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A statistical framework for detecting mislabeled and contaminated samples using shallow-depth sequence data.

Ariel W Chan1, Amy L Williams2, Jean-Luc Jannink3

  • 1Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, 407 Bradfield Hall, Ithaca, NY, 14853, USA. ac2278@cornell.edu.

BMC Bioinformatics
|December 14, 2018
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Researchers developed a new method to detect errors in DNA sequencing data. This approach accurately identifies sample mix-ups or contamination, improving data reliability for genetic studies.

Keywords:
Biological replicationError detectionMislabeled samplesShallow-depth sequence dataTechnical replication

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Replicate DNA sequencing is common for data validation.
  • Errors like contamination or sample mix-ups can occur during sequencing.
  • Existing error detection methods are often limited, especially for low-depth data.

Purpose of the Study:

  • To develop a robust method for detecting errors in DNA sequencing replicates.
  • To overcome limitations of existing ad hoc and pairwise comparison methods.
  • To provide a tool suitable for various sequencing depths.

Main Methods:

  • Utilized Bayes Theorem to calculate posterior probabilities.
  • Inferred relationships between putative replicate samples.
  • Developed an R package named BIGRED (Bayes Inferred Genotype Replicate Error Detector).

Main Results:

  • The new method is suitable for shallow, moderate, and high-depth sequence data.
  • Accurate error detection was achieved in simulation experiments.
  • The approach can infer which samples originate from an identical genotypic source.

Conclusions:

  • The BIGRED method effectively addresses limitations of current error detection approaches.
  • It offers a reliable tool for ensuring data integrity in genomic studies.
  • The R package is freely available for researchers.