EBSeq statistics

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Citations per year

Number of citations per year for the bioinformatics software tool EBSeq

Tool usage distribution map

This map represents all the scientific publications referring to EBSeq per scientific context
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Associated diseases

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EBSeq specifications


Unique identifier OMICS_01307
Name EBSeq
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Input format CSV,XLS,XLSX
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License Artistic License version 2.0
Computer skills Advanced
Version 1.21.0
Stability Stable
gplots, testthat, R(>=3.0.0), blockmodeling
Maintained Yes




No version available



  • person_outline Christina Kendziorski
  • person_outline Ning Leng

Additional information


Publication for EBSeq

EBSeq citations


Identification of genes regulating ovary differentiation after pollination in hazel by comparative transcriptome analysis

BMC Plant Biol
PMCID: 5941469
PMID: 29739322
DOI: 10.1186/s12870-018-1296-3
call_split See protocol

[…] DGE profiles were compared in order to determine changes in gene expression during ovary differentiation and ovule growth in hazel. Based on the unigene expression results, DEGs were identified with EBseq [] by setting the threshold of fold change as ≥2.00 and posterior probability of equivalent expression (PPEE) as ≤0.05. KEGG pathway enrichment analysis of DGE data was carried by a BLAST search […]


NOTCH signaling specifies arterial type definitive hemogenic endothelium from human pluripotent stem cells

Nat Commun
PMCID: 5940870
PMID: 29739946
DOI: 10.1038/s41467-018-04134-7

[…] statistical environment (R core team, 2014) was used at all of the stages of downstream data analysis. The entire set of libraries was pre-normalized as a pool using median normalization routine from EBSeq package. EBSeq with 10 iterations was applied to call for differential expression. The EBSeq’s default procedure of filtering low-expressed genes was suppressed by setting the QtrmCut parameter […]


Complete genome sequence and the expression pattern of plasmids of the model ethanologen Zymomonas mobilis ZM4 and its xylose utilizing derivatives 8b and 2032

Biotechnol Biofuels
PMCID: 5930841
PMID: 29743953
DOI: 10.1186/s13068-018-1116-x

[…] es. The pool of gene length- and library size-normalized values (FPKM) was additionally quantile-normalized via Bioconductor “affy” package []. The differential expression testing was performed using EBSeq [], the Empirical Bayesian-based differential expression calling package. Genes with posterior probability of differential expression above 0.95 were subject to further filtering based on the qu […]


Characterization of the enhancer and promoter landscape of inflammatory bowel disease from human colon biopsies

Nat Commun
PMCID: 5916929
PMID: 29695774
DOI: 10.1038/s41467-018-03766-z

[…] med most strongly used in gut tissue.Fig. 3PCA of enhancer expression showed separation between inflamed samples (UCa and CDa) and Ctrl, but only minor separation between UCa and CDa (Fig. ). We used EBSeq to define four differentially expressed enhancer groups, analogous to the TSS groups defined above: shared CDa and UCa up/downregulated vs. Ctrl (IBDup and IBDdown), upregulated in CDa vs. UCa ( […]


Comparative transcriptome analysis to investigate the potential role of miRNAs in milk protein/fat quality

Sci Rep
PMCID: 5908868
PMID: 29674689
DOI: 10.1038/s41598-018-24727-y

[…] rthermore, HTSeq v 0.6.1p2 was used to count the number of uniquely aligned reads using Rfam and miRBase21. Differential expression analysis of miRNA expression profiles was quartile-normalized using EBSeq v 1.12.0. miRNAs with an abundance of greater than one CPM in at least three of the samples were defined as true known miRNAs. miRNAs uniquely expressed in one tissue were defined as period-spec […]


GKAP Acts as a Genetic Modulator of NMDAR Signaling to Govern Invasive Tumor Growth

Cancer Cell
PMCID: 5896248
PMID: 29606348
DOI: 10.1016/j.ccell.2018.02.011

[…] used along with custom R utilities. All RNA-seq analyses were conducted in the R Statistical Programming language (http://www.r-project.org/). Isoform-level differential analyses were performed using EBSeq (). Gene set enrichment analysis (GSEA) was carried out using the pre-ranked mode with default settings (). Enrichment maps were generated using the EM () plugin for Cytoscape (). Heatmaps were […]

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EBSeq institution(s)
Department of Statistics, University of Wisconsin, Madison, WI, USA; Morgridge Institute for Research, Madison, WI, USA; McArdle Laboratory for Cancer Research, Department of Oncology, University of Wisconsin, Madison, WI, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
EBSeq funding source(s)
Supported by NIH GM102756, NIH CA28954, NIEHS ES17400 and The Morgridge Institute for Research.

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