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DEGseq specifications


Unique identifier OMICS_01305
Name DEGseq
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU Lesser General Public License version 3.0
Computer skills Advanced
Version 1.2.2
Stability Stable
qvalue, methods, stats, graphics, utils, ShortRead, grDevices, R(>=2.8.0), samr
Maintained Yes




No version available



  • person_outline Xuegong Zhang
  • person_outline Likun Wang
  • person_outline Xiaowo Wang

Publication for DEGseq

DEGseq citations


RNA Seq profiling of circular RNAs in human laryngeal squamous cell carcinomas

Mol Cancer
PMCID: 5930968
PMID: 29716593
DOI: 10.1186/s12943-018-0833-x

[…] ion mapped reads, including TopHat mapping and TopHat-Fusion mapping) [] was employed to calculate the expression level of individual circRNA. Differential expression between circRNAs was assessed by DEGseq algorithm. We defined the statistical criteria for selecting aberrant-expressed circRNA using a q-value < 0.05 with a fold change > 2.0 or < 0.5. […]


Maser: one stop platform for NGS big data from analysis to visualization

PMCID: 5905357
PMID: 29688385
DOI: 10.1093/database/bay027

[…] es the abundance of each transcript to test differentially expressed genes (DEGs), and visualizes the resultant outputs with the Cufflinks suite (). The latter pipeline, ‘Trinity, Bowtie, eXpress and DEGseq’, subsequently maps raw NGS reads to transcriptome assembly using Bowtie () to estimate the abundance of transcriptome using eXpress () and test DEGs with DEGseq (). In both pipelines, the resu […]


RNA seq analysis of lncRNA controlled developmental gene expression during puberty in goat and rat

BMC Genet
PMCID: 5879571
PMID: 29609543
DOI: 10.1186/s12863-018-0608-9

[…] Further analysis of RNA-seq data was performed using the statistical R package (ggplot2, DESeq, edgeR, and DEGSeq; R, Auckland, NZL), as well graphical representations, adopting multiple testing. SPSS 17.0 software package (SPSS, Chicago, IL, USA) was applied to analyze the qRT-PCR data. Significance of da […]


Identification of transcriptome signature for predicting clinical response to bevacizumab in recurrent glioblastoma

PMCID: 5943425
PMID: 29573206
DOI: 10.1002/cam4.1439

[…] Broad Institute ( Gene expression data were normalized using a “rank” method . The subtype of each sample was assigned using z‐score . DEGseq: Differentially expressed genes were identified using the R package “DEGseq” . Samples were divided into two groups according to clinical response to bevacizumab.ssGSEA: Single‐sample gene set […]


Transcriptomic Profiles of Brain Provide Insights into Molecular Mechanism of Feed Conversion Efficiency in Crucian Carp (Carassius auratus)

Int J Mol Sci
PMCID: 5877719
PMID: 29538345
DOI: 10.3390/ijms19030858
call_split See protocol

[…] t per kilobase of exon model per million mapped reads (FPKM) for each unigene between the two groups (Low_vs_High). Differentially expressed genes (DEGs) between the two groups were identified by the DEGseq package (samples with three biological replicates) applying the MA-plot-based method with Random Sampling model (MARS) method. In this study, DEGs with significant expression abundance between […]


c di GMP Regulates Various Phenotypes and Insecticidal Activity of Gram Positive Bacillus thuringiensis

Front Microbiol
PMCID: 5816809
PMID: 29487570
DOI: 10.3389/fmicb.2018.00045
call_split See protocol

[…] e number of reads mapped to each gene was recorded by R (Mortazavi et al., ) and normalized into RPKM (Reads Per Kilo bases per Million reads). The differentially expressed genes were recorded by the DEGseq package using the MARS (MA-plot-based method with Random Sampling model) method (Wang et al., ). We used FDR ≤0.001 and a ≥1.5-fold change (|log1.5 (Fold change) normalized| ≥1) as the threshol […]


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DEGseq institution(s)
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua, University, Beijing, China
DEGseq funding source(s)
Supported by National Natural Science Foundation of China (grant numbers 30625012, 60721003, 60905013); the National Basic Research Program of China (2004CB518605); Open Research Fund of State Key Laboratory of Bioelectronics, Southeast University of China.

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