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Related Experiment Videos

Draft versus finished sequence data for DNA and protein diagnostic signature development.

Shea N Gardner1, Marisa W Lam, Jason R Smith

  • 1Pathogen Bio-Informatics, Lawrence Livermore National Laboratory, PO Box 808, L-174, Livermore, CA 94551, USA. gardner26@llnl.gov

Nucleic Acids Research
|October 26, 2005
PubMed
Summary

A new Sequencing Analysis Pipeline (SAP) helps optimize pathogen genome sequencing. High-quality target genomes and low-quality near neighbors are cost-effective for DNA signature prediction.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Pathogen genome sequencing is expensive, requiring efficient resource allocation.
  • Developing diagnostic DNA and protein signatures relies on high-quality genomic data.

Purpose of the Study:

  • To develop a computational Sequencing Analysis Pipeline (SAP) to guide decisions on necessary genomic sequencing depth.
  • To assess the impact of sequencing quality and coverage on diagnostic signature prediction.

Main Methods:

  • SAP utilizes simulations to estimate optimal sequencing requirements for target genomes and near neighbors (NNs).
  • Evaluated Marburg and variola virus sequences to determine draft data sufficiency versus finished sequencing needs.
  • Simulated various error rates and coverage levels (3x-8x) to assess DNA and protein signature prediction accuracy.

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Main Results:

  • Intermediate to high-quality draft sequencing (10^-3 to 10^-5 error rates, ~8x coverage) of target organisms is sufficient for DNA signature prediction.
  • Low-quality draft sequencing (~1% error rate, 3x-6x coverage) of target isolates is inadequate for DNA signatures, but low-quality NNs are acceptable if target genomes are high-quality.
  • Sequencing errors in target genomes significantly hinder amino acid conservation detection for protein signatures, even with high-quality drafts.

Conclusions:

  • A cost-effective strategy for DNA signature prediction involves high-quality sequencing of target genomes and low-quality sequencing of NNs.
  • This approach may lead to underestimation of predicted protein signatures due to sequencing errors.
  • SAP provides a framework for optimizing sequencing investments in pathogen diagnostics.