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Zero-Inflated Negative Binomial Algorithm ZINBA

A flexible statistical framework capable of identifying regions of enrichment across a wide variety of DNA-seq data types, enrichment patterns, and experimental conditions. ZINBA's flexibility in modeling background and enrichment regions with sets of covariates allows for the identification of enriched regions in difficult modeling conditions, such as in datasets with complex local CNVs or lacking a matching input control sample. ZINBA can identify both broad and sharp regions of enrichment, and we demonstrate this capability in differentiating RNA Pol II elongation status. In addition, the statistical framework used is applicable to both high signal-to-noise data such as from CTCF ChIP-seq, as well as to low signal-to-noise data such as from FAIRE-seq. ZINBA produces peak calls that are consistent with known biological patterns, and performs favorably relative to existing specialized methods over a broad range of signal patterns and data types.

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1 user review

1 user review

Fabien Pichon's avatar image Fabien Pichon's country flag

Fabien Pichon

Too many files to generate for each experiment (very time consuming!) for a result far from satisfying.
To balance my comment, I have to say that I used ZINBA on FAIRE-seq which is relatively more noisy compared to ChIP-seq.
But another black point is that there is almost no support for a lot issues and instabilities.
For FAIRE-seq I would prefer Fseq and for ChIP-seq I would prefer MACS.

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ZINBA classification

ZINBA specifications

Software type:
Framework/Library
Restrictions to use:
None
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU General Public License version 2.0
Version:
2.02.03
Maintained:
Yes

ZINBA distribution

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No versioning.

ZINBA support

Documentation

Credits

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Publications

Institution(s)

Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biology, Carolina Center for Genome Sciences, and Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Funding source(s)

This work was supported by the NIH Biostatistics Training Grant in Cancer Genomics and NIH grants Bayesian Approaches to Model Selection for Survival Data (GM70335), Inference with Missing Covariates in Regression Models (CA74015), and ENCODE grant U54 HG004563.

Link to literature

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