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Brief. Bioinform., 2023 Mar 19;24(2):bbad024 | https://doi.org/10.1093/bib/bbad024 Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling

Ina Bang ^1†, Sang-Mok Lee ^1†, Seojoung Park^1†, Joon Young Park ¹, Linh Khanh Nong ¹, Ye Gao ², Bernhard O Palsson ^2,3,4, Donghyuk Kim ¹

¹School of Energy and Chemical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
²Department of Bioengineering, University of California San Diego, La Jolla CA 92093, USA.
³Department of Pediatrics, University of California San Diego, La Jolla CA 92093, USA.
⁴Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark.

^†Ina Bang, Sang-Mok Lee and Seojoung Park contributed equally to this work.
Corresponding author: Donghyuk Kim

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.

논문정보

형식Research article
게재일2023년 03월 (BRIC 등록일 2023-04-06)
연구진국내(교신)+국외 연구진
분야 생명과학 > 미생물학

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