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Related Experiment Videos

High-throughput identification, database storage and analysis of SNPs in EST sequences.

F J Useche1, G Gao, M Harafey

  • 1Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way, Newark, DE 19711, USA. useche@capsl.udel.edu

Genome Informatics. International Conference on Genome Informatics
|January 16, 2002
PubMed
Summary

This study introduces an in-silico pipeline for discovering single nucleotide polymorphisms (SNPs) in maize EST data. The developed software efficiently identifies SNPs and insertion/deletions, aiding genetic research.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are frequent DNA variations crucial for genetic markers.
  • Experimental SNP discovery is complex and costly.
  • In-silico SNP discovery offers a cost-effective alternative using existing large datasets.

Purpose of the Study:

  • To design and implement an in-silico SNP detection software pipeline.
  • To address challenges in large-scale in-silico SNP discovery.
  • To facilitate preliminary analysis and basic statistics of SNP data.

Main Methods:

  • Developed an integrated pipeline for data processing from sequence collection to SNP database.
  • Optimized PolyBayes parameters for SNP detection in maize expressed sequence tag (EST) data.

Related Experiment Videos

  • Integrated PHRAP and CAT assemblers, and a Bayesian engine (PolyBayes) for SNP detection.

    • Main Results:

    • Detected 2439 SNPs and 822 insertion/deletions (INDELs) in 68,000 maize ESTs with high confidence (PolyBayes probability > 0.99).
    • Implemented a user interface for preliminary data analysis and statistics.
    • Ensured smooth data transition between pipeline components using data interfaces.

    Conclusions:

    • The developed in-silico pipeline effectively addresses challenges in large-scale SNP discovery.
    • The pipeline facilitates efficient identification and preliminary analysis of SNPs and INDELs.
    • This approach aids in gaining insights into polymorphism distribution and significance prior to experimental validation.