabian.theis@helmholtz-muenchen.de.
⁶ School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany. fabian.theis@helmholtz-muenchen.de.
⁷ Department of Mathematics, Technical University of Munich, Garching, Germany. fabian.theis@helmholtz-muenchen.de.

^# Contributed equally.

PMID: 30971806
DOI: 10.1038/s41576-019-0122-6

Review

Deep learning: new computational modelling techniques for genomics

Gökcen Eraslan et al. Nat Rev Genet. 2019 Jul.

. 2019 Jul;20(7):389-403.

doi: 10.1038/s41576-019-0122-6.

Authors

Gökcen Eraslan^#^{1

2}, Žiga Avsec^#³, Julien Gagneur⁴, Fabian J Theis^{5

6

7}

Affiliations

¹ Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
² School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
³ Department of Informatics, Technical University of Munich, Garching, Germany.
⁴ Department of Informatics, Technical University of Munich, Garching, Germany. gagneur@in.tum.de.
⁵ Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany. fabian.theis@helmholtz-muenchen.de.
⁶ School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany. fabian.theis@helmholtz-muenchen.de.
⁷ Department of Mathematics, Technical University of Munich, Garching, Germany. fabian.theis@helmholtz-muenchen.de.

^# Contributed equally.

PMID: 30971806
DOI: 10.1038/s41576-019-0122-6

Abstract

As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.

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References

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1. Brown, P. O. & Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21, 33–37 (1999).
1. Ozaki, K. et al. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat. Genet. 32, 650–654 (2002).
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1. Oliver, S. Guilt-by-association goes global. Nature 403, 601–603 (2000).

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[1] Hieter, P. & Boguski, M. Functional genomics: it’s all how you read it. Science 278, 601–602 (1997).

[2] Hieter, P. & Boguski, M. Functional genomics: it’s all how you read it. Science 278, 601–602 (1997).

[3] Brown, P. O. & Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21, 33–37 (1999).

[4] Brown, P. O. & Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21, 33–37 (1999).

[5] Ozaki, K. et al. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat. Genet. 32, 650–654 (2002).

[6] Ozaki, K. et al. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat. Genet. 32, 650–654 (2002).

[7] Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

[8] Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

[9] Oliver, S. Guilt-by-association goes global. Nature 403, 601–603 (2000).

[10] Oliver, S. Guilt-by-association goes global. Nature 403, 601–603 (2000).

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Deep learning: new computational modelling techniques for genomics

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