seqgra: principled selection of neural network architectures for genomics prediction tasks
Name
btac101.pdf
Description
Published version
Size
1.9 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Krismer, Konstantin
Hammelman, Jennifer
Gifford, David K
Date Issued
April 28, 2022
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Krismer, Konstantin, Hammelman, Jennifer and Gifford, David K. 2022. "seqgra: principled selection of neural network architectures for genomics prediction tasks." Bioinformatics, 38 (9).
Version
Final published version
Abstract
Abstract
Motivation: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high
accuracy, their contributions to a mechanistic understanding of the biology of regulatory elements is often hindered
by the complexity of the predictive model and thus poor interpretability of its decision boundaries. To address this, we
introduce seqgra, a deep learning pipeline that incorporates the rule-based simulation of biological sequence data and
the training and evaluation of models, whose decision boundaries mirror the rules from the simulation process.
Results: We show that seqgra can be used to (i) generate data under the assumption of a hypothesized model of
genome regulation, (ii) identify neural network architectures capable of recovering the rules of said model and (iii)
analyze a model’s predictive performance as a function of training set size and the complexity of the rules behind
the simulated data.
Availability and implementation: The source code of the seqgra package is hosted on GitHub (https://github.com/gif
ford-lab/seqgra). seqgra is a pip-installable Python package. Extensive documentation can be found at https://
kkrismer.github.io/seqgra.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/btac101
