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Related Experiment Videos

SEAN: SNP prediction and display program utilizing EST sequence clusters.

Derek Huntley1, Angela Baldo, Saurabh Johri

  • 1Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, SW7 2AZ, UK. d.huntley@imperial.ac.uk

Bioinformatics (Oxford, England)
|December 17, 2005
PubMed
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SEAN predicts single nucleotide polymorphisms (SNPs) using expressed sequence tag (EST) alignments. This application enhances SNP discovery by evaluating sequence identity and abundance for accurate predictions.

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are crucial genetic variations.
  • Identifying SNPs accurately is essential for genetic research and diagnostics.
  • Expressed sequence tag (EST) data offers a valuable resource for variant discovery.

Purpose of the Study:

  • To introduce SEAN, an application for predicting SNPs.
  • To leverage multiple sequence alignments from EST clusters for SNP identification.
  • To develop a quality assessment method for SNP predictions.

Main Methods:

  • Utilized expressed sequence tag (EST) clusters to generate multiple sequence alignments.
  • Developed an algorithm incorporating rules for sequence identity and SNP abundance.

Related Experiment Videos

  • Implemented a Java viewer for visualizing EST alignments and predicted SNPs.

    • Main Results:

    • SEAN successfully predicts single nucleotide polymorphisms (SNPs).
    • The algorithm's quality assessment rules enhance prediction reliability.
    • Visualizations aid in the interpretation of EST alignments and identified SNPs.

    Conclusions:

    • SEAN provides an effective computational approach for SNP prediction from EST data.
    • The integrated quality metrics improve the confidence in identified SNPs.
    • The Java viewer facilitates the analysis and validation of predicted SNPs.