Detection of Mosaic and Population-Level Structural Variants With Sniffles2

Authors

Moritz Smolka
Luis F Paulin
Christopher M Grochowski
Dominic W Horner
Medhat Mahmoud
Sairam Behera
Ester Kalef-Ezra
Mira Gandhi
Karl Hong
Davut Pehlivan
Sonja W Scholz
Claudia M B Carvalho
Christos Proukakis
Fritz J Sedlazeck

Publication Date

10-1-2024

Journal

Nature Biotechnology

DOI

10.1038/s41587-023-02024-y

PMID

38168980

PMCID

PMC11217151

PubMedCentral® Posted Date

1-2-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Mosaicism, High-Throughput Nucleotide Sequencing, Genomic Structural Variation, Software, Sequence Analysis, DNA, Genome informatics, Genetics, Cancer, Software

Abstract

Calling structural variations (SVs) is technically challenging, but using long reads remains the most accurate way to identify complex genomic alterations. Here we present Sniffles2, which improves over current methods by implementing a repeat aware clustering coupled with a fast consensus sequence and coverage-adaptive filtering. Sniffles2 is 11.8 times faster and 29% more accurate than state-of-the-art SV callers across different coverages (5-50×), sequencing technologies (ONT and HiFi) and SV types. Furthermore, Sniffles2 solves the problem of family-level to population-level SV calling to produce fully genotyped VCF files. Across 11 probands, we accurately identified causative SVs around MECP2, including highly complex alleles with three overlapping SVs. Sniffles2 also enables the detection of mosaic SVs in bulk long-read data. As a result, we identified multiple mosaic SVs in brain tissue from a patient with multiple system atrophy. The identified SV showed a remarkable diversity within the cingulate cortex, impacting both genes involved in neuron function and repetitive elements.

Comments

This article has been corrected. See Nat Biotechnol. 2024 Jan 22;42(10):1616.

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