Introduction

Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction.Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance.We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS CONTACT: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.comSupplementary data are available at Bioinformatics online.

Publications

  1. Predicting regulatory variants with composite statistic.
    Cite this
    Li MJ, Pan Z, Liu Z, Wu J, Wang P, Zhu Y, Xu F, Xia Z, Sham PC, Kocher JP, Li M, Liu JS, Wang J, 2016-09-01 - Bioinformatics (Oxford, England)

Credits

  1. Mulin Jun Li
    Developer

    Department of Statistics, Harvard University, United States of America

  2. Zhicheng Pan
    Developer

  3. Zipeng Liu
    Developer

  4. Jiexing Wu
    Developer

    Department of Statistics, Harvard University, United States of America

  5. Panwen Wang
    Developer

  6. Yun Zhu
    Developer

  7. Feng Xu
    Developer

  8. Zhengyuan Xia
    Developer

  9. Pak Chung Sham
    Developer

  10. Jean-Pierre A Kocher
    Developer

    Department of Health Sciences Research, Center for Individualized Medicine, United States of America

  11. Miaoxin Li
    Developer

    Centre for Genomic Sciences, Department of Psychiatry, China

  12. Jun S Liu
    Developer

    Department of Statistics, Harvard University, United States of America

  13. Junwen Wang
    Investigator

    Centre for Genomic Sciences, Department of Health Sciences Research, United States of America

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Summary
AccessionBT000450
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesPerl
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByJunwen Wang