Introduction

A common problem in genomics is to test for associations between two or more genomic features, typically represented as intervals interspersed across the genome. Existing methodologies can test for significant pairwise associations between two genomic intervals; however, they cannot test for associations involving multiple sets of intervals. This limits our ability to uncover more complex, yet biologically important associations between multiple sets of genomic features. We introduce GINOM (Genomic INterval Overlap Model), a new method that enables testing of significant associations between multiple genomic features. We demonstrate GINOM's ability to identify higher-order associations with both simulated and real data. In particular, we used GINOM to explore L1 retrotransposable element insertion bias in lung cancer and found a significant pairwise association between L1 insertions and heterochromatic marks. Unlike other methods, GINOM also detected an association between L1 insertions and gene bodies marked by a facultative heterochromatic mark, which could explain the observed bias for L1 insertions towards cancer-associated genes.

Publications

  1. GINOM: A statistical framework for assessing interval overlap of multiple genomic features.
    Cite this
    Bryner D, Criscione S, Leith A, Huynh Q, Huffer F, Neretti N, 2017-06-01 - PLoS Computational Biology

Credits

  1. Darshan Bryner
    Developer

    Naval Surface Warfare Center Panama City Division, Panama City, United States of America

  2. Stephen Criscione
    Developer

    Department of Molecular Biology, Cell Biology and Biochemistry, United States of America

  3. Andrew Leith
    Developer

    Center for Computational Molecular Biology, Brown University, United States of America

  4. Quyen Huynh
    Developer

    Institute for Brain and Neural Systems, Brown University, United States of America

  5. Fred Huffer
    Developer

    Department of Statistics, Florida State University, United States of America

  6. Nicola Neretti
    Investigator

    Center for Computational Molecular Biology, Brown University, United States of America

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Summary
AccessionBT006366
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByNicola Neretti