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Analysis of isoform expression from splicing array using multiple comparisons.

T Murlidharan Nair1

  • 1Departments of Biological Sciences, Computer Science/Informatics, Indiana University South Bend, Bloomington, IN, USA. mnair@iusb.edu

Methods in Molecular Biology (Clifton, N.J.)
|December 2, 2011
PubMed
Summary
This summary is machine-generated.

Alternative splicing produces numerous gene isoforms in the human genome, impacting disease research. This work details methods for comparing these alternatively spliced gene products using the R statistical framework.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • The human genome exhibits a high prevalence of alternatively spliced genes, known as isoforms.
  • Aberrant splicing is increasingly linked to various human diseases, necessitating detailed expression profiling.
  • Understanding isoform expression is crucial for deciphering gene regulation at both transcriptional and posttranscriptional levels.

Purpose of the Study:

  • To provide a comprehensive guide for comparing alternatively spliced gene isoforms.
  • To detail methodologies for analyzing isoform expression data.
  • To leverage the R statistical framework for isoform analysis.

Main Methods:

  • Utilizing high-throughput microarray technology for isoform profiling.
  • Applying the R statistical framework for data analysis.
  • Implementing exhaustive comparison methods for gene isoforms.

Main Results:

  • Established a robust framework for the exhaustive comparison of gene isoforms.
  • Enabled detailed analysis of isoform expression patterns.
  • Facilitated the study of gene regulation through alternative splicing.

Conclusions:

  • The R statistical framework offers powerful tools for isoform comparison.
  • Accurate isoform profiling is essential for disease research.
  • This chapter provides foundational methods for analyzing alternative splicing in genomics.