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Related Experiment Videos

Bioinformatics tools for single nucleotide polymorphism discovery and analysis.

Robert J Clifford1, Michael N Edmonson, Cu Nguyen

  • 1Laboratory of Population Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA. clifforr@mail.nih.gov

Annals of the New York Academy of Sciences
|June 23, 2004
PubMed
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Single nucleotide polymorphisms (SNPs) are key genetic markers for disease research. Computational tools help discover and analyze SNPs, predicting their impact and annotating associated genes for researchers.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are crucial genetic variants for understanding disease etiology.
  • SNPs serve as markers in genetic mapping and genome-wide association studies (GWAS).
  • These variants can influence predisposition to diseases like diabetes, hypertension, and cancer, or affect disease progression.

Purpose of the Study:

  • To leverage bioinformatics for the discovery and analysis of single nucleotide polymorphisms (SNPs).
  • To computationally predict the functional impact (neutral or deleterious) of identified SNPs.
  • To annotate genes containing SNPs and provide accessible data tools for the research community.

Main Methods:

  • Utilizing computational methods for SNP identification and classification.

Related Experiment Videos

  • Employing bioinformatics techniques for SNP analysis and gene annotation.
  • Developing Internet-accessible tools for data retrieval based on gene, map location, or expression.

    • Main Results:

    • Successful identification and characterization of SNPs using computational approaches.
    • Prediction of SNP neutrality or deleteriousness to assess potential disease relevance.
    • Annotation of genes associated with SNPs, enriching genetic datasets.

    Conclusions:

    • Bioinformatics is essential for efficient SNP discovery and analysis in genetic research.
    • Computational prediction of SNP effects aids in understanding disease associations.
    • Accessible data resources empower researchers to utilize SNP information effectively for disease studies.