Structural Variant Filtering
Structural Variant Filtering is a computational method that distinguishes high-confidence structural variants from false or low-quality calls by applying specific criteria such as read depth, mapping quality, and population frequency within genomic sequencing data analysis.
17
Publications
1.1K
Citations
134
Authors
86
Institutions
Publications per year
2017–2026
14
Parents
17
Detection of mosaic and population-level structural variants with Sniffles2
Moritz Smolka, Luis F. Paulin, Christopher M. Grochowski et al. · Nature Biotechnology · 2024 · 439 citations · Full text
Samplot: a platform for structural variant visual validation and automated filtering
Jonathan R. Belyeu, Murad Chowdhury, Joseph Brown et al. · Genome biology · 2021 · 146 citations · Full text
SVision: a deep learning approach to resolve complex structural variants
Jiadong Lin, Songbo Wang, Peter A. Audano et al. · Nature Methods · 2022 · 110 citations · Full text
1–5 of 17
134
5
h-index
3
h-index
3
h-index
3
h-index
3
h-index
1–5 of 134
86
University of Utah
Salt Lake City, United States
5 h-index
13 publications
931 citations
Baylor College of Medicine
Houston, United States
5 h-index
14 publications
3.8K citations
Stanford University
Stanford, United States
3 h-index
12 publications
576 citations
University of Colorado Boulder
Boulder, United States
3 h-index
6 publications
346 citations
Cold Spring Harbor Laboratory
Cold Spring Harbor, United States
3 h-index
9 publications
321 citations
New York Genome Center
New York, United States
3 h-index
15 publications
843 citations

Rice University
Houston, United States
3 h-index
3 publications
557 citations
Universidad Nacional Autónoma de México
Mexico City, Mexico
3 h-index
4 publications
212 citations
1–8 of 86
3 publications · 157 citations
3
h-index
1 publications · 24 citations
2
h-index
1 publications · 22 citations
1
h-index
1 publications · 37 citations
1
h-index
1 publications · 439 citations
1
h-index
1–5