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

Authors

134

  1. FJ
    Fritz J. Sedlazeck

    Baylor College of Medicine, United States

    5 publications · 617 citations

    5

    h-index

  2. MC
    Murad Chowdhury

    University of Colorado Boulder, United States

    3 publications · 173 citations

    3

    h-index

  3. AR
    Aaron R. Quinlan

    University of Utah, United States

    3 publications · 173 citations

    3

    h-index

  4. RM
    Ryan M. Layer

    University of Utah, United States

    3 publications · 173 citations

    3

    h-index

  5. BS
    Brent S. Pedersen

    University of Utah, United States

    3 publications · 173 citations

    3

    h-index

1–5 of 134

Venues

  1. Nature Methods

    3 publications · 157 citations

    3

    h-index

  2. PLoS ONE

    1 publications · 24 citations

    2

    h-index

  3. Bioinformatics

    1 publications · 22 citations

    1

    h-index

  4. Nature Communications

    1 publications · 37 citations

    1

    h-index

  5. Nature Biotechnology

    1 publications · 439 citations

    1

    h-index

1–5