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Related Experiment Videos

Evaluating putative chimeric sequences from PCR-amplified products.

Juan M Gonzalez1, Johannes Zimmermann, Cesareo Saiz-Jimenez

  • 1Instituto de Recursos Naturales y Agrobiologia, CSIC Apartado 1052, 41080 Sevilla, Spain. jmgrau@irnase.csic.es

Bioinformatics (Oxford, England)
|September 7, 2004
PubMed
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Chimeric sequences generated during PCR can lead to inaccurate microbial diversity estimates. A new method efficiently detects these artifacts by comparing sequence fragments to known non-chimeric sequences.

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Microbial Ecology

Background:

  • Polymerase Chain Reaction (PCR) amplification of homologous genes from complex DNA mixtures frequently produces chimeric sequences.
  • Ribosomal RNA (rRNA) genes are crucial for microbial species identification and diversity assessment, making them susceptible to chimeric sequence artifacts.
  • Chimeric sequences can result in the erroneous identification of novel microbial species and inflate diversity estimates.

Purpose of the Study:

  • To develop an efficient strategy for detecting chimeric sequences in PCR-amplified DNA.
  • To address the challenge of false diversity estimates caused by chimeric sequences in microbial ecology studies.

Main Methods:

  • A novel strategy was developed to evaluate putative chimeric sequences by analyzing sequence fragments (words).

Related Experiment Videos

  • Nucleotide comparisons at corresponding positions were performed between the tested sequence and closely related reference sequences.
  • The method accounts for sequence variability across different regions to accurately assess chimeric origins.

    • Main Results:

    • The proposed strategy provides an efficient method for evaluating putative chimeric sequences.
    • This approach allows for a more accurate assessment of microbial diversity by filtering out artifacts.
    • The Ccode program implements this procedure for practical application.

    Conclusions:

    • Accurate detection of chimeric sequences is essential for reliable microbial diversity studies.
    • The developed method offers an efficient solution to identify and mitigate the impact of chimeric sequences.
    • This work contributes to improving the accuracy of microbial community analysis.