DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
Open accessAbstract
Summary: High-throughput RNA sequencing (RNA-seq) is rapidly emerging as a major quantitative transcriptome profiling platform. Here, we present DEGseq, an R package to identify differentially expressed genes or isoforms for RNA-seq data from different samples. In this package, we integrated three existing methods, and introduced two novel methods based on MA-plot to detect and visualize gene expression difference. Availability: The R package and a quick-start vignette is available at http://bioinfo.au.tsinghua.edu.cn/software/degseq Contact: xwwang@tsinghua.edu.cn; zhangxg@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In small-scale studies with no or few replicates, simpler methods based on the Poisson distribution, such as DEGseq, or on empirical distributions, such as NOISeq, can be an alternative.
“In such cases, simpler methods based on the Poisson distribution, such as DEGseq [62], or on empirical distributions (NOISeq [10]) can be an alternative”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [62]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · see citing works
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785 · see citing works
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568 · see citing works
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692 · see citing works
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
Self-citationYang, Y., Dudoit, S., Luu, P., Lin, D., Peng, V., Ngai, J., & Speed, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.4.e15 · Cited by 3,722 · see citing works
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771 · see citing works
Moderated statistical tests for assessing differences in tag abundance
Robinson, M. D., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm453 · Cited by 856 · see citing works
Statistical inferences for isoform expression in RNA-Seq
Jiang, H., & Wong, W. H. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp113 · Cited by 453 · see citing works
Bloom, J. S., Khan, Z., Kruglyak, L., Singh, M., & Caudy, A. A. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-221 · Cited by 178 · see citing works
samr: SAM: Significance Analysis of Microarrays
Tibshirani, R., Seo, M. J., Chu, G., et al. (2005) · doi:10.32614/cran.package.samr · Cited by 20 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Differential analysis of gene regulation at transcript resolution with RNA-seq
Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013) · Nature Biotechnology · doi:10.1038/nbt.2450 · Cited by 3,492 · see citing works
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Self-citationSperm tsRNAs contribute to intergenerational inheritance of an acquired metabolic disorder
Chen, Q., Yan, M., Cao, Z., Li, X., Zhang, Y., Shi, J., Feng, G.-H., Peng, H., Zhang, X., Zhang, Y., et al. (2016) · Science · doi:10.1126/science.aad7977 · Cited by 1,470 · see citing works
Self-citationCRISPR Inversion of CTCF Sites Alters Genome Topology and Enhancer/Promoter Function
Guo, Y., Xu, Q., Canzio, D., Shou, J., Li, J., Gorkin, D. U., Jung, I., Wu, H., Zhai, Y., Tang, Y., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.038 · Cited by 958 · see citing works
Computational methods for transcriptome annotation and quantification using RNA-seq
Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C. (2011) · Nature Methods · doi:10.1038/nmeth.1613 · Cited by 1,042 · see citing works
Schurch, N. J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G. G., Owen-Hughes, T., et al. (2016) · RNA · doi:10.1261/rna.053959.115 · Cited by 966 · see citing works
Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924 · see citing works
A Recently Evolved Transcriptional Network Controls Biofilm Development in Candida albicans
Nobile, C. J., Fox, E. P., Nett, J. E., Sorrells, T. R., Mitrovich, Q. M., Hernday, A. D., Tuch, B. B., Andes, D. R., & Johnson, A. D. (2012) · Cell · doi:10.1016/j.cell.2011.10.048 · Cited by 711 · see citing works
Direct Conversion of Fibroblasts to Neurons by Reprogramming PTB-Regulated MicroRNA Circuits
Xue, Y., Ouyang, K., Huang, J., Zhou, Y., Ouyang, H., Li, H., Wang, G., Wu, Q., Wei, C., Bi, Y., et al. (2013) · Cell · doi:10.1016/j.cell.2012.11.045 · Cited by 562 · see citing works
Miller, R., Wu, G., Deshpande, R. R., Vieler, A., Gärtner, K., Li, X., Moellering, E. R., Zäuner, S., Cornish, A. J., Liu, B., et al. (2010) · PLANT PHYSIOLOGY · doi:10.1104/pp.110.165159 · Cited by 511 · see citing works
Dong, Z.-Y., Zhang, J.-T., Liu, S.-Y., Su, J., Zhang, C., Xie, Z., Zhou, Q., Tu, H.-Y., Xu, C.-R., Yan, L.-X., et al. (2017) · OncoImmunology · doi:10.1080/2162402x.2017.1356145 · Cited by 440 · see citing works
Normalization, testing, and false discovery rate estimation for RNA-sequencing data
Li, J., Witten, D. M., Johnstone, I. M., & Tibshirani, R. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr031 · Cited by 369 · see citing works
Zhang, J. A., Mortazavi, A., Williams, B. A., Wold, B. J., & Rothenberg, E. V. (2012) · Cell · doi:10.1016/j.cell.2012.01.056 · Cited by 337 · see citing works
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Nature Precedings · doi:10.1038/npre.2010.4282.1 · Cited by 328 · see citing works
Aanes, H., Winata, C. L., Lin, C. H., Chen, J. P., Srinivasan, K. G., Lee, S. G. P., Lim, A. Y. M., Hajan, H. S., Collas, P., Bourque, G., et al. (2011) · Genome Research · doi:10.1101/gr.116012.110 · Cited by 285 · see citing works
Power analysis and sample size estimation for RNA-Seq differential expression
Ching, T., Huang, S., & Garmire, L. X. (2014) · RNA · doi:10.1261/rna.046011.114 · Cited by 263 · see citing works
Survey of the transcriptome of Aspergillus oryzae via massively parallel mRNA sequencing
Wang, B., Guo, G., Wang, C., Lin, Y., Wang, X., Zhao, M., Guo, Y., He, M., Zhang, Y., & Pan, L. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq256 · Cited by 227 · see citing works
Self-citationChen, D., Yang, X., Yang, J., Lai, G., Yong, T., Tang, X., Shuai, O., Zhou, G., Xie, Y., & Wu, Q. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00403 · Cited by 227 · see citing works
The solute carrier SLC35F2 enables YM155-mediated DNA damage toxicity
Winter, G. E., Radic, B., Mayor-Ruiz, C., Blomen, V. A., Trefzer, C., Kandasamy, R. K., Huber, K. V. M., Gridling, M., Chen, D., Klampfl, T., et al. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1590 · Cited by 175 · see citing works
Tao, X., Gu, Y.-H., Wang, H.-Y., Zheng, W., Li, X., Zhao, C.-W., & Zhang, Y.-Z. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036234 · Cited by 168 · see citing works
Liu, L., You, Z., Yu, H., Zhou, L., Zhao, H., Yan, X., Li, D., Wang, B., Zhu, L., Xu, Y., et al. (2017) · Nature Materials · doi:10.1038/nmat5024 · Cited by 163 · see citing works
Determining sequencing depth in a single-cell RNA-seq experiment
Zhang, M. J., Ntranos, V., & Tse, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14482-y · Cited by 112 · see citing works
RNA‐seq Data: Challenges in and Recommendations for Experimental Design and Analysis
Williams, A. G., Thomas, S., Wyman, S. K., & Holloway, A. K. (2014) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg1113s83 · Cited by 93 · see citing works
Zhang, H., Du, H., & Xu, Y. (2021) · Applied and Environmental Microbiology · doi:10.1128/aem.02992-20 · Cited by 92 · see citing works
Potent effect of the MDM2 inhibitor AMG232 on suppression of glioblastoma stem cells
Her, N.-G., Oh, J.-W., Oh, Y. J., Han, S., Cho, H. J., Lee, Y., Ryu, G. H., & Nam, D.-H. (2018) · Cell Death & Disease · doi:10.1038/s41419-018-0825-1 · Cited by 68 · see citing works
Yin, S., Wang, Y., Liu, N., Yang, M., Hu, Y., Li, X., Fu, Y., Luo, M., Sun, J., & Yang, X. (2019) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2019.109535 · Cited by 51 · see citing works
Choi, J., Lee, K., Ingvarsdottir, K., Bonasio, R., Saraf, A., Florens, L., Washburn, M. P., Tadros, S., Green, M. R., & Busino, L. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0084-5 · Cited by 50 · see citing works
Metabolic response of Clostridium ljungdahlii to oxygen exposure
Whitham, J. M., Tirado-Acevedo, O., Chinn, M. S., Pawlak, J. J., & Grunden, A. M. (2015) · Applied and Environmental Microbiology · doi:10.1128/aem.02491-15 · Cited by 47 · see citing works
Shen, X., Bai, X., Xu, J., Zhou, M., Xu, H., Nie, Q., Lu, X., & Zhang, X. (2016) · Molecular Biology Reports · doi:10.1007/s11033-016-4033-8 · Cited by 39 · see citing works
Self-citationNitric oxide controls shoot meristem activity via regulation of DNA methylation
Zeng, J., Zhao, X., Liang, Z., Hidalgo, I., Gebert, M., Fan, P., Wenzl, C., Gornik, S. G., & Lohmann, J. U. (2023) · Nature Communications · doi:10.1038/s41467-023-43705-1 · Cited by 36 · see citing works
Das, S., Rai, A., Merchant, M. L., Cave, M. C., & Rai, S. N. (2021) · Genes · doi:10.3390/genes12121947 · Cited by 35 · see citing works
Transcriptomic analysis reveals mechanisms for the different drought tolerance of sweet potatoes
Liu, E., Xu, L., Luo, Z., Li, Z., Zhou, G., Gao, H., Fang, F., Tang, J., Zhao, Y., Zhou, Z., et al. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1136709 · Cited by 25 · see citing works
Zhang, H., Xu, W., Chen, H., Chen, J., Liu, X., Chen, X., & Yang, S. (2021) · BMC Genomic Data · doi:10.1186/s12863-021-00989-w · Cited by 15 · see citing works
- Authors
- Wang, L., Feng, Z., Wang, X., Wang, X., & Zhang, X.
- Affiliations
- Jilin University (CN)
- 1 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua, University, Beijing 100084 and 2 College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-10-24
- Volume
- 26
- Issue
- 1
- Pages
- 136-138
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2119034410
- Cited by
- 4,553
- References
- 13
- Citations by year
- 2026: 200 · 2025: 304 · 2024: 394 · 2023: 382 · 2022: 464 · 2021: 563 · 2020: 511 · 2019: 369 · 2018: 273 · 2017: 259 · 2016: 212 · 2015: 203 · 2014: 136 · 2013: 111 · 2012: 90
- FWCI
- 22.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (60905013, 60721003)
- Southeast University
- State Key Laboratory of Bioelectronics
- National Key Research and Development Program of China (2004CB518605, 60721003)
- Open Research Fund of State Key Laboratory of Bioelectronics
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- differentially expressed genes, RNA-seq, differential expression analysis, isoform expression, MA plot, gene expression visualization, R package, gene expression profiling
- MeSH terms
- Algorithms, Base Sequence, Molecular Sequence Data, Programming Languages, RNA, Software, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
