DEOCSU(DEep-learning Optimized ChIP-exo peak calling SUite) is an deep-learning based ChIP-exo peak calling tool based on deep convolutional neural network model. The tool is named after the lab mascot pet DEOCSU[dəksu:] whose main staff is Seojoung Park in SBML-Kim lab.
Chromatin immunoprecipitation (ChIP) has been widely used to investigate DNA-binding proteins (e.g. transcription factors (TF) or transcriptional machinery) and their binding location at the genome-scale level. Although ChIP-exo increases the signal-to-noise ratio and allows researchers to identify high-resolution binding sites, a peak calling step for selecting bona fide peaks is time-consuming, and labor-intensive which is a major rate-limiting step of ChIP-exo data analysis.
Our newly developed DEOCSU has following characteristics
-
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%.
-
DEOCSU can be executed on a cloud computing platform or the local environment.
-
With visualization software(https://github.com/SBML-Kimlab/MetaScope), 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.
If you have any questions, please feel free to contact the author via email. https://sites.google.com/view/systemskimlab