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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
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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 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...
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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...
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Performing integrative functional genomics analysis in GeneWeaver.org.

Jeremy J Jay1, Elissa J Chesler

  • 1The Jackson Laboratory, Bar Harbor, ME, USA.

Methods in Molecular Biology (Clifton, N.J.)
|November 16, 2013
PubMed
Summary
This summary is machine-generated.

GeneWeaver integrates diverse functional genomics data, enabling biologists to analyze gene sets and functions more effectively. This web-based system facilitates data sharing and novel computational approaches for biological discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Functional genomics experiments generate large, complex datasets quantifying molecular entity relationships with biological processes.
  • Integrating these gene-centric data is challenging due to diverse, non-interoperable storage and the need for gene identifier harmonization across species.
  • Existing data integration methods often require significant computational expertise from biologists.

Purpose of the Study:

  • To present GeneWeaver, a web-based software system designed for the integration and analysis of diverse functional genomics data.
  • To provide biologists with an interactive environment and a suite of combinatorial tools for exploring gene sets and functions.
  • To facilitate data sharing and enable novel integrative analyses, including advanced set-set integration algorithms.

Main Methods:

  • Development of the GeneWeaver web-based software system.
  • Aggregation of a large data archive from various functional genomics sources.
  • Implementation of account management for group data sharing.
  • Integration of curated gene set retrieval, user data upload/storage, and advanced analytical algorithms.

Main Results:

  • GeneWeaver provides a unified platform for accessing and analyzing diverse functional genomics datasets.
  • The system supports collaborative research through user-defined group sharing of data and results.
  • Novel algorithmic approaches for set-set integration of genes and functions are available within the interactive environment.
  • Biologists can perform integrative analyses without extensive computational background.

Conclusions:

  • GeneWeaver effectively addresses the challenges of integrating and analyzing large-scale functional genomics data.
  • The platform empowers biologists to uncover convergent evidence, identify similar molecular entities, and compare biological processes.
  • GeneWeaver enhances biological discovery by providing accessible tools for data integration and sophisticated analysis.