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 Experiment Video

Updated: May 28, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

AWmeta Empowers Adaptively Weighted Transcriptomic Meta-Analysis.

Yanshi Hu1, Zixuan Wang1, Yueming Hu1

  • 1Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China.

Current Issues in Molecular Biology
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

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

PepAnno: A structure-aware deep learning framework for bioactive peptide prediction, structural visualization, and physicochemical profiling.

PLoS computational biology·2026
Same author

Efficient EVA decapsulation using ethyl lactate for delaminating and recycling of end-of-life crystalline silicon photovoltaic modules.

Waste management (New York, N.Y.)·2026
Same author

T2T-Hub: a central platform for analyzing plant and animal telomere-to-telomere genomes.

Nucleic acids research·2026
Same author

Bamboo-Like Whiskers-Reinforced Bioceramics Accelerate Large Segmental Bone Regeneration via Dual Modulation of Type-H Vessels and Osteoinduction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Brake-Drive Osteo System: Sequential Modulation of the Inflammatory Microenvironment and Osteogenesis for Osteoporotic Bone Defect Regeneration.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Extracellular matrix polysaccharides and glycoproteins in ovarian cancer: structural-functional macromolecular perspectives and potential exosome-derived biomarkers.

Cell communication and signaling : CCS·2026
Same journal

Correction: Wang et al. Phosphatidylserine Decarboxylase Promotes Ferroptosis Through STAT3/GPX4 Signaling in Gastric Cancer. <i>Curr. Issues Mol. Biol.</i> 2026, <i>48</i>, 300.

Current issues in molecular biology·2026
Same journal

Exploring the Relationship Between Protein-Level Ratios (rQLTs) and Duodenal Ulcer.

Current issues in molecular biology·2026
Same journal

Metformin as an Innate Immune Modulator: Metabolic and Epigenetic Reprogramming of Innate Immune Cells and Therapeutic Implications.

Current issues in molecular biology·2026
Same journal

Comprehensive Bioinformatic Characterization of CD70, CD80, and TIGIT as Diagnostic, Prognostic, and Immune Biomarkers in Pan-Cancer.

Current issues in molecular biology·2026
Same journal

Genome-Wide Identification and Expression Analysis of the Thaumatin-like Protein Genes in <i>Filipendula ulmaria</i> under <i>Bipolaris sorokiniana</i> Infection.

Current issues in molecular biology·2026
Same journal

Recent Dominant Transposition Events Affect Gene Regulatory Regions, but Not Coding Sequences, in Polar and Brown Bear Genomes.

Current issues in molecular biology·2026
See all related articles

AWmeta is a novel framework for transcriptomic meta-analysis, improving the identification of differentially expressed genes (DEGs) in complex diseases like Parkinson's and Crohn's. It enhances accuracy and reduces false positives by adaptively weighting study contributions.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcriptomic meta-analysis integrates multiple studies to identify differentially expressed genes (DEGs).
  • Existing methods using p-values or effect sizes have limitations in detecting subtle yet crucial gene signatures.
  • There is a need for more powerful and robust meta-analytical approaches for transcriptomic data.

Purpose of the Study:

  • To introduce AWmeta, a novel adaptively weighted framework for transcriptomic meta-analysis.
  • To unify p-value and effect-size meta-analytical paradigms.
  • To improve the identification of high-fidelity DEGs and enhance biological veracity in complex diseases.

Main Methods:

  • Developed AWmeta, an adaptively weighted meta-analysis framework.
Keywords:
differentially expressed genedisease gene prioritizationfunctional enrichmentrandom-effects modeltranscriptomic meta-analysis

More Related Videos

ATAC-Seq Optimization for Cancer Epigenetics Research
07:13

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

Cost-Efficient Transcriptomic-Based Drug Screening
06:40

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

Related Experiment Videos

Last Updated: May 28, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

ATAC-Seq Optimization for Cancer Epigenetics Research
07:13

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

Cost-Efficient Transcriptomic-Based Drug Screening
06:40

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

  • Benchmarked AWmeta on 35 Parkinson's and Crohn's disease transcriptomic datasets.
  • Compared AWmeta against the random-effects model (REM) and constituent studies.

    • Main Results:

    • AWmeta identified higher-fidelity DEGs with fewer false positives compared to REM.
    • The framework demonstrated robust gene differential expression quantification.
    • AWmeta successfully prioritized biologically relevant, tissue-contextual genes for Parkinson's and Crohn's disease.

    Conclusions:

    • AWmeta offers a more accurate and reliable method for transcriptomic meta-analysis.
    • The framework enhances the identification of disease-specific gene signatures.
    • AWmeta serves as a valuable tool for precision transcriptomic integration and mechanistic insights.