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Related Concept Videos

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...
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...

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Related Experiment Video

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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

Gene set analysis in the cloud.

Lu Zhang1, Shengchang Gu, Yuan Liu

  • 1Laboratory of Cardiovascular research, CRP-Santé, Luxembourg L-1150, Luxembourg.

Bioinformatics (Oxford, England)
|November 16, 2011
PubMed
Summary
This summary is machine-generated.

We developed YunBe, a cloud-based gene set analysis tool for biomarker discovery. It offers a cost-effective and flexible alternative to traditional methods for analyzing gene expression data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cloud computing presents cost-effective and flexible solutions for bioinformatics tasks.
  • High-throughput sequence data analysis has benefited from cloud platforms.
  • Pathway-based or gene set analysis of expression data remains less explored in cloud environments.

Purpose of the Study:

  • To develop a cloud-based gene set analysis algorithm for biomarker identification.
  • To create a user-friendly tool, YunBe, deployable on Amazon Web Services.
  • To evaluate YunBe's performance against desktop and cluster-based solutions.

Main Methods:

  • Developed a novel gene set analysis algorithm optimized for cloud infrastructure.
  • Implemented the algorithm into a tool named YunBe, accessible via Amazon Elastic MapReduce.
  • Conducted comparative performance analysis of YunBe against traditional computational approaches.

Main Results:

  • YunBe provides a ready-to-use solution for gene set analysis on the cloud.
  • Performance benchmarks demonstrate YunBe's viability compared to desktop and cluster solutions.
  • The cloud-based approach offers flexibility and potential cost savings for expression data analysis.

Conclusions:

  • YunBe effectively addresses the need for cloud-based gene set analysis in bioinformatics.
  • This tool enhances biomarker identification capabilities through scalable cloud computing.
  • The study validates the potential of cloud platforms for advanced gene expression data analysis.