Detection of mosaic and population-level structural variants with Sniffles2

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…

• UD
  U.S. Department of Health and Human Services
• NI
  National Institutes of Health

  Awards: 1ZIANS003154, 1U01HG011758-01, 1UG3NS132105-01, UM1HG008898, U01 AG058589, AG000538
• NI
  National Institute of General Medical Sciences

  Award: R01 GM132589
• NI
  National Institute of Neurological Disorders and Stroke

  Award: 1ZIANS003154