A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data

<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 · see citing works

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

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

Bioconductor: open software development for computational biology and bioinformatics

Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome Biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 13,133 · see citing works

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 · see citing works

A scaling normalization method for differential expression analysis of RNA-seq data

Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876 · see citing works

limma: Linear Models for Microarray Data

Smyth, G. K. (2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0_23 · Cited by 5,796 · 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

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 · see citing works

Understanding mechanisms underlying human gene expression variation with RNA sequencing

Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256 · see citing works

Small-sample estimation of negative binomial dispersion, with applications to SAGE data

Robinson, M. D., & Smyth, G. K. (2007) · Biostatistics · doi:10.1093/biostatistics/kxm030 · Cited by 1,073 · see citing works

Genome-wide mapping of alternative splicing in Arabidopsis thalianaPreprint

Filichkin, S. A., Priest, H. D., Givan, S. A., Shen, R., Bryant, D. W., Fox, S. E., Wong, W., & Mockler, T. C. (2009) · Genome Research · doi:10.1101/gr.093302.109 · Cited by 903 · 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

baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data

Hardcastle, T. J., & Kelly, K. A. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-422 · Cited by 755 · see citing works

The developmental dynamics of the maize leaf transcriptome

Li, P., Ponnala, L., Gandotra, N., Wang, L., Si, Y., Tausta, S. L., Kebrom, T. H., Provart, N. J., Patel, R. V., Myers, C. R., et al. (2010) · Nature Genetics · doi:10.1038/ng.703 · Cited by 743 · see citing works

RNA-Seq Atlas of Glycine max: A guide to the soybean transcriptome

Severin, A. J., Woody, J. L., Bolon, Y., Joseph, B., Diers, B. W., Farmer, A. D., Muehlbauer, G. J., Nelson, R. T., Grant, D., Specht, J. E., et al. (2010) · BMC Plant Biology · doi:10.1186/1471-2229-10-160 · Cited by 712 · see citing works

From RNA-seq reads to differential expression results

Oshlack, A., Robinson, M. D., & Young, M. D. (2010) · Genome biology · doi:10.1186/gb-2010-11-12-220 · Cited by 697 · see citing works

Improved statistical tests for differential gene expression by shrinking variance components estimates

Cui, X., Hwang, J. T. G., Qiu, J., Blades, N. J., & Churchill, G. A. (2004) · Biostatistics · doi:10.1093/biostatistics/kxh018 · Cited by 489 · see citing works

Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptomePreprint

Zhang, G., Guo, G., Hu, X., Zhang, Y., Li, Q., Li, R., Zhuang, R., Lu, Z., He, Z., Fang, X., et al. (2010) · Genome Research · doi:10.1101/gr.100677.109 · Cited by 481 · see citing works

Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seqPreprint

Lu, T., Lu, G., Fan, D., Zhu, C., Li, W., Zhao, Q., Feng, Q., Zhao, Y., Guo, Y., Li, W., et al. (2010) · Genome Research · doi:10.1101/gr.106120.110 · Cited by 315 · see citing works

A two-parameter generalized Poisson model to improve the analysis of RNA-seq data

Srivastava, S., & Chen, L. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq670 · Cited by 152 · see citing works

A Two-Stage Poisson Model for Testing RNA-Seq Data

Auer, P. L., & Doerge, R. W. (2011) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1627 · Cited by 139 · see citing works

Exploring plant transcriptomes using ultra high-throughput sequencing

Wang, L., Li, P., & Brutnell, T. P. (2010) · Briefings in Functional Genomics · doi:10.1093/bfgp/elp057 · Cited by 122 · see citing works

Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach

Lu, J., Tomfohr, J. K., & Kepler, T. B. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-165 · Cited by 79 · see citing works

Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis

Nettleton, D. S., Recknor, J. C., & Reecy, J. M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm583 · Cited by 78 · see citing works

Statistical Modeling of RNA-Seq Data

Salzman, J., Jiang, H., & Wong, W. H. (2011) · Statistical Science · doi:10.1214/10-sts343 · Cited by 68 · see citing works

Identifiability of isoform deconvolution from junction arrays and RNA-Seq

Hiller, D. J., Jiang, H., Xu, W., & Wong, W. H. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp544 · Cited by 65 · see citing works

Statistical modeling of RNA-Seq data

Salzman, J., Jiang, H., & Wong, W. H. (2011) · Cited by 62 · see citing works

Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates

Baggerly, K., Deng, L., Morris, J. S., & Aldaz, C. M. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-144 · Cited by 58 · see citing works

Optimal Tests Shrinking Both Means and Variances Applicable to Microarray Data Analysis

Hwang, J. T. G., & Liu, P. (2010) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1587 · Cited by 40 · 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

Ten years of next-generation sequencing technology

van Dijk, E., Auger, H., Jaszczyszyn, Y., & Thermes, C. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.07.001 · Cited by 1,632 · see citing works

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 · see citing works

A comparison of methods for differential expression analysis of RNA-seq data

Soneson, C., & Delorenzi, M. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-91 · Cited by 887 · see citing works

ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq

Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G., & Gloor, G. B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067019 · Cited by 877 · see citing works

ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions

Furey, T. S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3306 · Cited by 825 · see citing works

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r95 · Cited by 733 · see citing works

Corset: enabling differential gene expression analysis for

Davidson, N., & Oshlack, A. (2014) · Genome biology · doi:10.1186/s13059-014-0410-6 · Cited by 677 · see citing works

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 624 · see citing works

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

Seyednasrollah, F., Laiho, A., & Elo, L. L. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbt086 · Cited by 421 · see citing works

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 357 · see citing works

RNA-Seq Technology and Its Application in Fish Transcriptomics

Qian, X., Ba, Y., Zhuang, Q., & Zhong, G. (2013) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2013.0110 · Cited by 350 · see citing works

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 324 · see citing works

Applications of next‐generation sequencing in plant biology

Egan, A. N., Schlueter, J. A., & Spooner, D. M. (2012) · American Journal of Botany · doi:10.3732/ajb.1200020 · Cited by 322 · see citing works

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 · see citing works

Advanced Applications of RNA Sequencing and Challenges

Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28991 · Cited by 277 · 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

Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution?

Álvarez, M., Schrey, A. W., & Richards, C. L. (2014) · Molecular Ecology · doi:10.1111/mec.13055 · Cited by 248 · see citing works

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 250 · see citing works

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 234 · see citing works

Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA ‐seq experiments

Vijay, N., Poelstra, J. W., Künstner, A., & Wolf, J. B. W. (2012) · Molecular Ecology · doi:10.1111/mec.12014 · Cited by 225 · see citing works

Principles of transcriptome analysis and gene expression quantification: an RNA ‐seq tutorial

Wolf, J. B. W. (2013) · Molecular Ecology Resources · doi:10.1111/1755-0998.12109 · Cited by 219 · see citing works

Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

Chen, J. W., & Dhahbi, J. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-92725-8 · Cited by 208 · see citing works

VOLCANO PLOTS IN ANALYZING DIFFERENTIAL EXPRESSIONS WITH mRNA MICROARRAYS

Li, W. (2012) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720012310038 · Cited by 201 · see citing works

Transcriptome-Wide Changes in Chlamydomonas reinhardtii Gene Expression Regulated by Carbon Dioxide and the CO2-Concentrating Mechanism Regulator CIA5/CCM1

Wei, F., Si, Y., Douglass, S. M., Casero, D., Merchant, S., Pellegrini, M. A., Ladunga, I., Liu, P., & Spalding, M. H. (2012) · The Plant Cell · doi:10.1105/tpc.112.097949 · Cited by 196 · see citing works

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 · see citing works

Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing

Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., & Taylor, J. M. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-484 · Cited by 198 · see citing works

Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq

Rajkumar, A. P., Qvist, P., Lazarus, R. M., Lescai, F., Ju, J., Nyegaard, M., Mors, O. N., Børglum, A. D., Li, Q., & Christensen, J. (2015) · BMC Genomics · doi:10.1186/s12864-015-1767-y · Cited by 196 · see citing works

Transcriptome Analysis Based on RNA-Seq in Understanding Pathogenic Mechanisms of Diseases and the Immune System of Fish: A Comprehensive Review

Sudhagar, A., Kumar, G., & El‐Matbouli, M. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19010245 · Cited by 193 · see citing works

Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data

Guo, Y. P., Sheng, Q., Li, J., Ye, F., Samuels, D. C., & Shyr, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071462 · Cited by 189 · see citing works

RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species

Smith, S., Bernatchez, L., & Beheregaray, L. B. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-375 · Cited by 181 · see citing works

RNA-Seq: revelation of the messengers

Verk, M. C. V., Hickman, R. J., Pieterse, C. M. J., & Wees, S. C. M. V. (2013) · Trends in Plant Science · doi:10.1016/j.tplants.2013.02.001 · Cited by 156 · see citing works

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 134 · see citing works

Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments

Maza, É., Frasse, P., Senin, P., Bouzayen, M., & Zouine, M. (2013) · Communicative & Integrative Biology · doi:10.4161/cib.25849 · Cited by 136 · see citing works

Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets

Xu, X., Zhang, Y., Williams, J. L., Antoniou, E., McCombie, W. R., Wu, S., Zhu, W., Davidson, N. O., Denoya, P. I., & Li, E. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s9-s1 · Cited by 129 · see citing works

Integrated analysis of microRNA expression and mRNA transcriptome in lungs of avian influenza virus infected broilers

Wang, Y., Brahmakshatriya, V., Lupiani, B., Reddy, S. M., Soibam, B., Benham, A. L., Gunaratne, P. H., Liu, H., Trakooljul, N., Ing, N. H., et al. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-278 · Cited by 111 · see citing works

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 · see citing works

Long non-coding RNAs in innate and adaptive immunity

Aune, T. M., & Spurlock, C. F. (2015) · Virus Research · doi:10.1016/j.virusres.2015.07.003 · Cited by 103 · see citing works

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 · see citing works

RNA-Seq Gene Profiling - A Systematic Empirical Comparison

Fonseca, N. A., Marioni, J. C., & Brāzma, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107026 · Cited by 99 · see citing works

Comparing in vitro human liver models to in vivo human liver using RNA-Seq

Gupta, R., Schrooders, Y., Hauser, D. A., van Herwijnen, M. H. M., Albrecht, W., ter Braak, B., Brecklinghaus, T., Castell, J. V., Elenschneider, L., Escher, S. E., et al. (2020) · Archives of Toxicology · doi:10.1007/s00204-020-02937-6 · Cited by 94 · 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

Exploring local immunological adaptation of two stickleback ecotypes by experimental infection and transcriptome‐wide digital gene expression analysis

Lenz, T. L., Eizaguirre, C., Rotter, B., Kalbe, M., & Milinski, M. (2012) · Molecular Ecology · doi:10.1111/j.1365-294x.2012.05756.x · Cited by 88 · see citing works

Identification and Expression Analysis of the Barley (Hordeum vulgare L.) Aquaporin Gene Family

Hove, R. M., Ziemann, M., & Bhave, M. R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128025 · Cited by 86 · see citing works

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 86 · see citing works

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 · see citing works

The potential of genomics in plant systematics

Soltis, P. S., Gitzendanner, M. A., Stull, G. W., Chester, M., Chanderbali, A. S., Chamala, S., Jordon‐Thaden, I. E., Soltis, P. S., Schnable, P. S., & Barbazuk, W. B. (2013) · Taxon · doi:10.12705/625.13 · Cited by 79 · see citing works

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2893170 · Cited by 75 · see citing works

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 72 · see citing works

RNA-Seq transcriptome analysis of Spirodela dormancy without reproduction

Wang, W., Wu, Y., & Messing, J. W. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-60 · Cited by 66 · see citing works

Identification of two putative reference genes from grapevine suitable for gene expression analysis in berry and related tissues derived from RNA-Seq data

González-Agüero, M., García-Rojas, M., Genova, A. D., Correa, J., Maass, A., Orellana, A., & Hinrichsen, P. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-878 · Cited by 65 · see citing works

Identification of Drought-Responsive Phenolic Compounds and Their Biosynthetic Regulation Under Drought Stress in Ligularia Fischeri

Park, Y. J., Kwon, D. Y., Koo, S. Y., Truong, T. Q., Hong, S.-C., Choi, J., Moon, J., & Kim, S. M. (2022) · Frontiers in Plant Science · doi:10.2139/ssrn.4148598 · Cited by 63 · see citing works

MultiRankSeq: Multiperspective Approach for RNAseq Differential Expression Analysis and Quality Control

Guo, Y. P., Zhao, S., Ye, F., Sheng, Q., & Shyr, Y. (2014) · BioMed Research International · doi:10.1155/2014/248090 · Cited by 60 · see citing works

A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data

Silva, A., Rothstein, S. J., McNicholas, P. D., & Subedi, S. D. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2916-0 · Cited by 56 · see citing works

The transcriptome of the mosquito Aedes fluviatilis (Diptera: Culicidae), and transcriptional changes associated with its native Wolbachia infection

Caragata, E. P., Pais, F. S., Baton, L. A., Silva, J. B. L., Sorgine, M. H. F., & Moreira, L. A. (2017) · BMC Genomics · doi:10.1186/s12864-016-3441-4 · Cited by 55 · see citing works

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 56 · see citing works

Global characterization and target identification of piRNAs and endo-siRNAs in mouse gametes and zygotes

García-López, J., Hourcade, J. D. D., Alonso, L., Cárdenas, D. B., & del Mazo, J. (2014) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2014.04.006 · Cited by 50 · see citing works

Molecular Tools for Exploring Polyploid Genomes in Plants

Aversano, R., Ercolano, M. R., Caruso, I., Fasano, C., Rosellini, D., & Carputo, D. (2012) · International Journal of Molecular Sciences · doi:10.3390/ijms130810316 · Cited by 49 · see citing works

Interactive Dimensionality Reduction for Comparative Analysis

Fujiwara, T., Wei, X., Zhao, J., & Ma, K. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114807 · Cited by 48 · see citing works

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 47 · see citing works

Chemosensory gene expression in olfactory organs of the anthropophilic Anopheles coluzzii and zoophilic Anopheles quadriannulatus

Athrey, G., Cosme, L. V., Popkin-Hall, Z. R., Pathikonda, S., Takken, W., & Slotman, M. A. (2017) · BMC Genomics · doi:10.1186/s12864-017-4122-7 · Cited by 41 · see citing works

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 48 · see citing works

Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology

Hardcastle, T. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv569 · Cited by 44 · see citing works

◾ Differential Expression Analysis

Korpelainen, E., Tuimala, J., Somervuo, P., Huss, M., & Wong, G. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b17457-12 · Cited by 46 · see citing works

Large-scale transcriptional profiling of lignified tissues in Tectona grandis

Galeano, E., Vasconcelos, T. S., Vidal, M., Mejía‐Guerra, M. K., & Carrer, H. (2015) · BMC Plant Biology · doi:10.1186/s12870-015-0599-x · Cited by 44 · see citing works

Data-based RNA-seq simulations by binomial thinning

Gerard, D. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3450-9 · Cited by 43 · see citing works

Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome

Swindell, W. R., Xing, X., Voorhees, J. J., Elder, J. T., Johnston, A., & Gudjonsson, J. E. (2014) · Physiological Genomics · doi:10.1152/physiolgenomics.00022.2014 · Cited by 42 · see citing works

Discriminative pattern mining and its applications in bioinformatics

Liu, X., Wu, J. Y., Gu, F., Wang, J., & He, Z. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu042 · Cited by 39 · see citing works

Evaluation of the hormonal state of columnar apple trees (Malus x domestica) based on high throughput gene expression studies

Krost, C., Petersen, R., Lokan, S., Brauksiepe, B., Braun, P. R., & Schmidt, E. R. (2013) · Plant Molecular Biology · doi:10.1007/s11103-012-9992-0 · Cited by 38 · see citing works

Empirical Bayesian analysis of paired high-throughput sequencing data with a beta-binomial distribution

Hardcastle, T. J., & Kelly, K. A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-135 · Cited by 35 · see citing works

RNA-SEQ: A GLANCE AT TECHNOLOGIES AND METHODOLOGIES

Jazayeri, S. M., Melgarejo, L. M., & Romero, H. M. (2014) · Acta Biológica Colombiana · doi:10.15446/abc.v20n2.43639 · Cited by 35 · see citing works

Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives

Jeon, H., Xie, J., Jeon, Y., Jung, K. J., Gupta, A., Chang, W., & Chung, D. (2023) · Biomolecules · doi:10.3390/biom13020221 · Cited by 36 · see citing works

Global Transcriptome Analysis Reveals Differences in Gene Expression Patterns Between Nonhyperhydric and Hyperhydric Peach Leaves

Bakır, Y., Eldem, V., Zararsız, G., & Ünver, T. (2016) · The Plant Genome · doi:10.3835/plantgenome2015.09.0080 · Cited by 34 · see citing works

SIBER: systematic identification of bimodally expressed genes using RNAseq data

Tong, P., Chen, Y., Su, X., & Coombes, K. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts713 · Cited by 33 · see citing works

Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software

Huang, H.-C. R., Niu, Y., & Qin, L. (2015) · Cancer Informatics · doi:10.4137/cin.s21631 · Cited by 31 · see citing works

The analytical landscape of static and temporal dynamics in transcriptome data

Oh, S., Song, S., Dasgupta, N., & Grabowski, G. A. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00035 · Cited by 29 · see citing works

Transcriptional maturation of the mouse auditory forebrain

Hackett, T. A., Guo, Y. P., Clause, A. R., Hackett, N., Garbett, K. A., Zhang, P., Polley, D. B., & Mirnics, K. (2015) · BMC Genomics · doi:10.1186/s12864-015-1709-8 · Cited by 28 · see citing works

Guidelines for RNA-seq projects: applications and opportunities in non-model decapod crustacean species

Nguyen, T. V., Jung, H., Rotllant, G., Hurwood, D. A., Mather, P. B., & Ventura, T. (2018) · Hydrobiologia · doi:10.1007/s10750-018-3682-0 · Cited by 29 · see citing works

Gaussian graphical models with applications to omics analyses

Shutta, K. H., Vito, R. D., Scholtens, D. M., & Balasubramanian, R. (2022) · Statistics in Medicine · doi:10.1002/sim.9546 · Cited by 31 · see citing works

Wolbachia dominate Spiroplasma in the co‐infected spider mite Tetranychus truncatus

Yang, K., Xie, K., Zhu, Y., Huo, S., Hoffmann, A. A., & Hong, X. (2019) · Insect Molecular Biology · doi:10.1111/imb.12607 · Cited by 28 · see citing works

Comparison of multi-tissue aging between human and mouse

Zhuang, J., Zhang, L., Dai, S., Cui, L., Guo, C., Sloofman, L. G., & Yang, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-42485-3 · Cited by 27 · see citing works

A comprehensive workflow for optimizing RNA-seq data analysis

Gao, J., Zheng, J.-Y., Ren, S.-N., Yin, W., Xia, X., Li, Y., & Wang, H. (2024) · BMC Genomics · doi:10.1186/s12864-024-10414-y · Cited by 28 · see citing works

A multi-model statistical approach for proteomic spectral count quantitation

Branson, O. E., & Freitas, M. A. (2016) · Journal of Proteomics · doi:10.1016/j.jprot.2016.05.032 · Cited by 27 · see citing works

A Comparison of transgenic and wild type soybean seeds: analysis of transcriptome profiles using RNA-Seq

Lambirth, K., Whaley, A. M., Blakley, I., Schlueter, J. A., Bost, K. L., Loraine, A. E., & Piller, K. J. (2015) · BMC Biotechnology · doi:10.1186/s12896-015-0207-z · Cited by 27 · see citing works

Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii

Raithel, S., Johnson, L. C., Galliart, M., Brown, S., Shelton, J. M., Herndon, N., & Bello, N. M. (2016) · BMC Genomics · doi:10.1186/s12864-016-2442-7 · Cited by 23 · see citing works

Comparative RNA seq analysis of the New Zealand glowworm Arachnocampa luminosa reveals bioluminescence-related genes

Sharpe, M. L., Dearden, P. K., Gimenez, G., & Krause, K. L. (2015) · BMC Genomics · doi:10.1186/s12864-015-2006-2 · Cited by 24 · see citing works

An Optimal Test with Maximum Average Power While Controlling FDR with Application to RNA‐Seq Data

Si, Y., & Liu, P. (2013) · Biometrics · doi:10.1111/biom.12036 · Cited by 24 · see citing works

What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment

Cui, S., Ji, T., Li, J., Cheng, J., & Qiu, J. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2015-0011 · Cited by 23 · see citing works

Transcriptome reprogramming of resistant and susceptible peach genotypes during Xanthomonas arboricola pv. pruni early leaf infection

Gervasi, F., Ferrante, P., Dettori, M. T., Scortichini, M., & Verde, I. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196590 · Cited by 23 · see citing works

Probing Oral Microbial Functionality – Expression of spxB in Plaque Samples

Zhu, L. D., Xu, Y., Ferretti, J. J., & Kreth, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086685 · Cited by 23 · see citing works

RNA-Seq blood transcriptome profiling in familial attention deficit and hyperactivity disorder (ADHD)

Lorenzo, G., Braun, J., Muñoz, G., Casarejos, M. J., Bazán, E., & Jiménez‐Escrig, A. (2018) · Psychiatry Research · doi:10.1016/j.psychres.2018.10.025 · Cited by 21 · see citing works

New transcriptomic tools to understand testis development and functions

Lecluze, E., Jégou, B., Rolland, A. D., & Chalmel, F. (2018) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2018.02.019 · Cited by 20 · see citing works

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data

Gao, Z., Zhao, Z., & Tang, W. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00588 · Cited by 21 · see citing works

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

McArt, D. G., Dunne, P. D., Blayney, J. K., Salto‐Tellez, M., Schaeybroeck, S. V., Hamilton, P. W., & Zhang, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066902 · Cited by 20 · see citing works

Transcriptomic Profile of Whole Blood Cells from Elderly Subjects Fed Probiotic Bacteria Lactobacillus rhamnosus GG ATCC 53103 (LGG) in a Phase I Open Label Study

Solano-Aguilar, G. I., Molokin, A., Botelho, C. A., Fiorino, A., Vinyard, B. T., Li, R., Chen, C. T., Urban, J. F., Dawson, H. D., Andreyeva, I., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147426 · Cited by 20 · see citing works

lncRNA expression in the auditory forebrain during postnatal development

Guo, Y. P., Zhang, P., Sheng, Q., Zhao, S., & Hackett, T. A. (2016) · Gene · doi:10.1016/j.gene.2016.08.027 · Cited by 20 · see citing works

Multiple omics analysis reveals that high fiber diets promote gluconeogenesis and inhibit glycolysis in muscle

Wu, J., Ding, Y., Gong, H., Qi, Y., Sun, H., Liu, Y., Liu, Y., & Qiu, X. (2020) · BMC Genomics · doi:10.1186/s12864-020-07048-1 · Cited by 20 · see citing works

A semi-parametric statistical model for integrating gene expression profiles across different platforms

Lyu, Y., & Li, Q. (2016) · BMC Bioinformatics · doi:10.1186/s12859-015-0847-y · Cited by 20 · see citing works

Differential gene expression in Drosophila melanogaster and D. nigrosparsa infected with the same Wolbachia strain

Detcharoen, M., Schilling, M. P., Arthofer, W., Schlick-Steiner, B. C., & Steiner, F. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-90857-5 · Cited by 19 · see citing works

Transcriptome profiling in susceptible and tolerant rubber tree clones in response to cassiicolin Cas1, a necrotrophic effector from Corynespora cassiicola

Ribeiro, S., Label, P., Garcia, D., Montoro, P., & Pujade‐Renaud, V. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254541 · Cited by 19 · see citing works

Comparison of Software Packages for Detecting Differentially Expressed Genes from Single-Sample Rna-Seq Data

Zhou, R. (2021) · Open PRAIRIE (South Dakota State University)

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 · see citing works

A two-step integrated approach to detect differentially expressed genes in RNA-Seq data

Mahi, N. A., & Begum, M. (2016) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720016500347 · Cited by 2 · see citing works

Controlling False Discovery Rates in RNA-Sequencing Data

Burden, C. J., Qureshi, S. E., & Wilson, S. C. (2013) · ANU Open Research (Australian National University)

Application of edgeR and DESeq2 methods in plant experiments based on RNA-seq technology

Niedziela, G., Szabelska‐Beręsewicz, A., Zyprych-Walczak, J., & Graczyk, M. (2022) · Biometrical Letters · doi:10.2478/bile-2022-0009 · Cited by 13 · see citing works