Chromatin loop anchors can predict transcript and exon usage

Abstract

Epigenomics and transcriptomics data from high-throughput sequencing techniques such as RNA-seq and ChIP-seq have been successfully applied in predicting gene transcript expression. However, ChIA-PET has never been used. Here, we developed machine learning models to investigate if ChIA-PET could contribute to the transcript and exon usage prediction. In doing so, we used a large set of transcription factors as well as ChIA-PET data, which indicates locations of chromatin loops in the genome. We developed different Gradient Boosting Trees (GB) models according to the different tasks on the integrated dataset from three cell lines, including GM12878, HeLaS3 and K562. We validated the models via 10-fold cross validation, chromosome-split validation and cross-cell validation. Our results show that both transcript and splicing-derived exon usage can be effectively predicted with at least 0.7512 and 0.7459 of accuracy, respectively, on all cell lines from all kinds of validations. Examining the predictive features, we found that RNA Polymerase II ChIA-PET was one of the most important features in both transcript and exon usage prediction, suggesting that chromatin loop anchors are predictive of both transcript and exon usage.