Low-level variant calling for non-matched samples using a position-based and nucleotide-specific approach.
BMC Bioinformatics
; 22(1): 181, 2021 Apr 08.
Article
in En
| MEDLINE
| ID: mdl-33832433
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
High-Throughput Nucleotide Sequencing
/
Nucleotides
Type of study:
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
BMC Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2021
Type:
Article
Affiliation country:
United States