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Related Concept Videos

Genomics02:02

Genomics

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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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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Related Experiment Video

Updated: Nov 27, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data.

Sebastian Canzler1, Jörg Hackermüller2

  • 1Department of Molecular Systems Biology, Helmholtz-Centre for Environmental Research - UFZ, Permoserstraße 15, 04318, Leipzig, Germany. sebastian.canzler@ufz.de.

BMC Bioinformatics
|December 8, 2020
PubMed
Summary

The multiGSEA package offers a versatile solution for analyzing multiple omics data layers. It integrates gene set enrichment analysis (GSEA) across various omics types, providing robust pathway enrichment insights.

Keywords:
BioconductorGSEAMulti-omicsPathway enrichmentRSoftware

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene set and pathway enrichment methods are crucial for interpreting omics data and understanding molecular responses to diseases or treatments.
  • Gene Set Enrichment Analysis (GSEA) is a well-established method for controlling statistical errors in enrichment analyses.
  • The increasing demand for multi-omics integration has led to the development of specialized tools, yet many have limitations.

Purpose of the Study:

  • To introduce multiGSEA, a novel package for combined GSEA-based pathway enrichment analysis across multiple omics layers.
  • To provide a versatile tool that overcomes limitations of existing multi-omics integration methods.

Main Methods:

  • The multiGSEA package performs single-omics enrichment using the GSEA algorithm.
  • It queries 8 different pathway databases and supports 11 organisms.
  • Transcript, protein, and metabolite IDs are comprehensively mapped for integrated analysis.

Main Results:

  • multiGSEA calculates a combined GSEA-based pathway enrichment score from multiple omics data.
  • It generates a robust composite measure for multi-omics pathway enrichment.
  • The package facilitates seamless integration of diverse omics data types.

Conclusions:

  • multiGSEA is a highly versatile tool for multi-omics pathway integration.
  • It minimizes restrictions related to omics layer selection, pathway databases, organism choice, and identifier mapping.
  • The package is publicly available for broader scientific use.