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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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Analysis of Microarray Data from Medulloblastoma Tissue Samples.

Debojyoti Dhar1, Gopala Kallapura2

  • 1Leucine Rich Bio Pvt Ltd., Bengaluru, Karnataka, India. ddhar@leucinerichbio.com.

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Summary
This summary is machine-generated.

Microarray technology enables simultaneous gene expression analysis using DNA chips. This chapter details the computational analysis of data generated from microarray experiments to understand gene expression patterns.

Keywords:
Affymatrix arrays [more specific can be—GeneChip® Mouse Gene 2.0 ST Array (Affymetrix)]Gene Set Enrichment Analysis (GSEA)KEGG (Kyoto Encyclopedia of Genes and Genomes)LIMMA (Linear Models for Microarray)MedulloblastomaMicroarraySTRING (Search Tool for Recurring Instances of Neighbouring Genes)

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Area of Science:

  • Molecular Biology
  • Bioinformatics

Background:

  • Microarray technology is a laboratory tool for high-throughput gene expression profiling.
  • Gene chips, or DNA chips, feature thousands of DNA spots on microscope slides, acting as probes.
  • Gene expression analysis identifies the transcriptome, the set of messenger RNA (mRNA) transcripts.

Purpose of the Study:

  • To outline the computational analysis steps following a microarray experiment.
  • To provide a guide for interpreting gene expression data.

Main Methods:

  • The chapter focuses on the computational analysis of data derived from microarray experiments.
  • It covers the process of analyzing gene expression data.

Main Results:

  • The primary outcome is a structured approach to microarray data analysis.
  • Understanding gene expression patterns through computational methods.

Conclusions:

  • Computational analysis is crucial for extracting meaningful biological insights from microarray data.
  • This process facilitates the study of the transcriptome.