Email updates

Keep up to date with the latest news and content from Genome Biology and BioMed Central.

Open Access Highly Accessed Method

ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions

Naim U Rashid1, Paul G Giresi2, Joseph G Ibrahim1, Wei Sun13* and Jason D Lieb2*

Author Affiliations

1 Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

2 Department of Biology, Carolina Center for Genome Sciences, and Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

3 Department of Genetics and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

For all author emails, please log on.

Genome Biology 2011, 12:R67  doi:10.1186/gb-2011-12-7-r67

Published: 25 July 2011

Additional files

Additional file 1:

Figures S1 to S13 and Table S1. Supplementary Figures S1 to S13, and Table S1, corresponding to parameter estimates from the ZINBA BIC-selected models.

Format: PDF Size: 1.4MB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 2:

Supplementary methods. Mathematical details of the ZINBA mixture regression algorithm, in addition to data access details.

Format: PDF Size: 224KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 3:

ZINBA version 2.0. A freeze of the ZINBA software as of 9 July 2011, which is included in the manuscript for archival purposes only. We recommend that users download ZINBA from our website [46] to ensure that the most up-to-date version is installed.

Format: GZ Size: 2.1MB Download file

Open Data