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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Sample Tracking Using Unique Sequence Controls.

Richard A Moore1, Thomas Zeng2, T Roderick Docking2

  • 1Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia; Faculty of Health Science, Simon Fraser University, Burnaby, British Columbia.

The Journal of Molecular Diagnostics : JMD
|December 15, 2019
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Summary
This summary is machine-generated.

A novel genetic barcoding method uses unique DNA sequences to track samples in sequencing pipelines. This inexpensive and flexible approach accurately verifies sample identity and detects cross-contamination in clinical diagnostics.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Accurate sample tracking is critical for high-throughput sequencing, especially in clinical settings.
  • Existing methods for sample identity verification have limitations.
  • There is a need for a simple, accurate, and intrinsic sample tracking solution.

Purpose of the Study:

  • To develop an effective genetic barcoding approach for intrinsic sample tracking in sequencing pipelines.
  • To create a reliable method for verifying sample identity and quantifying cross-contamination.

Main Methods:

  • Developed a panel of unique, short DNA sequences (approx. 200 bp) with low homology to existing databases.
  • Cloned these sequences into a common vector for easy integration.
  • Added unique DNA sequences to samples upon receipt.
  • Detected control sequences via PCR and sequencing throughout the pipeline.

Main Results:

  • The genetic barcodes were successfully detected at various stages of the sequencing process.
  • The method accurately confirmed sample identity.
  • Cross-contamination levels could be reliably determined.
  • The approach proved to be inexpensive, flexible, and platform-agnostic.

Conclusions:

  • This genetic barcoding method offers a robust and efficient solution for intrinsic sample tracking in sequencing.
  • It is suitable for targeted clinical diagnostic, whole-genome, and RNA-sequencing applications.
  • The approach enhances data integrity and reliability in high-throughput sequencing workflows.