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This R package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. Tested on ChIP-Seq peaks and nucleosome positions.

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adeschen/consensusSeekeR

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consensusSeekeR: Detection of consensus regions inside a group of experiments using genomic positions and genomic ranges

R-CMD-check-bioc codecov License: Artistic-2.0

This R package compares multiple narrowPeak data from different experiments to extract common peak regions. The size of the analyzed region is adjustable, as well as the number of experiences in which a peak must be present to tag a potential region as a consensus region. If needed, the consensus regions can be extended to cover the entire regions of enclosed peaks.

Bioconductor Package

Bioconductor Time

consensusSeekeR is now an official package of Bioconductor. The current release can be directly downloaded from their website: Current release

Citing

If you use this package for a publication, we would ask you to cite the following:

Samb R, Khadraoui K, Belleau P, et al. (2015) Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. Statistical Applications in Genetics and Molecular Biology. Published online before print December 10, 2015. doi:10.1515/sagmb-2014-0098

Authors

Astrid Deschênes, Fabien Claude Lamaze, Pascal Belleau and Arnaud Droit

Maintainer

Astrid Deschênes

License

This package and the underlying consensusSeekeR code are distributed under the Artistic license 2.0. You are free to use and redistribute this software.

For more information on Artistic 2.0 License see http://opensource.org/licenses/Artistic-2.0

Bugs/Feature requests

If you have any bugs or feature requests, let us know. Thanks!

About

This R package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. Tested on ChIP-Seq peaks and nucleosome positions.

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