Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Genomic landscape and phylogenetic insights of <i>Burkholderia pseudomallei</i> over two decades in southern China and its global surveillance.

Emerging microbes & infections·2026
Same author

Construction of an artificial intelligence system for the Los Angeles classification-based assessment of reflux esophagitis (with video).

Digital health·2026
Same author

A multi-view TSK fuzzy system with deformable Gaussian membership functions and rule-level attention for classification.

PloS one·2026
Same author

Noncollinear ferrielectricity in a van der Waals crystal.

Nature communications·2026
Same author

Construction and validation of a multi-function artificial intelligence-assisted system for pressure injury recognition.

Frontiers in physiology·2026
Same author

Development and validation of an artificial intelligence-assisted system for automatic Boston scoring of bowel cleanliness in colonoscopy (with video).

Frontiers in public health·2026

Related Experiment Video

Updated: Jun 25, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A novel biclustering algorithm for mining m6A co-methylation patterns based on beta-binomial distribution and data

Zhaoyang Liu1, Yuteng Xiao2, Dao Xiang1

  • 1School of Information Engineering (School of Big Data), Xuzhou University of Technology, Xuzhou, China.

Plos Computational Biology
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced beta-binomial distribution biclustering algorithm (EBBM) to reliably mine N6-methyladenosine (m6A) co-methylation patterns from sequencing data, improving biological insights.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
13:21

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Related Experiment Videos

Last Updated: Jun 25, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
13:21

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • N6-methyladenosine (m6A) is crucial in RNA metabolism, physiology, and pathology, but its regulatory mechanisms remain complex.
  • Current computational methods for m6A co-methylation pattern mining lack robustness, especially with low signal-to-noise sequencing data.
  • Existing algorithms often yield unreliable performance, hindering the full understanding of m6A's role.

Purpose of the Study:

  • To develop a robust computational algorithm for accurate m6A co-methylation pattern mining.
  • To address the limitations of existing algorithms in handling noisy sequencing data.
  • To enhance the biological interpretability of m6A sequencing data analysis.

Main Methods:

  • Proposed an enhanced beta-binomial distribution biclustering algorithm (EBBM) incorporating a data screening strategy.
  • Utilized a Bayesian framework with Gibbs sampling for parameter inference.
  • Integrated data screening during parameter inference to mitigate the impact of low signal-to-noise data.

Main Results:

  • EBBM demonstrated superior performance in simulation experiments, effectively handling noisy data and accurately mining pre-planted co-methylation patterns.
  • The algorithm significantly outperformed mainstream biclustering methods in both simulation and real-world data analysis.
  • Applied to human m6A sequencing data (32 samples), EBBM identified two significant co-methylation patterns enriched in biological processes like negative regulation of phosphorylation and peptidyl lysine methylation.
  • GEO_Score analysis confirmed the enhanced biological relevance of EBBM-derived results compared to existing algorithms.

Conclusions:

  • The proposed EBBM algorithm offers a robust and reliable approach for m6A co-methylation pattern mining, particularly from noisy sequencing data.
  • EBBM enhances the accuracy and biological meaningfulness of m6A data analysis, facilitating deeper insights into RNA regulation.
  • This method provides a valuable tool for researchers investigating the complex roles of m6A in various biological processes and diseases.