ST-ChIP: Accurate prediction of spatiotemporal ChIP-seq data with recurrent neural networks

Abstract

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a powerful method for locating protein-DNA binding sites. Spatiotemporal ChIP-seq data greatly contribute to the studies of dynamic biological processes as they contain information from both spatial and temporal dimensions. However, we can hardly find a computational method for forecasting spatiotemporal ChIP-seq data in the literature. Here we present ST-ChIP, a supervised method using Long Short-Term Memory (LSTM) for predicting coverage or peaks of spatiotemporal ChIP-seq data. We benchmarked three recurrent neural networks and found that two of them achieved higher predictive performances on recovering coverage or peaks of the forecasting time steps. Our results demonstrate that enhancer regions are enriched with our predicted H3K4me1 coverage, and promoter regions are enriched with our predicted H3K4me3 peaks, which match the findings from other studies. In total, ST-ChIP is an effective method for accurately predicting spatiotemporal ChIP-seq data. ST-ChIP is publicly available at http://dna.cs.miami.edu/ST-ChIP/.