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Related Concept Videos

DNA Microarrays02:34

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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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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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Microarray Analysis for Saccharomyces cerevisiae
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Web and Cloud Computing to Analyze Microarray Data.

Barbara Calabrese1

  • 1Data Analytics Center, Università degli Studi "Magna Graecia" di Catanzaro, Catanzaro, CZ, Italy. calabreseb@unicz.it.

Methods in Molecular Biology (Clifton, N.J.)
|December 13, 2021
PubMed
Summary
This summary is machine-generated.

Microarray technology measures thousands of gene expression levels. This chapter details essential web and cloud tools for storing and analyzing this massive experimental data, aiding biological interpretation.

Keywords:
Cloud computingInfrastructure as a Service (IaaS)MicroarrayPlatform as a Service (PaaS)Software as a Service (SaaS)Web computing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Microarray technology enables high-throughput, simultaneous measurement of hundreds of thousands of gene expression levels.
  • The exponential growth of experimental data necessitates robust storage and analysis solutions.
  • Effective interpretation of complex biological data is crucial for scientific advancement.

Purpose of the Study:

  • To present key databases for microarray data storage.
  • To introduce essential web and cloud computing tools for microarray data analysis.
  • To guide biologists in managing and interpreting large-scale gene expression datasets.

Main Methods:

  • Review of prominent microarray data storage databases.
  • Identification and description of relevant web-based analysis platforms.
  • Exploration of cloud computing solutions for high-throughput data processing.

Main Results:

  • A curated list of essential databases for microarray data.
  • An overview of accessible web and cloud tools for data analysis.
  • Guidelines for efficient data management and interpretation.

Conclusions:

  • Access to appropriate databases and tools is vital for microarray data analysis.
  • Web and cloud computing offer scalable solutions for handling large biological datasets.
  • This chapter provides a foundational resource for researchers utilizing microarray technology.