Prediction of chromatin looping using deep hybrid learning (DHL)

Background: With the development of rapid and cheap sequencing techniques, the cost of whole-genome sequencing (WGS) has dropped significantly. However, the complexity of the human genome is not limited to the pure sequence—and additional experiments are required to learn the human genome’s influence on complex traits. One of the most exciting aspects for scientists nowadays is the spatial organisation of the genome, which can be discovered using spatial experiments ( e.g. , Hi-C, ChIA-PET). The information about the spatial contacts helps in the analysis and brings new insights into our understanding of the disease developments. Methods: We have used an ensemble of deep learning with classical machine learning algorithms. The deep learning network we used was DNABERT, which utilises the BERT language model (based on transformers) for the genomic function. The classical machine learning models included support vector machines (SVMs), random forests (RFs), and K-nearest neighbor (KNN). The whole approach was wrapped together as deep hybrid learning (DHL). Results: We found that the DNABERT can be used to predict the ChIA-PET experiments with high precision. Additionally, the DHL approach has increased the metrics on CTCF and RNAPII sets. Conclusions: DHL approach should be taken into consideration for the models utilising the power of deep learning. While straightforward in the concept, it can improve the results significantly.