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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

MICon Contamination Detection Workflow for Next-Generation Sequencing Laboratories Using Microhaplotype Loci and

Jagadheshwar Balan¹, Tejaswi Koganti¹, Shubham Basu¹

¹Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota.

The Journal of Molecular Diagnostics : JMD

|May 26, 2023

Summary

We developed MICon, a novel contamination detection model for next-generation sequencing (NGS) panels. MICon accurately identifies sample contamination using microhaplotype variant allele frequencies, crucial for oncology testing.

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Area of Science:

Genomics and Bioinformatics
Molecular Diagnostics
Cancer Research

Background:

Advancements in sequencing technology increase data output and reduce costs, enabling efficient multiplexing.
Pooled sequencing strategies, while cost-effective, elevate the risk of sample contamination.
Sample contamination in oncology testing can lead to misidentification of critical variants or reporting of false positives, especially with low-frequency variants.

Purpose of the Study:

To address the challenge of sample contamination detection in small next-generation sequencing (NGS) panels.
To develop a novel contamination detection model that performs accurately on limited variant data.
To prevent the clinical reporting of contaminated samples in targeted NGS panels.

Main Methods:

Development of MICon (Microhaplotype Contamination detection), a new contamination detection model.
Utilizes microhaplotype site variant allele frequencies for contamination assessment.
Validated on a heterogeneous hold-out test cohort of 210 samples.

Main Results:

MICon demonstrated state-of-the-art performance in detecting sample contamination.
Achieved an area under the receiver-operating characteristic curve of 0.995 in the test cohort.
Outperforms existing tools on smaller gene panels where variant candidates are limited.

Conclusions:

MICon is an effective tool for identifying sample contamination in small NGS panels.
The model's high accuracy is critical for reliable variant calling in clinical oncology.
Microhaplotype analysis provides a robust method for contamination detection in targeted sequencing.