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DC Field | Value | Language |
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dc.citation.number | 2 | - |
dc.citation.startPage | bbad024 | - |
dc.citation.title | BRIEFINGS IN BIOINFORMATICS | - |
dc.citation.volume | 24 | - |
dc.contributor.author | Bang, Ina | - |
dc.contributor.author | Lee, Sang-Mok | - |
dc.contributor.author | Park, Seojoung | - |
dc.contributor.author | Park, Joon Young | - |
dc.contributor.author | Nong, Linh Khanh | - |
dc.contributor.author | Gao, Ye | - |
dc.contributor.author | Palsson, Bernhard O | - |
dc.contributor.author | Kim, Donghyuk | - |
dc.date.accessioned | 2023-12-21T12:48:51Z | - |
dc.date.available | 2023-12-21T12:48:51Z | - |
dc.date.created | 2023-03-06 | - |
dc.date.issued | 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 | https://scholarworks.unist.ac.kr/handle/201301/62174 | - |
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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