REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data
Lin Zhang1,2, Shutao Chen1,2, Jiani Ma1,2
1Engineering Research Center of Intelligent Control for Underground Space, China University of Mining and Technology, Ministry of Education, Xuzhou, China.
Frontiers in Genetics
|June 14, 2021
Summary
This study introduces REW-ISA V2, a new algorithm for analyzing epitranscriptome data. It identifies N6-methyladenosine (m6A) co-methylation patterns, revealing biological insights into gene regulation and signal pathways.
Area of Science:
- Molecular Biology
- Bioinformatics
- Epigenetics
Background:
- N6-methyladenosine (m6A) modifications regulate numerous biological processes.
- The systematic regulatory mechanisms of m6A sites remain unclear.
- Epitranscriptome data offers insights into m6A regulation.
Purpose of the Study:
- To develop a novel algorithm for mining RNA co-methylation patterns.
- To elucidate the specific regulatory mechanisms of m6A at a systematic level.
- To leverage homologous information within epitranscriptome data.
Main Methods:
- Developed the REW-ISA V2 algorithm, an enhancement of REW-ISA.
- Fused homologous information (gene-m6A site and cell line-condition relationships).
- Applied REW-ISA V2 to MERIP-seq data.
Main Results:
- Identified fifteen potential local function blocks (LFBs) of co-methylated m6A sites.
- LFBs exhibited greater biological significance compared to other biclustering algorithms.
- Discovered correlations between LFBs, signal pathways, and m6A methyltransferases.
Conclusions:
- REW-ISA V2 effectively mines co-methylation patterns from epitranscriptome data.
- The algorithm integrates homologous information for enhanced analysis.
- Identified co-methylation patterns provide insights into specific biological conditions.
