Discovering differential genome sequence activity with interpretable and efficient deep learning.

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 interpr...

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Autores principales:	Jennifer Hammelman, David K Gifford
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f68a75bb216e4deea942b65eba7350a7
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spelling	oai:doaj.org-article:f68a75bb216e4deea942b65eba7350a72021-12-02T19:58:07ZDiscovering differential genome sequence activity with interpretable and efficient deep learning.1553-734X1553-735810.1371/journal.pcbi.1009282https://doaj.org/article/f68a75bb216e4deea942b65eba7350a72021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009282https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Discovering 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/.Jennifer HammelmanDavid K GiffordPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009282 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jennifer Hammelman David K Gifford Discovering differential genome sequence activity with interpretable and efficient deep learning.
description	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/.
format	article
author	Jennifer Hammelman David K Gifford
author_facet	Jennifer Hammelman David K Gifford
author_sort	Jennifer Hammelman
title	Discovering differential genome sequence activity with interpretable and efficient deep learning.
title_short	Discovering differential genome sequence activity with interpretable and efficient deep learning.
title_full	Discovering differential genome sequence activity with interpretable and efficient deep learning.
title_fullStr	Discovering differential genome sequence activity with interpretable and efficient deep learning.
title_full_unstemmed	Discovering differential genome sequence activity with interpretable and efficient deep learning.
title_sort	discovering differential genome sequence activity with interpretable and efficient deep learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f68a75bb216e4deea942b65eba7350a7
work_keys_str_mv	AT jenniferhammelman discoveringdifferentialgenomesequenceactivitywithinterpretableandefficientdeeplearning AT davidkgifford discoveringdifferentialgenomesequenceactivitywithinterpretableandefficientdeeplearning
_version_	1718375816119713792

Discovering differential genome sequence activity with interpretable and efficient deep learning.

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