baySeq pipeline

baySeq specifications


Unique identifier OMICS_01299
Name baySeq
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0, GNU General Public License version 2.0
Computer skills Advanced
Version 2.12.0
Stability Stable
Requirements methods, GenomicRanges, abind, perm, parallel
Maintained Yes



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  • person_outline Thomas J. Hardcastle <>

Publication for baySeq

baySeq IN pipelines

PMCID: 4607515
PMID: 26338190
DOI: 10.1093/gbe/evv156

[…] with the first 9 bp and the final 40 bp trimmed before mapping to remove low and variable-quality bases. all subsequent statistical analyses were performed using r and bioconductor, especially the bayseq package for assessing differential expression (hardcastle and kelly 2010; r development core team 2014). the library scaling factors were calculated as the sum of nonzero gene expression […]

PMCID: 3299011
PMID: 22416242
DOI: 10.3389/fmicb.2012.00085

[…] analysis. the gene expression and normalization analysis was performed as previously described (mortazavi et al., 2008). genes were tested for differential expression using the bioconductor package bayseq (hardcastle and kelly, 2010) relative to the 0-min infection time point. the analysis was carried out for c. albicans and m. musculus genes individually., fuzzy c-means clustering (bezdek, […]

PMCID: 3353902
PMID: 22615914
DOI: 10.1371/journal.pone.0037118

[…] using the program pvclust in the r language [26], [27]., the proportion of differentially expressed (de) genes between samples was estimated via an empirical bayesian approach in the r program bayseq [28]. posterior likelihoods of differential expression were calculated for different sets of sample groupings (models) comparing the in situ source water samples to the control replicates […]

baySeq institution(s)
Department of Plant Sciences, University of Cambridge, Cambridge, UK
baySeq funding source(s)
Supported by European Research Council Advanced Investigator Grant ERC-2013-AdG 340642 – TRIBE.

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