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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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Identification of Culprit Genes for Different Diseases by Analyzing Microarray Data.

Ayushman Kumar Banerjee1, Shrayana Ghosh2, Chittabrata Mal1

  • 1Department of Bioinformatics, Maulana Abul Kalam Azad University of Technology, West Bengal, Haringhata, West Bengal, India.

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Summary

This study details using R programming for microarray analysis to identify disease-causing genes. It covers data pre-processing, normalization, statistical analysis, and functional enrichment for biological insights.

Keywords:
Differentially expressed genesGene ontologyMicroarray analysisPathway analysisR-programming

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying disease-causing genes is crucial for understanding disease mechanisms.
  • Microarray analysis is a key technique for detecting differentially expressed genes between conditions.
  • The R programming language offers robust tools for complex biological data analysis.

Purpose of the Study:

  • To outline a comprehensive pipeline for identifying disease-associated genes using microarray data.
  • To demonstrate the application of R programming and its packages for microarray analysis.
  • To facilitate the functional interpretation of identified genes through ontology and pathway analysis.

Main Methods:

  • Utilizing R programming language and specialized statistical packages for microarray data analysis.
  • Implementing data pre-processing and normalization techniques to ensure data quality.
  • Applying statistical analysis with visualization tools like heatmaps and box plots.
  • Performing gene ontology and pathway analysis for functional characterization of altered genes.

Main Results:

  • A structured workflow for analyzing raw microarray data to pinpoint potential disease-causing genes.
  • Demonstration of effective visualization techniques for interpreting gene expression patterns.
  • Identification of gene ontology and pathway enrichment to understand biological functions.

Conclusions:

  • R programming provides a powerful and versatile platform for comprehensive microarray data analysis.
  • The presented pipeline enables efficient identification and functional annotation of disease-related genes.
  • This approach aids in advancing the understanding of disease biology and potential therapeutic targets.