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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data
Goo Jun1, Matthew Flickinger, Kurt N Hetrick
1Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
American Journal of Human Genetics
|October 30, 2012
Summary
New methods reliably detect low-level DNA sample contamination in sequencing studies using combined or individual data types. This helps prevent genotype errors and false associations, ensuring data integrity.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- DNA sample contamination is a significant issue in DNA sequencing, leading to genotype misclassification and false associations.
- Existing methods primarily address cross-species contamination, with limited options for detecting within-species contamination.
Purpose of the Study:
- To develop and evaluate methods for identifying within-species DNA sample contamination in sequencing studies.
- To assess the impact of contamination on genotype accuracy and propose preventative strategies.
Main Methods:
- Developed methods utilizing combinations of sequencing reads and array-based genotype data.
- Implemented approaches using sequence reads alone or array-based genotype data alone.
- Validated methods using in silico and experimentally contaminated samples.
Main Results:
- Reliably detected and estimated contamination levels as low as 1%.
- Demonstrated the utility of both combined and individual data analyses for contamination detection.
- Evaluated the impact of contamination on genotype accuracy.
Conclusions:
- The developed methods offer robust detection of within-species DNA contamination.
- Screening for contamination prior to or after data generation is crucial for maintaining data integrity.
- Effective strategies can be implemented to prevent contamination in sequencing studies.
