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Kim, Donghyuk
Systems Biology and Machine Learning Lab.
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dc.citation.number 2 -
dc.citation.startPage bbad024 -
dc.citation.volume 24 - Bang, Ina - Lee, Sang-Mok - Park, Seojoung - Park, Joon Young - Nong, Linh Khanh - Gao, Ye - Palsson, Bernhard O - Kim, Donghyuk - 2023-12-21T12:48:51Z - 2023-12-21T12:48:51Z - 2023-03-06 - 2023-03 -
dc.description.abstract Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users’ specific needs. -
dc.identifier.bibliographicCitation BRIEFINGS IN BIOINFORMATICS, v.24, no.2, pp.bbad024 -
dc.identifier.doi 10.1093/bib/bbad024 -
dc.identifier.issn 1467-5463 -
dc.identifier.scopusid 2-s2.0-85150665777 -
dc.identifier.uri -
dc.identifier.wosid 001042120200033 -
dc.language 영어 -
dc.publisher Oxford University Press -
dc.title Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Biochemical Research Methods;Mathematical & Computational Biology -
dc.relation.journalResearchArea Biochemistry & Molecular Biology;Mathematical & Computational Biology -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor peak calling -
dc.subject.keywordAuthor ChIP-exo -
dc.subject.keywordAuthor deep-learning -
dc.subject.keywordPlus TRANSCRIPTION FACTORS -
dc.subject.keywordPlus DNA -
dc.subject.keywordPlus BINDING -


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