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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Estimating sequencing error rates using families.

Kelley Paskov1, Jae-Yoon Jung2,3, Brianna Chrisman4

  • 1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. kpaskov@stanford.edu.

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|April 24, 2021
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Summary
This summary is machine-generated.

Family data can precisely estimate sequencing error rates for any platform or pipeline. This method reveals significant sample-to-sample variation and performance issues in certain genomic regions.

Keywords:
FamiliesMicroarraySequencing errorWhole-exome sequencingWhole-genome sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Genetic sequencing

Background:

  • Next-generation sequencing (NGS) is advancing into clinical practice, necessitating accurate error rate knowledge for patient care.
  • Evolving sequencing platforms and variant-calling pipelines complicate precise error quantification for specific assay/software combinations.
  • Family data offer a unique resource for estimating genome-wide sequencing error rates by identifying Mendelian errors.

Purpose of the Study:

  • To introduce a novel method for granular, per-sample estimation of sequencing precision and recall.
  • To validate the method's accuracy using family data and compare it against consensus approaches.
  • To demonstrate the method's versatility across different sequencing types and its sensitivity to pipeline improvements.

Main Methods:

  • Utilized Mendelian errors within family sequencing data to estimate precision and recall.
  • Validated accuracy using monozygotic twins and monozygotic quadruplets.
  • Applied the method to whole genome sequencing, whole exome sequencing, and microarray datasets.

Main Results:

  • Developed a method for highly granular, per-sample precision and recall estimates, independent of platform or calling methodology.
  • Demonstrated significant inter-sample variation in sequencing error rates (over an order of magnitude).
  • Quantified performance decreases in low-complexity genomic regions and with increasing distance from exome targets.
  • Showed comparable accuracy between lymphoblastoid cell lines and whole blood samples.
  • Confirmed that whole-genome sequencing can achieve microarray-level precision and recall at disease-associated single nucleotide variant sites.

Conclusions:

  • Family genotype datasets are powerful resources for fine-grained sequencing error estimation.
  • The developed method is applicable to any sequencing platform and variant-calling methodology.
  • This approach enables robust quality assessment for clinical sequencing applications.