Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling
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- Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling
- Bang, Ina; Lee, Sang-Mok; Park, Seojoung; Park, Joon Young; Nong, Linh Khanh; Gao, Ye; Palsson, Bernhard O; Kim, Donghyuk
- Issue Date
- Oxford University Press
- BRIEFINGS IN BIOINFORMATICS, v.24, no.2, pp.bbad024
- 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.
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