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A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE.

Kevin P Keegan1, William L Trimble, Jared Wilkening

  • 1Argonne National Laboratory, Argonne, Illinois, United States of America. kkeegan@anl.gov

Plos Computational Biology
|June 12, 2012
PubMed
Summary

We developed Duplicate Read Inferred Sequencing Error Estimation (DRISEE) to assess sequencing errors in metagenomic data. DRISEE offers more accurate error estimates than current methods, improving data quality for downstream analyses.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Sequencing quality assessment is crucial for reliable downstream analyses, especially in metagenomics.
  • Existing methods for estimating sequencing error have limitations, including reliance on reference genomes or Phred scores.
  • Artifactual duplicate reads (ADRs) are a common byproduct of sequencing platforms like 454 and Illumina.

Purpose of the Study:

  • To introduce a novel method, DRISEE (Duplicate Read Inferred Sequencing Error Estimation), for assessing sequencing error.
  • To provide accurate, platform-independent error estimates for shotgun metagenomic data.
  • To identify and characterize previously unknown sequencing errors.

Main Methods:

  • DRISEE analyzes artifactual duplicate reads (ADRs) to estimate sequencing error.

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  • Positional and global error estimates are generated for individual samples and entire datasets.
  • The method is applied to non-amplicon data from 454 and Illumina sequencing platforms.

    • Main Results:

    • DRISEE provides more accurate sequencing error estimates compared to existing methods for shotgun metagenomic data.
    • The method identified previously uncharacterized errors in de novo sequence data.
    • Positional error estimates can inform read trimming, while global estimates help identify problematic samples.

    Conclusions:

    • DRISEE is a valuable open-source tool for assessing sequencing quality in shotgun metagenomic datasets.
    • The method enhances the reliability of downstream analyses by providing accurate error quantification.
    • DRISEE contributes to a better understanding of sequencing error profiles across different platforms.