Sequencing technology does not eliminate biological variability

Mapping and quantifying mammalian transcriptomes by RNA-Seq

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RNA-Seq: a revolutionary tool for transcriptomics

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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

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Stochastic Gene Expression in a Single Cell

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Alternative isoform regulation in human tissue transcriptomes

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RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays

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Tackling the widespread and critical impact of batch effects in high-throughput data

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The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

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Relative Impact of Nucleotide and Copy Number Variation on Gene Expression Phenotypes

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Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

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Understanding mechanisms underlying human gene expression variation with RNA sequencing

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Fundamentals of experimental design for cDNA microarrays

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Multiple-laboratory comparison of microarray platforms

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Transcriptome genetics using second generation sequencing in a Caucasian population

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Individuality and variation in gene expression patterns in human blood

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Genetic Analysis of Human Traits In Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines

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Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

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Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

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A survey of best practices for RNA-seq data analysis

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rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data

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RNA sequencing: the teenage years

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Detecting differential usage of exons from RNA-seq data

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Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

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How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

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Removing technical variability in RNA-seq data using conditional quantile normalization

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svaseq: removing batch effects and other unwanted noise from sequencing data

Leek, J. T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku864 · Cited by 538 · see citing works

RNA-seq differential expression studies: more sequence or more replication?

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From Big Data to Precision Medicine

Hulsen, T., Jamuar, S. S., Moody, A. R., Karnes, J. H., Varga, O., Hedensted, S., Spreafico, R., Hafler, D. A., & McKinney, E. F. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00034 · Cited by 466 · see citing works

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

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RNA-sequencing from single nuclei

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Recommendations for the design and analysis of epigenome-wide association studies

Michels, K. B., Binder, A. M., Dedeurwaerder, S., Epstein, C. B., Greally, J. M., Gut, I., Houseman, E. A., Izzi, B., Kelsey, K. T., Meissner, A., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2632 · Cited by 378 · see citing works

MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data

Shen, S., Park, J. W., Huang, J., Dittmar, K. A., Lu, Z., Zhou, Q., Carstens, R. P., & Xing, Y. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1291 · Cited by 362 · see citing works

A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data

Wu, H., Wang, C., & Wu, Z. J. (2012) · Biostatistics · doi:10.1093/biostatistics/kxs033 · Cited by 241 · 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

The power and promise of RNA ‐seq in ecology and evolution

Todd, E. V., Black, M. A., & Gemmell, N. J. (2016) · Molecular Ecology · doi:10.1111/mec.13526 · Cited by 229 · see citing works

Mining cancer methylomes: prospects and challenges

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Multiple sources of bias confound functional enrichment analysis of global -omics data

Timmons, J. A., Szkop, K. J., & Gallagher, I. J. (2015) · Genome biology · doi:10.1186/s13059-015-0761-7 · Cited by 183 · see citing works

ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

Frazee, A. C., Langmead, B., & Leek, J. T. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-449 · Cited by 155 · see citing works

Reviving the Transcriptome Studies: An Insight Into the Emergence of Single-Molecule Transcriptome Sequencing

Wang, B., Kumar, V., Olson, A. J., & Ware, D. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00384 · Cited by 142 · see citing works

AI applications in functional genomics

Caudai, C., Galizia, A., Geraci, F., Pera, L. L., Morea, V., Salerno, E., Via, A., & Colombo, T. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.10.009 · Cited by 150 · see citing works

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 123 · see citing works

Transfer and functional consequences of dietary microRNAs in vertebrates: Concepts in search of corroboration

Witwer, K. W., & Hirschi, K. D. (2014) · BioEssays · doi:10.1002/bies.201300150 · Cited by 118 · see citing works

PROPER: comprehensive power evaluation for differential expression using RNA-seq

Wu, H., Wang, C., & Wu, Z. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu640 · Cited by 114 · see citing works

Fighting a Losing Battle: Vigorous Immune Response Countered by Pathogen Suppression of Host Defenses in the Chytridiomycosis-Susceptible FrogAtelopus zeteki

Ellison, A., Savage, A. E., DiRenzo, G. V., Langhammer, P. F., Lips, K. R., & Zamudio, K. R. (2014) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.010744 · Cited by 107 · see citing works

Current RNA-seq methodology reporting limits reproducibility

Simoneau, J., Dumontier, S., Gosselin, R., & Scott, M. S. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz124 · Cited by 98 · see citing works

Genetic Variants Contribute to Gene Expression Variability in Humans

Hulse‐Kemp, A. M., & Cai, J. J. (2012) · Genetics · doi:10.1534/genetics.112.146779 · Cited by 98 · see citing works

Dual RNA-Sequencing to Elucidate the Plant-Pathogen Duel

Naidoo, S., Visser, E. A., Zwart, L., du Toit, Y., Bhadauria, V., & Shuey, L. S. (2017) · Current Issues in Molecular Biology · doi:10.21775/cimb.027.127 · Cited by 93 · 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

Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

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multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments

Stansfield, J. C., Cresswell, K. G., & Dozmorov, M. G. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz048 · Cited by 81 · see citing works

Methods to Study Splicing from High-Throughput RNA Sequencing Data

Alamancos, G. P., Agirre, E., & Eyras, E. (2014) · Methods in molecular biology · doi:10.1007/978-1-62703-980-2_26 · Cited by 78 · see citing works

GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data

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RNA-Seq analysis implicates dysregulation of the immune system in schizophrenia

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A Comparison of RNA-Seq and Exon Arrays for Whole Genome Transcription Profiling of the L5 Spinal Nerve Transection Model of Neuropathic Pain in the Rat

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Normalizing RNA-Sequencing Data by Modeling Hidden Covariates with Prior Knowledge

Mostafavi, S., Battle, A. J., Zhu, X., Urban, A. E., Levinson, D. F., Montgomery, S. B., & Koller, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068141 · Cited by 77 · see citing works

Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing

Aghababazadeh, F. A., Li, Q., & Fridley, B. L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206312 · Cited by 76 · see citing works

Minimizing Clonal Variation during Mammalian Cell Line Engineering for Improved Systems Biology Data Generation

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Genome-Wide Localization of Protein-DNA Binding and Histone Modification by a Bayesian Change-Point Method with ChIP-seq Data

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FunPat: function-based pattern analysis on RNA-seq time series data

Sanavia, T., Finotello, F., & Camillo, B. D. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s6-s2 · Cited by 66 · 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

A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments

Esnaola, M., Puig, P., Gonzalez, D., Castelo, R., & González, J. R. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-254 · Cited by 62 · see citing works

Transcript expression plasticity as a response to alternative larval host plants in the speciation process of corn and rice strains of Spodoptera frugiperda

Silva‐Brandão, K. L., Horikoshi, R. J., Bernardi, D., Omoto, C., Figueira, A., & Brandão, M. M. (2017) · BMC Genomics · doi:10.1186/s12864-017-4170-z · Cited by 64 · see citing works

Variability of Protein and Phosphoprotein Levels in Clinical Tissue Specimens during the Preanalytical Phase

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Comparative Transcriptome Profiling of mRNA and lncRNA Related to Tail Adipose Tissues of Sheep

Ma, L., Zhang, M., Jin, Y., Erdenee, S., Hu, L., Chen, H., Cai, Y., & Lan, X. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00365 · Cited by 61 · see citing works

Differential expression analysis of RNA-seq data at single-base resolution

Frazee, A. C., Sabunciyan, S., Hansen, K. D., Irizarry, R. A., & Leek, J. T. (2014) · Biostatistics · doi:10.1093/biostatistics/kxt053 · Cited by 59 · see citing works

Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicing

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Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments

Yu, X., Aghababazadeh, F. A., Chen, Y. A., & Fridley, B. L. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0849-4_9 · Cited by 54 · see citing works

Lathyrus sativus transcriptome resistance response to Ascochyta lathyri investigated by deepSuperSAGE analysis

Almeida, N. F., Krezdorn, N., Rotter, B., Winter, P., Rubiales, D., & Patto, M. C. V. (2015) · Frontiers in Plant Science · doi:10.3389/fpls.2015.00178 · Cited by 53 · see citing works

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles

Bajikar, S. S., Fuchs, C., Roller, A., Theis, F. J., & Janes, K. A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1311647111 · Cited by 46 · see citing works

RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods

Holik, A. Z., Law, C. W., Liu, R., Wang, Z., Wang, W., Ahn, J., Asselin-Labat, M.-L., Smyth, G. K., & Ritchie, M. E. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1063 · Cited by 44 · see citing works

Genomic reaction norms inform predictions of plastic and adaptive responses to climate change

Oomen, R. A., & Hutchings, J. A. (2022) · Journal of Animal Ecology · doi:10.1111/1365-2656.13707 · Cited by 43 · see citing works

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 · see citing works

Schizont transcriptome variation among clinical isolates and laboratory-adapted clones of the malaria parasite Plasmodium falciparum

Tarr, S. J., Díaz‐Ingelmo, O., Stewart, L. B., Hocking, S. E., Murray, L., Duffy, C. W., Otto, T. D., Chappell, L., Rayner, J. C., Awandare, G. A., et al. (2018) · BMC Genomics · doi:10.1186/s12864-018-5257-x · Cited by 40 · see citing works

Anaerobic biosynthesis of rhamnolipids by Pseudomonas aeruginosa: performance, mechanism and its application potential for enhanced oil recovery

Zhao, F., Wang, Q., Zhang, Y., & Lei, L. (2021) · Microbial Cell Factories · doi:10.1186/s12934-021-01593-4 · Cited by 39 · see citing works

Transcriptome profiling analysis of senescent gingival fibroblasts in response to Fusobacterium nucleatum infection

Ahn, S. H., Chun, S., Park, C., Lee, J., Lee, S.-W., & Lee, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188755 · Cited by 39 · see citing works

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 37 · see citing works

Transcriptome sequencing of Salvia miltiorrhiza after infection by its endophytic fungi and identification of genes related to tanshinone biosynthesis

Jiang, Y. J., Wang, L., Lu, S., Xue, Y., Wei, X., Lü, J., & Zhang, Y. (2019) · Pharmaceutical Biology · doi:10.1080/13880209.2019.1680706 · Cited by 39 · see citing works

Gene Expression Profiling of Corpus luteum Reveals Important Insights about Early Pregnancy in Domestic Sheep

Pokharel, K., Peippo, J., Weldenegodguad, M., Honkatukia, M., Li, M.-H., & Kantanen, J. (2020) · Genes · doi:10.3390/genes11040415 · Cited by 38 · see citing works

Impact of Bioinformatic Procedures in the Development and Translation of High-Throughput Molecular Classifiers in Oncology

Ferté, C., Trister, A. D., Huang, E. S., Bot, B. M., Guinney, J., Commo, F., Sieberts, S. K., André, F., Besse, B., Soria, J., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-3937 · Cited by 36 · see citing works

Dysregulation of MicroRNA Regulatory Network in Lower Extremities Arterial Disease

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Multi-Omics Analysis of the Gut-Liver Axis Reveals the Mechanism of Liver Injury in Colitis Mice

Zhu, L., Zong, X., Xiao, X., Cheng, Y., Fu, J., Lu, Z., Jin, M., Wang, F., & Wang, Y. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2021.773070 · Cited by 34 · see citing works

Strong Maternal Effects on Gene Expression inArabidopsis lyrataHybrids

Videvall, E., Sletvold, N., Hagenblad, J., Ågren, J., & Hansson, B. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv342 · Cited by 35 · see citing works

Combining Next-Generation Sequencing and Microarray Technology into a Transcriptomics Approach for the Non-Model Organism Chironomus riparius

Marinković, M., de Leeuw, W. C., de Jong, M., Kraak, M. H. S., Admiraal, W. A., Breit, T. M., & Jonker, M. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048096 · Cited by 35 · see citing works

Robust identification of perturbed cell types in single-cell RNA-seq data

Nicol, P. B., Paulson, D., Qian, G., Liu, X. S., Irizarry, R. A., & Sahu, A. D. (2024) · Nature Communications · doi:10.1038/s41467-024-51649-3 · Cited by 34 · see citing works

Surgery increases cell death and induces changes in gene expression compared with anesthesia alone in the developing piglet brain

Broad, K. D., Kawano, G., Fierens, I., Rocha‐Ferreira, E., Hristova, M., Ezzati, M., Rostami, J., Alconada, D. A., Chaban, B., Hassell, J. K., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173413 · Cited by 33 · see citing works

Short Term Exposure to Heat and Sediment Triggers Changes in Coral Gene Expression and Photo-Physiological Performance

Poquita-Du, R. C., Huang, D., Chou, L. M., Mrinalini, & Todd, P. A. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00121 · Cited by 32 · see citing works

Identification of apple miRNAs and their potential role in fire blight resistance

Kaja, E., Szcześniak, M. W., Jensen, P. J., Axtell, M. J., McNellis, T. W., & Makałowska, I. (2014) · Tree Genetics & Genomes · doi:10.1007/s11295-014-0812-3 · Cited by 31 · see citing works

DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions

Klambauer, G., Unterthiner, T., & Hochreiter, S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt834 · Cited by 31 · see citing works

Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes

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Microfluidics Facilitates the Development of Single-Cell RNA Sequencing

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Transcriptomics Provides a Genetic Signature of Vineyard Site and Offers Insight into Vintage-Independent Inoculated Fermentation Outcomes

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Tamarix microRNA Profiling Reveals New Insight into Salt Tolerance

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Additional file 12 of RNA-seq: technical variability and sampling

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Additional file 15 of RNA-seq: technical variability and sampling

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