Read-depth Based Variant Detection

Read-Depth Based Variant Detection is a computational method for identifying structural variants in genomic sequences by analyzing the local deviations in sequencing read depth relative to a reference genome, distinguishing it from other variant detection approaches through its reliance on depth-of-coverage patterns rather than read-pair, split-read, or assembly-based signals.

50

Publications

5.7K

Citations

340

Authors

158

Institutions

Publications per year

2017–2026

28

Authors

340

  1. AR
    Aaron R. Quinlan

    University of Utah, United States

    4 publications · 2K citations

    4

    h-index

  2. BS
    Brent S. Pedersen

    University of Utah, United States

    3 publications · 245 citations

    3

    h-index

  3. RM
    Ryan M. Layer

    University of Utah, United States

    3 publications · 1.9K citations

    3

    h-index

  4. JD
    Junbo Duan

    Xi'an Jiaotong University, China

    2 publications · 108 citations

    3

    h-index

  5. XY
    Xiguo Yuan

    Xidian University, China

    2 publications · 92 citations

    3

    h-index

1–5 of 340

Venues

  1. Genome biology

    5 publications · 2.3K citations

    5

    h-index

  2. BMC Bioinformatics

    5 publications · 218 citations

    5

    h-index

  3. Nucleic Acids Research

    5 publications · 245 citations

    5

    h-index

  4. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 4 publications · 60 citations

    4

    h-index

  5. Bioinformatics

    3 publications · 789 citations

    3

    h-index

1–5