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Related Experiment Videos

Mining for single nucleotide polymorphisms and insertions/deletions in maize expressed sequence tag data.

Jacqueline Batley1, Gary Barker, Helen O'Sullivan

  • 1School of Biological Sciences, University of Bristol, United Kingdom.

Plant Physiology
|May 15, 2003
PubMed
Summary

A new computational method identifies single nucleotide polymorphisms (SNPs) and small insertions/deletions from expressed sequence tag data. This approach effectively distinguishes true genetic variations in maize, enhancing genomic analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Plant Science

Background:

  • Expressed sequence tags (ESTs) are valuable for gene discovery but contain sequencing errors.
  • Identifying true genetic variations like single nucleotide polymorphisms (SNPs) from EST data is challenging.
  • Accurate SNP discovery is crucial for genetic mapping and marker-assisted selection in crops.

Purpose of the Study:

  • To develop and validate a computational method for identifying candidate SNPs and small insertions/deletions (indels) from EST data.
  • To distinguish true polymorphisms from sequencing errors using a redundancy-based approach.
  • To assess the accuracy and utility of the developed method in maize (Zea mays).

Main Methods:

  • Developed a computer-based method utilizing redundancy in sequence read alignments to identify valid SNPs.

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  • Implemented a cosegregation measure to assess the validity of SNP patterns across multiple loci.
  • Applied the method to a large dataset of 102,551 maize EST sequences.

    • Main Results:

    • Identified 14,832 candidate polymorphisms with an SNP redundancy score of two or greater.
    • High redundancy and cosegregation scores correlated with true SNPs, confirmed by haplotype segregation analysis.
    • Validation of 264 candidate SNPs across 27 loci in four inbred maize lines supported the method's accuracy.
    • Observed SNP transition/transversion ratios and indel size frequencies consistent with direct sequencing methods.

    Conclusions:

    • The developed computational method reliably identifies candidate SNPs and indels from EST data.
    • Redundancy and cosegregation scores serve as effective metrics for distinguishing true genetic variation from sequencing errors.
    • This approach provides a robust tool for large-scale SNP discovery in maize and other organisms with EST data.