REW-ISA: unveiling local functional blocks in epi-transcriptome profiling data via an RNA expression-weighted iterative signature algorithm

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.

FBCwPlaid: A Functional Biclustering Analysis of Epi-Transcriptome Profiling Data Via a Weighted Plaid Model

Recent studies have shown that in-depth studies on epi-transcriptomic patterns of N6-methyladenosine (m⁶A) may help understand its complex functions and co-regulatory mechanisms. Since most biclustering algorithms are developed in scenarios of gene expression analysis, which does not share the same characteristics with m⁶A methylation profile, we propose a weighted Plaid biclustering model (FBCwPlaid) based on the Lagrange multiplier method to discover the potential functional patterns. Each pattern is achieved by minimizing approximation error between FBCwPlaid predicted value and real data. To address the issue that site expression level determines methylation level confidence, it uses RNA expression levels of each site as weights to make lower expressed sites less confident. FBCwPlaid also allows overlapping biclusters, indicating some sites may participate in multiple biological functions. FBCwPlaid was then applied on MeRIP-Seq data of 69,446 methylation sites under 32 experimental conditions, each of which represented a stimulus to a particular cell line or environment. Finally, three patterns were discovered, and further pathway analysis and enzyme specificity test showed that sites involved in each pattern are highly relevant to m⁶A methyltransferases. Further detailed analyses showed that some patterns are condition-specific, indicating that some specific sites' methylation profiles may occur in specific cell lines or conditions.

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

Background: Previous studies have shown that N6-methyladenosine (m⁶A) is related to many life processes and physiological and pathological phenomena. However, the specific regulatory mechanism of m⁶A sites at the systematic level is not clear. Therefore, mining the RNA co-methylation patterns in the epi-transcriptome data is expected to explain the specific regulation mechanism of m⁶A.

Methods: Considering that the epi-transcriptome data contains homologous information (the genes corresponding to the m⁶A sites and the cell lines corresponding to the experimental conditions), rational use of this information will help reveal the regulatory mechanism of m⁶A. Therefore, based on the RNA expression weighted iterative signature algorithm (REW-ISA), we have fused homologous information and developed the REW-ISA V2 algorithm.

Results: Then, REW-ISA V2 was applied in the MERIP-seq data to find potential local function blocks (LFBs), where sites are hyper-methylated simultaneously across the specific conditions. Finally, REW-ISA V2 obtained fifteen LFBs. Compared with the most advanced biclustering algorithm, the LFBs obtained by REW-ISA V2 have more significant biological significance. Further biological analysis showed that these LFBs were highly correlated with some signal pathways and m⁶A methyltransferase.

Conclusion: REW-ISA V2 fuses homologous information to mine co-methylation patterns in the epi-transcriptome data, in which sites are co-methylated under specific conditions.

Recent advances in functional annotation and prediction of the epitranscriptome

RNA modifications, in particular N⁶-methyladenosine (m⁶A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.

BDBB: A Novel Beta-distribution-based Biclustering Algorithm for Revealing Local Co-methylation Patterns in Epi-transcriptome Profiling Data

N6-methyladenosine (m⁶A) has been shown to play crucial roles in RNA metabolism, physiology, and pathological processes. However, the specific regulatory mechanisms of most methylation sites remain uncharted due to the complexity of life processes. Biological experimental methods are costly to solve this problem, and computational methods are relatively lacking. The discovery of local co-methylation patterns (LCPs) of m6A epi-transcriptome data can benefit to solve the above problems. Based on this, we propose a novel biclustering algorithm based on the beta distribution (BDBB), which realizes the mining of LCPs of m⁶A epi-transcriptome data. BDBB employs the Gibbs sampling method to complete parameter estimation. In the process of modeling, LCPs are recognized as sharp beta distributions compared to the background distribution. Simulation study showed BDBB can extract all the three actual LCPs implanted in the background data and the overlap conditions between them with considerable accuracy (almost close to 100%). On MeRIP-Seq data of 69,446 methylation sites under 32 experimental conditions from 10 human cell lines, BDBB unveiled two LCPs, and Gene Ontology (GO) enrichment analysis showed that they were enriched in histone modification and embryo development, etc. important biological processes respectively. The GOE_Score scoring indicated that the biclustering results of BDBB in the m⁶A epi-transcriptome data are more biologically meaningful than the results of other biclustering algorithms.