Sequence Composition Bias Analysis

Sequence Composition Bias Analysis is a methodological approach in bioinformatics that identifies systematic deviations in nucleotide or amino acid distribution across sequencing reads, distinguishing itself by assessing non-randomness in sequence composition to detect technical artifacts or biological anomalies in high-throughput sequencing data.

88

Publications

15.3K

Citations

357

Authors

174

Institutions

Publications per year

2017–2026

14

Authors

357

  1. CN
    Chad Nusbaum

    Broad Institute, United States

    3 publications · 2.5K citations

    3

    h-index

  2. MR
    Michael Ross

    UCLA Medical Center, United States

    3 publications · 2.5K citations

    3

    h-index

  3. CR
    Carsten Russ

    Broad Institute, United States

    3 publications · 2.5K citations

    3

    h-index

  4. FG
    Feng‐Biao Guo

    University of Electronic Science and Technology of China, China

    2 publications · 60 citations

    2

    h-index

  5. MG
    Morgan G. I. Langille

    Dalhousie University, Canada

    2 publications · 676 citations

    2

    h-index

1–5 of 357

Venues

  1. Bioinformatics

    8 publications · 3.7K citations

    8

    h-index

  2. PLoS ONE

    6 publications · 337 citations

    6

    h-index

  3. Nucleic Acids Research

    5 publications · 2K citations

    5

    h-index

  4. Genome Research

    GR · 4 publications · 388 citations

    4

    h-index

  5. Molecular Biology and Evolution

    MBE · 4 publications · 398 citations

    4

    h-index

1–5