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Related Experiment Videos

Bioinformatics approaches and resources for single nucleotide polymorphism functional analysis.

Sean Mooney1

  • 1Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, 714 N Senate Ave; EF 250, Indianapolis, IN 46202, USA. sdmooney@iupui.edu

Briefings in Bioinformatics
|April 14, 2005
PubMed
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This review covers computational tools and resources for understanding genetic variation effects. It focuses on single nucleotide polymorphisms (SNPs) and their role in disease and genetics studies.

Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology

Background:

  • The human genome project spurred research into genetic variation.
  • Over four million single nucleotide polymorphisms (SNPs) are identified in the human genome.
  • Understanding genetic variation is crucial for disease research and genetics studies.

Purpose of the Study:

  • To provide an overview of computational tools and resources for analyzing functional genetic variation.
  • To summarize public resources for SNP identification and characterization.

Main Methods:

  • Review of existing literature and public databases.
  • Functional inference methods from structural and functional genomics.
  • Analysis from the perspectives of structure, expression, evolution, and phenotype.

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Main Results:

  • A broad range of tools and resources are available for studying genetic variation.
  • Bioinformatics approaches are effective for functional inference.
  • Public resources facilitate SNP identification and characterization.

Conclusions:

  • Computational methods are essential for predicting the effects of genetic variation.
  • Understanding SNPs aids in genetics studies and elucidating disease mechanisms.
  • Accessible resources support the study of functional variation.