Detection of Mosaic and Population-level Structural Variants with Sniffles2

Overview

Journal Nat Biotechnol

Specialty Biotechnology

Date 2024 Jan 3

PMID 38168980

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

Affiliations

Soon will be listed here.

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.

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