Cited by 424Gene expression and cancer classificationGenomics and Phylogenetic StudiesSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Comparison of software packages for detecting differential expression in RNA-seq studies

Open access

Seyednasrollah, F., Laiho, A., Elo, L. L.·2015·Briefings in Bioinformatics

DOI 10.1093/bib/bbt086PMC PMC4293378

Open this paper in CoChat — free account Open PDF

Abstract

RNA-sequencing (RNA-seq) has rapidly become a popular tool to characterize transcriptomes. A fundamental research problem in many RNA-seq studies is the identification of reliable molecular markers that show differential expression between distinct sample groups. Together with the growing popularity of RNA-seq, a number of data analysis methods and pipelines have already been developed for this task. Currently, however, there is no clear consensus about the best practices yet, which makes the choice of an appropriate method a daunting task especially for a basic user without a strong statistical or computational background. To assist the choice, we perform here a systematic comparison of eight widely used software packages and pipelines for detecting differential expression between sample groups in a practical research setting and provide general guidelines for choosing a robust pipeline. In general, our results demonstrate how the data analysis tool utilized can markedly affect the outcome of the data analysis, highlighting the importance of this choice.

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

Crossref · 10.1093/bib/bbt086

Cites

27 of 27 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
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
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
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Trapnell, C., Roberts, A., Goff, L. A., Pertea, G. M., Kim, D., Kelley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L., & Pachter, L. S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.016 · Cited by 12,145
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
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
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
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680
Differential expression in RNA-seq: A matter of depth
Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011) · Genome Research · doi:10.1101/gr.124321.111 · Cited by 1,632

Flagged first, then by citations

Show all 27 references

Sorted by citations

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
Self-citation
RNA sequencing: the teenage years
Stark, R., Grzelak, M., & Hadfield, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0150-2 · Cited by 2,041
Differential analysis of RNA-seq incorporating quantification uncertainty
Pimentel, H., Bray, N. L., Puente, S., Melsted, P., & Pachter, L. (2017) · Nature Methods · doi:10.1038/nmeth.4324 · Cited by 1,534
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
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
RNA-Seq differential expression analysis: An extended review and a software tool
Silva, J. C., Domingues, D. S., & Lopes, F. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0190152 · Cited by 676
MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing
Kalari, K. R., Nair, A., Bhavsar, J., O’Brien, D. R., Davila, J. I., Bockol, M. A., Nie, J. J., Tang, X., Baheti, S., Doughty, J. B., et al. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-224 · Cited by 423
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data
Zhang, Z. H., Jhaveri, D. J., Marshall, V. M., Bauer, D. C., Edson, J., Narayanan, R. K., Robinson, G. J., Lundberg, A. E., Bartlett, P. F., Wray, N. R., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0103207 · Cited by 373
Understanding sequencing data as compositions: an outlook and review
Quinn, T. P., Erb, I., Richardson, M. F., & Crowley, T. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty175 · Cited by 372
Exaggerated false positives by popular differential expression methods when analyzing human population samples
Li, Y., Ge, X., Peng, F., Li, W., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02648-4 · Cited by 303
Interpretation of differential gene expression results of RNA-seq data: review and integration
McDermaid, A., Monier, B., Zhao, J., Liu, B., & Ma, Q. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby067 · Cited by 344
Resolving host–pathogen interactions by dual RNA-seq
Westermann, A. J., Barquist, L., & Vogel, J. (2017) · PLoS Pathogens · doi:10.1371/journal.ppat.1006033 · Cited by 301
Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis
Corchete, L. A., Rojas, E. A., Alonso‐López, D., Rivas, J. D. L., Gutierrez, N. C., & Burguillo, F. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-76881-x · Cited by 266
A broken promise: microbiome differential abundance methods do not control the false discovery rate
Hawinkel, S., Mattiello, F., Bijnens, L., & Thas, O. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbx104 · Cited by 199
Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis
Finotello, F., & Camillo, B. D. (2014) · Briefings in Functional Genomics · doi:10.1093/bfgp/elu035 · Cited by 253
Trimming of sequence reads alters RNA-Seq gene expression estimates
Williams, C., Baccarella, A., Parrish, J. Z., & Kim, C. C. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0956-2 · Cited by 231
A basal stem cell signature identifies aggressive prostate cancer phenotypes
Smith, B. A., Sokolov, A., Uzunangelov, V., Baertsch, R., Newton, Y., Graim, K. S., Mathis, C., Cheng, D., Stuart, J. M., & Witte, O. N. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1518007112 · Cited by 196
Systematic evaluation of differential splicing tools for RNA-seq studies
Mehmood, A., Laiho, A., Venäläinen, M. S., McGlinchey, A. J., Wang, N., & Elo, L. L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz126 · Cited by 174
Self-citation
RaNA-Seq: interactive RNA-Seq analysis from FASTQ files to functional analysis
Prieto, C., & Barrios, D. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz854 · Cited by 169
ROTS: An R package for reproducibility-optimized statistical testing
Suomi, T., Seyednasrollah, F. S., Jaakkola, M. K., Faux, T., & Elo, L. L. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005562 · Cited by 156
Self-citation
Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage
Soneson, C., Matthes, K. L., Nowicka, M., Law, C. W., & Robinson, M. D. (2016) · Genome biology · doi:10.1186/s13059-015-0862-3 · Cited by 135
Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud
Griffith, M., Walker, J., Spies, N. C., Ainscough, B. J., & Griffith, O. L. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004393 · Cited by 134
The Impact of Normalization Methods on RNA-Seq Data Analysis
Zyprych-Walczak, J., Szabelska‐Beręsewicz, A., Handschuh, L., Górczak, K., Klamecka, K., Figlerowicz, M., & Siatkowski, I. (2015) · BioMed Research International · doi:10.1155/2015/621690 · Cited by 125
Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size
Lamarre, S., Frasse, P., Zouine, M., Labourdette, D., Sainderichin, E., Hu, G., Berre, V. L., Bouzayen, M., & Maza, É. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.00108 · Cited by 106
Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow
Buschmann, D., Haberberger, A., Kirchner, B., Spornraft, M., Riedmaier, I., Schelling, G., & Pfaffl, M. W. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw545 · Cited by 102
Analytical tools and current challenges in the modern era of neuroepigenomics
Maze, I., Shen, L., Zhang, B., Garcia, B. A., Shao, N., Mitchell, A., Sun, H., Akbarian, S., Allis, C. D., & Nestler, E. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3816 · Cited by 100
An evaluation of RNA-seq differential analysis methods
Li, D., Zand, M. S., Dye, T., Goniewicz, M. L., Rahman, I., & Xie, Z. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264246 · Cited by 96
Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods
Quinn, T. P., Crowley, T. M., & Richardson, M. F. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2261-8 · Cited by 96
Integrative Differential Expression Analysis for Multiple EXperiments (IDEAMEX): A Web Server Tool for Integrated RNA-Seq Data Analysis
Jacinto, V. J., Sánchez‐Flores, A., & Vega‐Alvarado, L. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00279 · Cited by 92
ROTS: reproducible RNA-seq biomarker detector—prognostic markers for clear cell renal cell cancer
Seyednasrollah, F., Rantanen, K., Jaakkola, P., & Elo, L. L. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv806 · Cited by 75
Self-citation
A comparison of per sample global scaling and per gene normalization methods for differential expression analysis of RNA-seq data
Li, X., Brock, G. N., Rouchka, E. C., Cooper, N. G. F., Wu, D., O’Toole, T. E., Gill, R. S., Eteleeb, A. M., O’Brien, L., & Rai, S. N. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176185 · Cited by 89
Comparison of methods to detect differentially expressed genes between single-cell populations
Jaakkola, M. K., Seyednasrollah, F., Mehmood, A., & Elo, L. L. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw057 · Cited by 86
Self-citation
Evaluation of methods for differential expression analysis on multi-group RNA-seq count data
Tang, M., Sun, J., Shimizu, K., & Kadota, K. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0794-7 · Cited by 83
Robustness of differential gene expression analysis of RNA-seq
Stupnikov, A., McInerney, C. E., Savage, K. I., McIntosh, S., Emmert‐Streib, F., Kennedy, R. D., Salto‐Tellez, M., Prise, K. M., & McArt, D. G. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.040 · Cited by 84
Analysis of Whole Transcriptome Sequencing Data: Workflow and Software
Yang, I. S., & Kim, S. (2015) · Genomics & Informatics · doi:10.5808/gi.2015.13.4.119 · Cited by 84
Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis
Spies, D., & Ciaudo, C. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2015.08.004 · Cited by 86
Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq
Williams, C., Baccarella, A., Parrish, J. Z., & Kim, C. C. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1457-z · Cited by 83
The five pillars of computational reproducibility: bioinformatics and beyond
Ziemann, M., Poulain, P., & Bora, A. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad375 · Cited by 106
Variation in the transcriptome of different ecotypes of Arabidopsis thaliana reveals signatures of oxidative stress in plant responses to spaceflight
Choi, W., Barker, R. J., Kim, S., Swanson, S. J., & Gilroy, S. (2019) · American Journal of Botany · doi:10.1002/ajb2.1223 · Cited by 75
Navigating the Rapids: The Development of Regulated Next-Generation Sequencing-Based Clinical Trial Assays and Companion Diagnostics
Pant, S., WEINER, R. S., & Marton, M. J. (2014) · Frontiers in Oncology · doi:10.3389/fonc.2014.00078 · Cited by 78
standR: spatial transcriptomic analysis for GeoMx DSP data
Liu, N., Bhuva, D. D., Mohamed, A., Bokelund, M., Kulasinghe, A., Tan, C. W., & Davis, M. J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1026 · Cited by 76
Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance
Baccarella, A., Williams, C. R., Parrish, J. Z., & Kim, C. C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2445-2 · Cited by 72
Differential gene expression according to race and host plant in the pea aphid
Eyres, I., Jaquiéry, J., Sugio, A., Duvaux, L., Gharbi, K., Zhou, J., Legeai, F., Nelson, M., Simon, J., Smadja, C. M., et al. (2016) · Molecular Ecology · doi:10.1111/mec.13771 · Cited by 73
The Omics Dashboard for interactive exploration of gene-expression data
Paley, S., Parker, K., Spaulding, A., Tomb, J.-F., O’Maille, P. E., & Karp, P. D. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx910 · Cited by 71
Mutant p53 as a therapeutic target for the treatment of triple-negative breast cancer: Preclinical investigation with the anti-p53 drug, PK11007
Synnott, N. C., Bauer, M. R., Madden, S. F., Murray, A. M., Klinger, R. W., O’Donovan, N., O’Connor, D. P., Gallagher, W. M., Crown, J. P., Fersht, A. R., et al. (2017) · Cancer Letters · doi:10.1016/j.canlet.2017.09.053 · Cited by 72
Differential expression analysis for RNAseq using Poisson mixed models
Sun, S., Hood, M. M., Scott, L. J., Peng, Q., Mukherjee, S., Tung, J., & Zhou, X. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx204 · Cited by 75
Statistical normalization methods in microbiome data with application to microbiome cancer research
Xia, Y. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2244139 · Cited by 76
Transcriptome and Small RNAome Dynamics during a Resistant and Susceptible Interaction between Cucumber and Downy Mildew
Burkhardt, A., & Day, B. (2016) · The Plant Genome · doi:10.3835/plantgenome2015.08.0069 · Cited by 67
Time-Course Transcriptome Analysis of Arabidopsis Siliques Discloses Genes Essential for Fruit Development and Maturation
Mizzotti, C., Rotasperti, L., Moretto, M., Tadini, L., Resentini, F., Galliani, B. M., Galbiati, M., Engelen, K., Pesaresi, P., & Masiero, S. (2018) · PLANT PHYSIOLOGY · doi:10.1104/pp.18.00727 · Cited by 61
A comparison of transcriptome analysis methods with reference genome
Liu, X., Zhao, J., Xue, L., Zhao, T., Ding, W., Han, Y., & Ye, H. (2022) · BMC Genomics · doi:10.1186/s12864-022-08465-0 · Cited by 64
SERS microscopy as a tool for comprehensive biochemical characterization in complex samples
Kneipp, J., Seifert, S., & Gärber, F. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00460d · Cited by 61
Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis
Rigaill, G. J., Balzergue, S., Brunaud, V., Blondet, E., Rau, A., Rogier, O., Caïus, J., Maugis-Rabusseau, C., Soubigou‐Taconnat, L., Aubourg, S., et al. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw092 · Cited by 75
Comparison of Bacillus subtilis transcriptome profiles from two separate missions to the International Space Station
Morrison, M. D., Fajardo-Cavazos, P., & Nicholson, W. L. (2018) · npj Microgravity · doi:10.1038/s41526-018-0061-0 · Cited by 61
miR167 limits anther growth to potentiate anther dehiscence
Zheng, L., Nagpal, P., Villarino, G., Trinidad, B., Bird, L., Huang, Y., & Reed, J. W. (2019) · Development · doi:10.1242/dev.174375 · Cited by 58
In-Depth Duodenal Transcriptome Survey in Chickens with Divergent Feed Efficiency Using RNA-Seq
Yi, G., Yuan, J., Bi, H., Yan, W., Yang, N., & Qu, L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0136765 · Cited by 57
Differential gene expression analysis tools exhibit substandard performance for long non-coding RNA-sequencing data
Assefa, A. T., Paepe, K. D., Everaert, C., Mestdagh, P., Thas, O., & Vandesompele, J. (2018) · Genome biology · doi:10.1186/s13059-018-1466-5 · Cited by 59
Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies
Li, X., Cooper, N. G. F., O’Toole, T. E., & Rouchka, E. C. (2020) · BMC Genomics · doi:10.1186/s12864-020-6502-7 · Cited by 60
RNA-seq analysis for detecting quantitative trait-associated genes
Seo, M., Kim, K., Yoon, J. H., Jeong, J. Y., Lee, H. J., Cho, S., & Kim, H. (2016) · Scientific Reports · doi:10.1038/srep24375 · Cited by 57
RNA-seq Analysis Reveals Unique Transcriptome Signatures in Systemic Lupus Erythematosus Patients with Distinct Autoantibody Specificities
Rai, R., Chauhan, S. K., Singh, V. V., Rai, M., & Rai, G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0166312 · Cited by 54
Comparative Transcriptomic Analysis of Virulence Factors in Leptosphaeria maculans during Compatible and Incompatible Interactions with Canola
Sonah, H., Zhang, X., Deshmukh, R. K., Borhan, M. H., Fernando, W. G. D., & Bélanger, R. R. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01784 · Cited by 51
Vitamin D receptor as a target for breast cancer therapy
Murray, A. M., Madden, S. F., Synnott, N. C., Klinger, R. W., O’Connor, D. P., O’Donovan, N., Gallagher, W. M., Crown, J. P., & Duffy, M. J. (2017) · Endocrine Related Cancer · doi:10.1530/erc-16-0463 · Cited by 53
Transcriptomic Analysis of Gill and Kidney from Asian Seabass (Lates calcarifer) Acclimated to Different Salinities Reveals Pathways Involved with Euryhalinity
Vij, S., Purushothaman, K., Sridatta, P. S. R., & Jerry, D. R. (2020) · Genes · doi:10.3390/genes11070733 · Cited by 52
An RNA-Seq Protocol for Differential Expression AnalysisPreprint
Owens, N. D. L., Domenico, E. D., & Gilchrist, M. J. (2019) · Cold Spring Harbor Protocols · doi:10.1101/pdb.prot098368 · Cited by 51
Meta-analysis of the space flight and microgravity response of the Arabidopsis plant transcriptome
Barker, R. J., Kruse, C. P. S., Johnson, C. M., Saravia-Butler, A., Fogle, H. W., Chang, H.-S., Trane, R. M., Kinscherf, N. A., Villacampa, A., Manzano, A., et al. (2023) · npj Microgravity · doi:10.1038/s41526-023-00247-6 · Cited by 56
Whole blood transcriptomic analysis of beef cattle at arrival identifies potential predictive molecules and mechanisms that indicate animals that naturally resist bovine respiratory disease
Scott, M. A., Woolums, A. R., Swiderski, C. E., Perkins, A., Nanduri, B., Smith, D. R., Karisch, B. B., Epperson, W. B., & Blanton, J. R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227507 · Cited by 52
Reference genome and transcriptome informed by the sex chromosome complement of the sample increase ability to detect sex differences in gene expression from RNA-Seq data
Olney, K. C., Brotman, S. M., Andrews, J. P., Valverde-Vesling, V. A., & Wilson, M. A. (2020) · Biology of Sex Differences · doi:10.1186/s13293-020-00312-9 · Cited by 51
Advances in Transcriptomics
Wirka, R. C., Pjanic, M., & Quertermous, T. (2018) · Circulation Research · doi:10.1161/circresaha.117.310910 · Cited by 49
Transcriptomic Analysis of Calcium Remodeling in Colorectal Cancer
Pérez-Riesgo, E., Gutiérrez, L. G., Ubierna, D., Acedo, A., Moyer, M. P., Núñez, L., & Villalobos, C. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18050922 · Cited by 48
Somatic polyploidy is associated with the upregulation of c-MYC interacting genes and EMT-like signature
Vázquez‐Martín, A., Anatskaya, O. V., Giuliani, A., Erenpreisa, J. A., Huang, S., Salmiņa, K., Inashkina, I., Huna, A., Nikolsky, N. N., & Vinogradov, A. E. (2016) · Oncotarget · doi:10.18632/oncotarget.12118 · Cited by 48
consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction
Waardenberg, A. J., & Field, M. A. (2019) · PeerJ · doi:10.7717/peerj.8206 · Cited by 46
Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data
Baik, B., Yoon, S., & Nam, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232271 · Cited by 47
TRAPLINE: a standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation
Wolfien, M., Rimmbach, C., Schmitz, U., Jung, J., Krebs, S., Steinhoff, G., David, R., & Wolkenhauer, O. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0873-9 · Cited by 49
Regulation of gene expression is associated with tolerance of the Arctic copepod Calanus glacialis to CO 2‐acidified sea water
Bailey, A., Wit, P. D., Thor, P., Browman, H. I., Bjelland, R. M., Shema, S. D., Fields, D. M., Runge, J. A., Thompson, C. R. S., & Hop, H. (2017) · Ecology and Evolution · doi:10.1002/ece3.3063 · Cited by 42
Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2873010 · Cited by 49
Temporal progress of gene expression analysis with RNA-Seq data: A review on the relationship between computational methods
Silva, J. C., Domingues, D. S., Menotti, D., Hungría, M., & Lopes, F. M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.11.051 · Cited by 45
RNAontheBENCH: computational and empirical resources for benchmarking RNAseq quantification and differential expression methods
Germain, P., Vitriolo, A., Adamo, A., Laise, P., Das, V., & Testa, G. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw448 · Cited by 43
Liver transcriptome analysis reveals important factors involved in the metabolic adaptation of the transition cow
Ha, N.-T., Drögemüller, C., Reimer, C., Schmitz‐Hsu, F., Bruckmaier, R. M., Simianer, H., & Gross, J. J. (2017) · Journal of Dairy Science · doi:10.3168/jds.2016-12454 · Cited by 47
High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis
Cui, W., Xue, H., Wei, L., Jin, J., Tian, X., & Wang, Q. (2021) · Human Genomics · doi:10.1186/s40246-021-00308-5 · Cited by 42
A Comparative Transcriptome Analysis between Wild and Albino Yellow Catfish (Pelteobagrus fulvidraco)
Zou, M., Zhang, X., Shi, Z.-C., Lin, L., Ouyang, G., Zhang, G., Zheng, H., Wei, K.-J., & Ji, W. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131504 · Cited by 43
ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data
Ihnatová, I., & Budínská, E. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0763-1 · Cited by 42
RNentropy: an entropy-based tool for the detection of significant variation of gene expression across multiple RNA-Seq experiments
Zambelli, F., Mastropasqua, F., Picardi, E., D’Erchia, A. M., Pesole, G., & Pavesi, G. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky055 · Cited by 44
Phylogenetic analyses and in-seedling expression of ammonium and nitrate transporters in wheat
Bajgain, P., Russell, B., & Mohammadi, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-25430-8 · Cited by 41
Transcriptomic Analysis of mRNA-lncRNA-miRNA Interactions in Hepatocellular Carcinoma
Tang, X., Feng, D., Li, M., Zhou, J., Li, X., Zhao, D., Hao, B., Li, D., & Ding, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-52559-x · Cited by 43
Avian transcriptomics: opportunities and challenges
Jax, E., Wink, M., & Kraus, R. H. (2018) · Journal für Ornithologie · doi:10.1007/s10336-018-1532-5 · Cited by 44
Conserved genes and pathways in primary human fibroblast strains undergoing replicative and radiation induced senescence
Marthandan, S., Menzel, U., Priebe, S., Groth, M., Guthke, R., Platzer, M., Hemmerich, P., Kaether, C., & Diekmann, S. (2016) · Biological Research · doi:10.1186/s40659-016-0095-2 · Cited by 39
De novo transcriptome assembly and analysis of differential gene expression in response to drought in European beech
Mueller, M., Seifert, S., Lübbe, T., Leuschner, C., & Finkeldey, R. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184167 · Cited by 37
A Guide for Designing and Analyzing RNA-Seq Data
Chatterjee, A., Ahn, A., Rodger, E. J., Stockwell, P. A., & Eccles, M. R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7834-2_3 · Cited by 42
Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Choi, S. H., Labadorf, A. T., Myers, R. H., Lunetta, K. L., Dupuis, J., & DeStefano, A. L. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1498-y · Cited by 37
Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes
Chen, M., & Zhou, X. (2017) · Scientific Reports · doi:10.1038/s41598-017-13665-w · Cited by 38
Phylostratic Shift of Whole-Genome Duplications in Normal Mammalian Tissues towards Unicellularity Is Driven by Developmental Bivalent Genes and Reveals a Link to Cancer
Anatskaya, O. V., Vinogradov, A. E., Vainshelbaum, N. M., Giuliani, A., & Erenpreisa, J. A. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21228759 · Cited by 36
Transcriptomic Signature of the SHATTERPROOF2 Expression Domain Reveals the Meristematic Nature of Arabidopsis Gynoecial Medial Domain
Villarino, G., Flores‐Vergara, M. A., Hu, Q., Sehra, B., Robles, L. M., Brumós, J., Stepanova, A. N., Manrique, S., Colombo, L., Sundberg, E., et al. (2016) · PLANT PHYSIOLOGY · doi:10.1104/pp.15.01845 · Cited by 35
Applied equine genetics
Finno, C. J., & Bannasch, D. L. (2014) · Equine Veterinary Journal · doi:10.1111/evj.12294 · Cited by 36
Association of gene expression with biomass content and composition in sugarcane
Hoang, N. V., Furtado, A., O’Keeffe, A. J., Botha, F. C., & Henry, R. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183417 · Cited by 35
Onco-proteogenomics: Multi-omics level data integration for accurate phenotype prediction
Dimitrakopoulos, L., Prassas, I., Diamandis, E. P., & Charames, G. S. (2017) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2017.1384446 · Cited by 36
dearseq: a variance component score test for RNA-seq differential analysis that effectively controls the false discovery rate
Gauthier, M., Agniel, D. M., Thiébaut, R., & Hejblum, B. P. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa093 · Cited by 33
Genomic limitations to RNA sequencing expression profiling
Hirsch, C. D., Springer, N. M., & Hirsch, C. N. (2015) · The Plant Journal · doi:10.1111/tpj.13014 · Cited by 32
3D: diversity, dynamics, differential testing – a proposed pipeline for analysis of next-generation sequencing T cell repertoire data
Zhang, L., Cham, J., Paciorek, A. T., Trager, J. B., Sheikh, N. A., & Fong, L. H. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1544-9 · Cited by 33
Dual RNA‐Seq of Lysobacter capsici AZ78 – Phytophthora infestans interaction shows the implementation of attack strategies by the bacterium and unsuccessful oomycete defense responses
Tomada, S., Sonego, P., Moretto, M., Engelen, K., Pertot, I., Perazzolli, M., & Puopolo, G. (2017) · Environmental Microbiology · doi:10.1111/1462-2920.13861 · Cited by 33
Molecular classification of autofluorescence excision margins in oral potentially malignant disorders
Farah, C. S., Kordbacheh, F., John, K., Bennett, N. J., & Fox, S. A. (2017) · Oral Diseases · doi:10.1111/odi.12818 · Cited by 33
Plau and Tgfbr3 are YAP-regulated genes that promote keratinocyte proliferation
Corley, S., Mendoza‐Reinoso, V., Giles, N., Singer, E. S., Common, J., Wilkins, M. R., & Beverdam, A. (2018) · Cell Death and Disease · doi:10.1038/s41419-018-1141-5 · Cited by 33
Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies
de Brevern, A. G., Meyniel, J.-P., Fairhead, C., Neuvéglise, C., & Malpertuy, A. (2015) · BioMed Research International · doi:10.1155/2015/904541 · Cited by 33

Authors: Seyednasrollah, F., Laiho, A., & Elo, L. L.
Affiliations: University of Turku (FI)
Corresponding author: Elo, L. L.
ORCID: Seyednasrollah, F.
Laiho, A.
Elo, L. L.
Venue: Briefings in Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2013-12-02
Volume: 16
Issue: 1
Pages: 59-70
ISSN: 1467-5463 (ISSN-L)
1477-4054
DOI: 10.1093/bib/bbt086
PMC: PMC4293378
OpenAlex: W2123579165
Cited by: 421
References: 27
Citations by year: 2026: 8 · 2025: 10 · 2024: 18 · 2023: 35 · 2022: 34 · 2021: 39 · 2020: 41 · 2019: 41 · 2018: 39 · 2017: 55 · 2016: 52 · 2015: 32 · 2014: 16 · 2013: 1
FWCI: 15.09
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Briefings in Bioinformatics (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: Academy of Finland
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: differential gene expression, RNA-seq, software comparison, bioinformatics pipeline, molecular markers, gene expression profiling
MeSH terms: Animals, Data Interpretation, Statistical, Humans, Software, Sequence Analysis, RNA, Mice, Transcriptome

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

Comparison of software packages for detecting differential expression in RNA-seq studies

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks