Discovering differential genome sequence activity with interpretable and efficient deep learning
Name
journal.pcbi.1009282.pdf
Description
Published version
Size
2.33 MB
Format
Adobe PDF
Checksum (MD5)
2276c27d1cc1902ceea9aebedeae768a
Author(s)
Hammelman, Jennifer
Gifford, David K
Date Issued
August 2021
Journal
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Version
Final published version
Abstract
Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail.mit.edu/deepaccess-package/.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1009282
