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Computer Tools to Analyze Microarray Data.

Giuseppe Agapito1

  • 1Department of Medical and Surgical Science, University Magna Graecia, Catanzaro, Italy. agapito@unicz.it.

Methods in Molecular Biology (Clifton, N.J.)
|May 23, 2019
PubMed
Summary
This summary is machine-generated.

This chapter reviews user-friendly software tools for analyzing microarray data. These tools help researchers easily interpret large genomic datasets for applications in medicine and biology, including SNP identification.

Keywords:
Data miningGenomicsGenotypingMicroarraysStatistical analysis

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Area of Science:

  • Genomic analysis
  • Bioinformatics
  • Computational biology

Background:

  • Microarrays generate vast amounts of data crucial for biological and medical research.
  • Analyzing this complex data requires advanced computational tools for efficient interpretation.
  • Current challenges include managing large datasets and making results comprehensible.

Purpose of the Study:

  • To review user-friendly software tools for microarray data analysis.
  • To identify tools that simplify the interpretation of complex genomic datasets.
  • To support researchers, including non-experts, in managing and analyzing microarray data.

Main Methods:

  • Review of existing software tools for microarray data analysis.
  • Focus on tools designed for ease of use and accurate prediction.
  • Evaluation of tools for their ability to handle large datasets and identify Single Nucleotide Polymorphisms (SNPs).

Main Results:

  • Identification of several software tools suitable for non-expert users.
  • These tools facilitate efficient data analysis and interpretation of microarray experiments.
  • The reviewed tools aid in discriminating and identifying SNPs associated with gene activity, drug response, and complex diseases.

Conclusions:

  • Accessible software tools are essential for advancing microarray data analysis in biology and medicine.
  • The reviewed tools empower researchers to better understand genomic data and its implications.
  • Development of intuitive and powerful analytical software enhances the utility of microarrays in scientific discovery.