bioRxivBioinformaticsPreprint
Revisiting Evidence for Epigenetic Control of Alternative Splicing
Abstract
Alternative splicing is crucial for increasing eukaryotic cell transcriptome and proteome diversity. Changes in alternative splicing play a key role in cell differentiation and tissue development, and aberrations in this process have been associated with diseases. Despite its importance, the exact mechanisms for regulating alternative splicing are poorly understood. Several epigenetic marks, such as histone modification H3K36me3, have previously been associated with changes in alternative splicing. Here, we leverage the EpiATLAS data set to systematically re-evaluate evidence for epigenetic control of alternative splicing. We used SUPPA2 to calculate percentage spliced-in (PSI) values for skipped exons and retained introns and integrated this information with histone ChIP-seq and DNA methylation data. In addition to genome-wide association analysis with partial correlation and machine learning, we perform locus-specific PSI modeling on individual alternative splicing events. The latter represents a new contribution enabled by the unprecedented number of uniformly processed datasets in EpiATLAS. Our results confirm previously reported global associations of DNAm and H3K36me3 for exon inclusion and emphasize the importance of intrinsic features for genome-wide associations. On an event-specific level, trying to identify co-transcriptionally spliced events with epigenetic influence, we show that overall gene expression biases locus-specific analyses. Further, we show that epigenetic signal can predict PSI value in cis and trans, indicating sample-specific epigenetic fingerprints that distort across-sample analyses. Specifically, generalized linear models select epigenetic features predictive for PSI values not only in their genomic vicinity but also on different chromosomes. Our study demonstrates that epigenetic marks are associated with the alternative inclusion of exons and introns. Without a mechanistic explanation for these associations, our work emphasizes the need for more detailed research into the relationship between epigenetic changes and transcriptome diversity.
- organism: human
- assay: ChIP-seq
- assay: DNA methylation
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 2
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Specific histone modifications associate with alternative exon selection during mammalian development
Qiwen Hu, Casey S. Greene, Elizabeth A. Heller·Nucleic Acids ResearchPeer-reviewed·Apr 2, 2020
- Alternative splicing links histone modifications to stem cell fate decision
Yungang Xu, Weiling Zhao, … Xiaobo Zhou·Genome biologyPeer-reviewed·Sep 14, 2018
- Epigenome-based Splicing Prediction using a Recurrent Neural Network
Donghoon Lee, Jing Zhang, … Mark Gerstein·PLoS Computational BiologyPeer-reviewed·Jun 25, 2020
- Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision
Yungang Xu, Yongcui Wang, … Xiaobo Zhou·Nucleic Acids ResearchPeer-reviewed·Sep 15, 2017
- Histone marks are drivers of the splicing changes necessary for an epithelial-to-mesenchymal transition
Alexandre Segelle, Yaiza Núñez‐Álvarez, … Reini F. Luco·bioRxiv·May 4, 2021
- Stationary Analysis the Alternative Splicing Profile Reveals the Splicing Code
Meng Li·bioRxiv·Apr 23, 2017