Introduction

We propose a parametric model, HiCNorm, to remove systematic biases in the raw Hi-C contact maps, resulting in a simple, fast, yet accurate normalization procedure. Compared with the existing Hi-C normalization method developed by Yaffe and Tanay, HiCNorm has fewer parameters, runs >1000 times faster and achieves higher reproducibility.Freely available on the web at: http://www.people.fas.harvard.edu/∼junliu/HiCNorm/.jliu@stat.harvard.eduSupplementary data are available at Bioinformatics online.

Publications

  1. HiCNorm: removing biases in Hi-C data via Poisson regression.
    Cite this
    Hu M, Deng K, Selvaraj S, Qin Z, Ren B, Liu JS, 2012-12-01 - Bioinformatics (Oxford, England)

Credits

  1. Ming Hu
    Developer

    Department of Statistics, Harvard University, United States of America

  2. Ke Deng
    Developer

  3. Siddarth Selvaraj
    Developer

  4. Zhaohui Qin
    Developer

  5. Bing Ren
    Developer

  6. Jun S Liu
    Investigator

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Summary
AccessionBT007014
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByJun S Liu