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Related Concept Videos

Genomics02:02

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Updated: Aug 6, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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MOPA: An integrative multi-omics pathway analysis method for measuring omics activity.

Jaemin Jeon1, Eon Yong Han2, Inuk Jung2

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-Gu, Seoul, Republic of Korea.

Plos One
|March 17, 2023
PubMed
Summary
This summary is machine-generated.

MOPA is a new multi-omics pathway analysis tool that scores pathway enrichment. It identifies key biological pathways and their regulatory relationships across different omics data types, outperforming existing methods.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Pathway analysis is crucial for interpreting genomic and transcriptomic data.
  • Existing methods often analyze single omics types, limiting comprehensive biological insights.

Purpose of the Study:

  • To introduce MOPA (Multi-Omics Pathway Analysis), a novel integrative method for pathway analysis.
  • To develop metrics for scoring multi-omics enrichment (mES) and omics contribution (OCR) within pathways.

Main Methods:

  • MOPA integrates gene expression, miRNA, and methylation data for sample-wise pathway scoring.
  • It calculates multi-omics Enrichment Scores (mES) and Omics Contribution Rates (OCR).
  • Evaluated on nine cancer types and 93 clinical features.

Main Results:

  • MOPA demonstrated superior or equal performance compared to existing pathway analysis tools.
  • The mES and OCR metrics effectively explain pathway-specific omics relationships.
  • Identified distinct mES and OCR values for the TGF-beta signaling pathway in colon adenocarcinoma CMS4 subtype, linked to mRNA and miRNA expression.

Conclusions:

  • MOPA provides a robust framework for multi-omics pathway analysis.
  • The mES and OCR metrics offer valuable insights into omics interplay within biological pathways.
  • MOPA facilitates the discovery of clinically relevant multi-omics signatures in cancer.