m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

Song-Yao Zhang, Shao-Wu Zhang, Lian Liu, Jia Meng and Yufei Huang

PLOS Computational Biology, 2016, vol. 12, issue 12, 1-31

Abstract: As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine (m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.Author Summary: Powered by methylated RNA immunoprecipitation sequencing (MeRIP-Seq) technology, recent studies have revealed a new mode of post transcriptional regulation mediated by mRNA N6-methyladenosine (m6A). Currently, the analysis of m6A focuses mostly on prediction of m6A sites as well as differential m6A methylation, and systematic approach for predicting m6A functions is yet to emerge. We develop here m6A-Driver, the first network-based approach, to identify m6A-driven genes and their associated networks, whose functional interactions are likely to be actively modulated by m6A methylation under a specific condition. Our test results showed that m6A-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. m6A-Driver is an effective and reliable approach to identify functionally relevant m6A-driven genes and networks from MeRIP-Seq data.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005287 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 05287&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005287

DOI: 10.1371/journal.pcbi.1005287

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().