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NBPSeq specifications


Unique identifier OMICS_08860
Name NBPSeq
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 2.0
Computer skills Advanced
Version 0.3.0
Stability Stable
qvalue, splines, R(≥3.00)
Source code URL https://cran.r-project.org/src/contrib/NBPSeq_0.3.0.tar.gz
Maintained Yes




No version available



  • person_outline Yanming Di
  • person_outline Jeff Chang
  • person_outline Jason Cumbie

Publication for NBPSeq

NBPSeq citations


Differential toxicity and venom gland gene expression in Centruroides vittatus

PLoS One
PMCID: 5627916
PMID: 28976980
DOI: 10.1371/journal.pone.0184695

[…] is important for proper interpretation of transcriptomic data. EdgeR was chosen as an analysis platform due to small sample size and performance compared other analysis programs (e.g., DEseq, EBseq, NBPseq)(42). This strengthened our selection strategy of a ≥2.0 biological threshold, providing the basis for selecting genes (transcripts) of interest for further analysis.Considering there was one b […]


Evaluation of logistic regression models and effect of covariates for case–control study in RNA Seq analysis

BMC Bioinformatics
PMCID: 5294900
PMID: 28166718
DOI: 10.1186/s12859-017-1498-y

[…] NA-Seq analysis methods have been evaluated in different settings including multi-group study designs [, –]. Soneson et al. [] compared performance of RNA-Seq analysis tools (edgeR, DESeq, baySeq [], NBPSeq [], TSPM [], EBSeq [], NOIseq [], SAMseq [], ShrinkSeq [] and limma []) using real and simulated data sets. They reported that when sample size is small, the results should be cautiously interp […]


Combinatory annotation of cell membrane receptors and signalling pathways of Bombyx mori prothoracic glands

Sci Data
PMCID: 5004587
PMID: 27576083
DOI: 10.1038/sdata.2016.73

[…] 51. The resulting gene counts table was subjected to differential expression analysis for the contrasts day 0, 5th instar versus day 6, 5th instar using the Bioconductor packages DESeq, edgeR, limma, NBPSeq, NOISeq and baySeq. To combine the statistical significance from multiple algorithms so as to optimize the trade-off between true positives and false hits, we applied the PANDORA weighted p-val […]


Differential Expression of Genes Involved in Host Recognition, Attachment, and Degradation in the Mycoparasite Tolypocladium ophioglossoides

PMCID: 4777134
PMID: 26801645
DOI: 10.1534/g3.116.027045

[…] technical replicates was near Poisson distribution, suggesting it was safe to combine the counts from the technical replicates. For assessing differential expressed genes (DEG), the software package NBPSeq () in R () was used to fit negative binomial regression models to RNA read counts where one of the regression coefficients corresponds to the log (base2) fold change between two treatments, for […]


Identifying stably expressed genes from multiple RNA Seq data sets

PMCID: 5178351
PMID: 28028467
DOI: 10.7717/peerj.2791

[…] de here is that the median fold change between normalized relative frequencies in two samples should be 1. In other words, this normalization method assumes that the majority of genes are not DE. The NBPSeq package () has an inbuilt function for this procedure and it will be used for count normalization in this paper. With the estimates from , we see that the median fold change in normalized relat […]


RNA Seq analysis of resistant and susceptible potato varieties during the early stages of potato virus Y infection

BMC Genomics
PMCID: 4475319
PMID: 26091899
DOI: 10.1186/s12864-015-1666-2

[…] Changes in transcript expression were analyzed with either the Cuffdiff program from Cufflinks [] or NBPSeq []. These programs were chosen because they use different ways to model the negative binomial dispersion parameter [, , ]. Pairwise comparisons were made between PVY-inoculated vs. mock-inocula […]


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NBPSeq institution(s)
Oregon State University, Corvallis, OR, USA
NBPSeq funding source(s)
Supported in part by startup funds from OSU and the National Research Initiative Competitive Grant no. 2008-35600-18783 from the USDA's National Institute of Food and Agriculture, Microbial Functional Genomics Program; and by Computational and Genome Biology Initiative Fellowships from Oregon State University.

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