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

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

PLoS computational biology·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

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
Same author

Real-time deep-learning NICE classification and withdrawal-speed monitoring for colorectal endoscopy.

Digital health·2025

Related Experiment Video

Updated: Nov 11, 2025

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

11.5K

BDBB: A Novel Beta-Distribution-Based Biclustering Algorithm for Revealing Local Co-Methylation Patterns in

Zhaoyang Liu, Yuteng Xiao, Hongsheng Yin

    IEEE Journal of Biomedical and Health Informatics
    |March 25, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel biclustering algorithm, BDBB, to identify local co-methylation patterns (LCPs) in N6-methyladenosine (m6A) epitranscriptome data. BDBB effectively reveals biologically significant patterns in complex RNA methylation data.

    More Related Videos

    Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
    14:56

    Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

    Published on: May 6, 2022

    4.8K
    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

    10.3K

    Related Experiment Videos

    Last Updated: Nov 11, 2025

    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

    11.5K
    Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
    14:56

    Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

    Published on: May 6, 2022

    4.8K
    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

    10.3K

    Area of Science:

    • Epitranscriptomics
    • Computational Biology
    • Bioinformatics

    Background:

    • N6-methyladenosine (m6A) modifications are critical in RNA regulation but their mechanisms are complex and not fully understood.
    • Existing experimental methods for studying m6A are costly, and computational approaches are limited.
    • Identifying local co-methylation patterns (LCPs) offers a promising avenue to decipher m6A regulatory mechanisms.

    Purpose of the Study:

    • To develop a novel computational method for discovering local co-methylation patterns (LCPs) in m6A epitranscriptome data.
    • To address the limitations of experimental and existing computational methods in analyzing complex m6A data.
    • To uncover novel biological insights into RNA methylation regulation.

    Main Methods:

    • Proposed a novel biclustering algorithm named BDBB (biclustering algorithm based on the beta distribution).
    • Utilized Gibbs sampling for parameter estimation within the BDBB model.
    • Modeled LCPs as distinct beta distributions against a background distribution.

    Main Results:

    • BDBB demonstrated high accuracy (near 100%) in extracting simulated LCPs and their overlaps.
    • Applied to MeRIP-Seq data, BDBB identified two significant LCPs across human cell lines.
    • Gene Ontology (GO) enrichment analysis linked these LCPs to histone modification and embryo development.

    Conclusions:

    • BDBB is an effective computational tool for mining LCPs in m6A epitranscriptome data.
    • The identified LCPs are biologically relevant, associating with key cellular processes.
    • BDBB provides a more biologically meaningful approach to m6A data analysis compared to other biclustering algorithms.