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Updated: Jul 10, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

CHARR efficiently estimates contamination from DNA sequencing data.

Wenhan Lu¹, Laura D Gauthier², Timothy Poterba¹

¹Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

American Journal of Human Genetics

|November 24, 2023

Summary

This summary is machine-generated.

DNA sample contamination in sequencing can cause errors. A new metric, CHARR (contamination from homozygous alternate reference reads), estimates contamination using variant data, offering a cost-effective and efficient solution for large datasets.

Keywords:

DNA sequencing contamination data science genetic research quality control variant calling

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Area of Science:

Genomics
Bioinformatics

Background:

DNA sample contamination is a significant challenge in whole-genome and whole-exome sequencing.
Contamination can lead to genotyping errors and impact variant call quality.

Purpose of the Study:

To develop a novel metric for estimating DNA sample contamination.
To provide a cost-effective and efficient method for contamination assessment in large-scale sequencing projects.

Main Methods:

Introducing CHARR (contamination from homozygous alternate reference reads), a metric leveraging variant-level data.
Utilizing single-sample gVCFs, VCF/BCF callsets, or Hail VariantDataset formats for computation.

Main Results:

CHARR accurately estimates contamination levels, comparable to existing methods.
The CHARR metric significantly reduces storage and computational costs.

Conclusions:

CHARR offers an accurate and efficient approach to DNA sample contamination assessment.
This method improves downstream analysis for ultra-large whole-genome and exome sequencing datasets.