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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\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/cgs.csail.mit.edu\/deepaccess-package\/\" xlink:type=\"simple\">https:\/\/cgs.csail.mit.edu\/deepaccess-package\/<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009282","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T14:50:55Z","timestamp":1628520655000},"page":"e1009282","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":13,"title":["Discovering differential genome sequence activity with interpretable and efficient deep learning"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1008-2666","authenticated-orcid":true,"given":"Jennifer","family":"Hammelman","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1709-4034","authenticated-orcid":true,"given":"David K.","family":"Gifford","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"key":"pcbi.1009282.ref001","doi-asserted-by":"crossref","first-page":"i121","DOI":"10.1093\/bioinformatics\/btw255","article-title":"Convolutional neural network architectures for predicting DNA-protein binding","volume":"32","author":"H Zeng","year":"2016","journal-title":"Bioinformatics"},{"key":"pcbi.1009282.ref002","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"B Alipanahi","year":"2015","journal-title":"Nat Biotechnol"},{"key":"pcbi.1009282.ref003","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1101\/gr.200535.115","article-title":"Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks","volume":"26","author":"DR Kelley","year":"2016","journal-title":"Genome Res"},{"key":"pcbi.1009282.ref004","doi-asserted-by":"crossref","first-page":"e107","DOI":"10.1093\/nar\/gkw226","article-title":"DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences","volume":"44","author":"D Quang","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1009282.ref005","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1038\/nmeth.3547","article-title":"Predicting effects of noncoding variants with deep learning-based sequence model","volume":"12","author":"J Zhou","year":"2015","journal-title":"Nat Methods"},{"key":"pcbi.1009282.ref006","doi-asserted-by":"crossref","DOI":"10.1101\/gr.263228.120","article-title":"Identification of determinants of differential chromatin accessibility through a massively parallel genome-integrated reporter assay","volume":"30","author":"J Hammelman","year":"2020","journal-title":"Genome Res"},{"key":"pcbi.1009282.ref007","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/S0896-6273(01)80026-X","article-title":"Requirement for the Homeobox Gene Hb9 in the Consolidation of Motor Neuron Identity","volume":"23","author":"S Arber","year":"1999","journal-title":"Neuron"},{"key":"pcbi.1009282.ref008","article-title":"Learning Important Features Through Propagating Activation Differences","author":"A Shrikumar","year":"2017","journal-title":"arxiv"},{"key":"pcbi.1009282.ref009","article-title":"Technical Note on Transcription Factor Motif Discovery from Importance Scores (TF-MoDISco) version 0.5. 1.1","author":"A Shrikumar","year":"2018","journal-title":"arXiv Prepr arXiv181100416"},{"key":"pcbi.1009282.ref010","doi-asserted-by":"crossref","first-page":"e27","DOI":"10.1093\/nar\/gkaa009","article-title":"Uncovering tissue-specific binding features from differential deep learning","volume":"48","author":"M Phuycharoen","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1009282.ref011","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1186\/s13059-020-02055-7","article-title":"Aicher J, R. 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