MCI-frcnn: A deep learning method for topological micro-domain boundary detection - Tian, Simon Zhongyuan; Yin, Pengfei; Jing, Kai; Yang, Yang; Xu, Yewen; Huang, Guangyu; Ning, Duo; Fullwood, Melissa J.; Zheng, Meizhen (Frontiers in Cell and Developmental Biology, 2022)
Chromatin structural domains, or Topologically Associated Domains (TADs), are a general organizing principle in chromatin biology. RNA polymerase II (RNAPII) mediated multiple chromatin interactive loops, tethering together as RNAPII associated chromatin interaction domains (RAIDs), to offer framework for gene regulation. RAID and TAD alterations have been found to be associated with diseases. They can be further dissected as micro-domains (micro-TADs and micro-RAIDs) by clustering single molecule chromatin interactive complexes from next-generation three-dimensional (3D) genome techniques, such as ChIA-Drop. Currently, there are few tools for micro-domain boundary identification. In this work, we developed the “MCI-frcnn” deep learning method to train a Faster Region-based Convolutional Neural Network (Faster R-CNN) for micro-domain boundary detection. At the training phase in MCI-frcnn, 50 images of RAIDs from Drosophila RNAPII ChIA-Drop data, containing 261 micro-RAIDs with ground truth boundaries, were trained for 7 days. Using this well-trained MCI-frcnn, we detected micro-RAIDs boundaries for the input new images, with a fast speed (5.26 fps), high recognization accuracy (AUROC = 0.85, mAP = 0.69) and high boundary region quantification (genomic IoU = 76%). We further applied MCI-frcnn to detect human micro-TADs boundaries using human GM12878 SPRITE data, and obtained a high region quantification score (mean gIoU = 85%). In all, the MCI-frcnn deep learning method which we developed in this work is a general tool for micro-domains boundaries detection regardless the data types or species.