Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach

Abstract Background The widespread use of next-generation sequencing has identified an important role for somatic mosaicism in many diseases. However, detecting low-level mosaic variants from next-generation sequencing data remains challenging. Results Here, we present a method for Position-Based Va...

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Main Authors:	Jeffrey N. Dudley, Celine S. Hong, Marwan A. Hawari, Jasmine Shwetar, Julie C. Sapp, Justin Lack, Henoke Shiferaw, NISC Comparative Sequencing Program, Jennifer J. Johnston, Leslie G. Biesecker
Format:	Article
Language:	English
Published:	BMC 2021-04-01
Series:	BMC Bioinformatics
Subjects:	Mosaic variants Prediction of mosaic variants Somatic overgrowth disorder
Online Access:	https://doi.org/10.1186/s12859-021-04090-y

id	doaj-ef209cf7fefe41e7888cace1d84a2c3f
record_format	Article
spelling	doaj-ef209cf7fefe41e7888cace1d84a2c3f2021-04-11T11:44:59ZengBMCBMC Bioinformatics1471-21052021-04-0122111710.1186/s12859-021-04090-yLow-level variant calling for non-matched samples using a position-based and nucleotide-specific approachJeffrey N. Dudley0Celine S. Hong1Marwan A. Hawari2Jasmine Shwetar3Julie C. Sapp4Justin Lack5Henoke Shiferaw6NISC Comparative Sequencing Program7Jennifer J. Johnston8Leslie G. Biesecker9National Human Genome Research Institute, National Institutes of HealthNational Human Genome Research Institute, National Institutes of HealthNational Human Genome Research Institute, National Institutes of HealthNational Human Genome Research Institute, National Institutes of HealthNational Human Genome Research Institute, National Institutes of HealthNIAID Collaborative Bioinformatics Resource, National Institutes of Allergy and Infectious Diseases, National Institutes of HealthNational Human Genome Research Institute, National Institutes of HealthNIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of HealthNational Human Genome Research Institute, National Institutes of HealthNational Human Genome Research Institute, National Institutes of HealthAbstract Background The widespread use of next-generation sequencing has identified an important role for somatic mosaicism in many diseases. However, detecting low-level mosaic variants from next-generation sequencing data remains challenging. Results Here, we present a method for Position-Based Variant Identification (PBVI) that uses empirically-derived distributions of alternate nucleotides from a control dataset. We modeled this approach on 11 segmental overgrowth genes. We show that this method improves detection of single nucleotide mosaic variants of 0.01–0.05 variant allele fraction compared to other low-level variant callers. At depths of 600 × and 1200 ×, we observed > 85% and > 95% sensitivity, respectively. In a cohort of 26 individuals with somatic overgrowth disorders PBVI showed improved signal to noise, identifying pathogenic variants in 17 individuals. Conclusion PBVI can facilitate identification of low-level mosaic variants thus increasing the utility of next-generation sequencing data for research and diagnostic purposes.https://doi.org/10.1186/s12859-021-04090-yMosaic variantsPrediction of mosaic variantsSomatic overgrowth disorder
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Jeffrey N. Dudley Celine S. Hong Marwan A. Hawari Jasmine Shwetar Julie C. Sapp Justin Lack Henoke Shiferaw NISC Comparative Sequencing Program Jennifer J. Johnston Leslie G. Biesecker
spellingShingle	Jeffrey N. Dudley Celine S. Hong Marwan A. Hawari Jasmine Shwetar Julie C. Sapp Justin Lack Henoke Shiferaw NISC Comparative Sequencing Program Jennifer J. Johnston Leslie G. Biesecker Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach BMC Bioinformatics Mosaic variants Prediction of mosaic variants Somatic overgrowth disorder
author_facet	Jeffrey N. Dudley Celine S. Hong Marwan A. Hawari Jasmine Shwetar Julie C. Sapp Justin Lack Henoke Shiferaw NISC Comparative Sequencing Program Jennifer J. Johnston Leslie G. Biesecker
author_sort	Jeffrey N. Dudley
title	Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach
title_short	Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach
title_full	Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach
title_fullStr	Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach
title_full_unstemmed	Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach
title_sort	low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach
publisher	BMC
series	BMC Bioinformatics
issn	1471-2105
publishDate	2021-04-01
description	Abstract Background The widespread use of next-generation sequencing has identified an important role for somatic mosaicism in many diseases. However, detecting low-level mosaic variants from next-generation sequencing data remains challenging. Results Here, we present a method for Position-Based Variant Identification (PBVI) that uses empirically-derived distributions of alternate nucleotides from a control dataset. We modeled this approach on 11 segmental overgrowth genes. We show that this method improves detection of single nucleotide mosaic variants of 0.01–0.05 variant allele fraction compared to other low-level variant callers. At depths of 600 × and 1200 ×, we observed > 85% and > 95% sensitivity, respectively. In a cohort of 26 individuals with somatic overgrowth disorders PBVI showed improved signal to noise, identifying pathogenic variants in 17 individuals. Conclusion PBVI can facilitate identification of low-level mosaic variants thus increasing the utility of next-generation sequencing data for research and diagnostic purposes.
topic	Mosaic variants Prediction of mosaic variants Somatic overgrowth disorder
url	https://doi.org/10.1186/s12859-021-04090-y
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Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach

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