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Related Concept Videos

Genomics02:02

Genomics

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...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Genomic DNA in Eukaryotes00:58

Genomic DNA in Eukaryotes

Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.

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Related Experiment Video

Updated: May 28, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Integrating diverse genomic data using gene sets.

Svitlana Tyekucheva1, Luigi Marchionni, Rachel Karchin

  • 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02115, USA.

Genome Biology
|October 25, 2011
PubMed
Summary
This summary is machine-generated.

New data analysis methods interpret multiple genomic features simultaneously. These tools reveal disease-related gene sets missed by analyzing individual data types, improving genetic effect detection.

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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Simultaneous measurement of multiple genomic features (e.g., DNA, RNA, epigenetics) generates complex datasets.
  • Analyzing diverse genomic data types individually can limit the discovery of biological insights and disease mechanisms.

Purpose of the Study:

  • To introduce and evaluate novel data analysis methods for interpreting multi-omic genomic data from single biological samples.
  • To develop tools that provide an interpretable common scale for diverse genomic information using gene sets.

Main Methods:

  • Development of computational methods for integrated analysis of multiple genomic datasets.
  • Utilizing gene sets as a unifying framework to interpret heterogeneous genomic measurements.
  • Evaluation of the methods' ability to detect genetic effects and discover disease-related gene sets.

Main Results:

  • The developed methods successfully interpret simultaneous measurements of multiple genomic features.
  • Genetic effects can be detected even when acting through different mechanisms across samples.
  • Important disease-related gene sets were discovered and validated, which were not identifiable through single-data-type analyses.

Conclusions:

  • Integrated analysis of multi-omic data using gene sets enhances the interpretation of genomic information.
  • The proposed methods offer a powerful approach for discovering novel biological insights and disease-related genetic factors.
  • This approach improves the detection and understanding of genetic effects in complex biological systems.