Abstract
Alternative splicing (AS) is frequent during early mouse embryonic development. Specific histone post-translational modifications (hPTMs) have been shown to regulate exon splicing by either directly recruiting splice machinery or indirectly modulating transcriptional elongation. In this study, we hypothesized that hPTMs regulate expression of alternatively spliced genes for specific processes during differentiation. To address this notion, we applied an innovative machine learning approach to relate global hPTM enrichment to AS regulation during mammalian tissue development. We found that specific hPTMs, H3K36me3 and H3K4me1, play a role in skipped exon selection among all the tissues and developmental time points examined. In addition, we used iterative random forest model and found that interactions of multiple hPTMs most strongly predicted splicing when they included H3K36me3 and H3K4me1. Collectively, our data demonstrated a link between hPTMs and alternative splicing which will drive further experimental studies on the functional relevance of these modifications to alternative splicing.