REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data

Biclustering for Epi-Transcriptomic Co-functional Analysis

Dynamic and reversible N6-methyladenosine (m6A) modifications are associated with many essential cellular functions as well as physiological and pathological phenomena. In-depth study of m6A co-functional patterns in epi-transcriptomic data may help to understand its complex regulatory mechanisms. In this chapter, we describe several biclustering mining algorithms for epi-transcriptomic data to discover potential co-functional patterns. The concepts and computational methods discussed in this chapter will be particularly useful for researchers working in related fields. We also aim to introduce new deep learning techniques into the field of co-functional analysis of epi-transcriptomic data.

The role of RNA m6A methylation in lipid metabolism

The m6A methylation is the most numerous modification of mRNA in mammals, coordinated by RNA m6A methyltransferases, RNA m6A demethylases, and RNA m6A binding proteins. They change the RNA m6A methylation level in their specific manner. RNA m6A modification has a significant impact on lipid metabolic regulation. The "writer" METTL3/METTL14 and the "eraser" FTO can promote the accumulation of lipids in various cells by affecting the decomposition and synthesis of lipids. The "reader" YTHDF recognizes m6A methylation sites of RNA and regulates the target genes' translation. Due to this function that regulates lipid metabolism, RNA m6A methylation plays a pivotal role in metabolic diseases and makes it a great potential target for therapy.