Sequencing technology does not eliminate biological variability

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

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

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

Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516

Stochastic Gene Expression in a Single Cell

Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441

Alternative isoform regulation in human tissue transcriptomes

Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181

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

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

Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., & Irizarry, R. A. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2825 · Cited by 2,196

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

MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126

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

Stranger, B. E., Forrest, M. S., Dunning, M., Ingle, C., Beazley, C., Thorne, N., Redon, R., Bird, C., Grassi, A., Lee, C., et al. (2007) · Science · doi:10.1126/science.1136678 · Cited by 1,743

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 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

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

McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195

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

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

Shen, S., Park, J. W., Lu, Z.-X., Lin, L., Henry, M. D., Wu, Y. N., Zhou, Q., & Xing, Y. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419161111 · Cited by 2,713

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

Detecting differential usage of exons from RNA-seq data

Anders, S., Reyes, A., & Huber, W. (2012) · Genome Research · doi:10.1101/gr.133744.111 · Cited by 1,545

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

Anders, S., McCarthy, D. J., Chen, Y., Okoniewski, M. J., Smyth, G. K., Huber, W. K., & Robinson, M. D. (2013) · Nature Protocols · doi:10.1038/nprot.2013.099 · Cited by 1,170

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

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

Hansen, K. D., Irizarry, R. A., & WU, Z. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr054 · Cited by 607

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 551

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

Liu, Y., Zhou, J., & White, K. P. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt688 · Cited by 497

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 487

Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders

Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430

RNA-sequencing from single nuclei

Grindberg, R. V., Yee-Greenbaum, J. L., McConnell, M. J., Novotny, M., O’Shaughnessy, A. L., Lambert, G. M., Araúzo-Bravo, M. J., Lee, J., Fishman, M., Robbins, G. E., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1319700110 · Cited by 422

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

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 373

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 248

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

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

Mining cancer methylomes: prospects and challenges

Stirzaker, C., Taberlay, P. C., Statham, A. L., & Clark, S. J. (2013) · Trends in Genetics · doi:10.1016/j.tig.2013.11.004 · Cited by 202

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

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 157

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 147

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 155

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

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 121

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

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 110

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

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

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 98

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

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

Gierliński, M., Cole, C., Schofield, P., Schurch, N., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G. G., Owen‐Hughes, T., et al. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv425 · Cited by 84

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

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 79

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

Zhao, K., Lu, Z., Park, J. W., Zhou, Q., & Xing, Y. (2013) · Genome biology · doi:10.1186/gb-2013-14-7-r74 · Cited by 76

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

Xu, J., Sun, J., Chen, J., Wang, L. H.-C., Li, A., Helm, M., Dubovsky, S. L., Bacanu, S., Zhao, Z., & Chen, X. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-s8-s2 · Cited by 77

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

Perkins, J. R., Antunes‐Martins, A., Calvo, M., Grist, J., Rust, W., Schmid, R., Hildebrandt, T., Kohl, M., Orengo, C. A., McMahon, S. B., et al. (2014) · Molecular Pain · doi:10.1186/1744-8069-10-7 · Cited by 78

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 75

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 83

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

Grav, L. M., Sergeeva, D., Lee, J. S., de Mas, I. M., Lewis, N. E., Andersen, M. R., Nielsen, L. K., Lee, G. M., & Kildegaard, H. F. (2018) · ACS Synthetic Biology · doi:10.1021/acssynbio.8b00140 · Cited by 71

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

Xing, H., Mo, Y., Liao, W., & Zhang, M. Q. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002613 · Cited by 71

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

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

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 64

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 63

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

Gündisch, S., Hauck, S. M., Sarioglu, H., Schott, C., Viertler, C., Kap, M., Schuster, T., Reischauer, B., Rosenberg, R., Verhoef, C., et al. (2012) · Journal of Proteome Research · doi:10.1021/pr300560y · Cited by 62

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

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 63

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

Wang, X., & Cairns, M. J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s5-s16 · Cited by 57

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 55

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

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 47

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 47

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

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

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

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

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

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

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

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

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

Dysregulation of MicroRNA Regulatory Network in Lower Extremities Arterial Disease

Bogucka‐Kocka, A., Zalewski, D. P., Ruszel, K. P., Stępniewski, A., Gałkowski, D., Bogucki, J., Komsta, Ł., Kołodziej, P., Zubilewicz, T., Feldo, M., et al. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01200 · Cited by 35

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

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

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 36

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 35

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

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

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

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

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

Vitali, F., Li, Q., Schissler, A. G., Berghout, J., Kenost, C., & Lussier, Y. A. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx149 · Cited by 31

Microfluidics Facilitates the Development of Single-Cell RNA Sequencing

Pan, Y., Cao, W., Mu, Y., & Zhu, Q. (2022) · Biosensors · doi:10.3390/bios12070450 · Cited by 32

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Stokes, T., Cen, H. H., Kapranov, P., Gallagher, I. J., Pitsillides, A. A., Volmar, C., Kraus, W. E., Johnson, J. D., Phillips, S. M., Wahlestedt, C., et al. (2023) · Advanced Genetics · doi:10.1002/ggn2.202200024 · Cited by 28

Omics/systems biology and cancer cachexia

Gallagher, I. J., Jacobi, C., Tardif, N., Rooyackers, O., & Fearon, K. (2016) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2015.12.022 · Cited by 28

Transcriptome analysis of sex-related genes in the blood clam Tegillarca granosa

Chen, H., Xiao, G., Chai, X., Lin, X., Fang, J., & Teng, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184584 · Cited by 27

Maintaining Genome Integrity during Seed Development in Phaseolus vulgaris L.: Evidence from a Transcriptomic Profiling Study

Parreira, J. R., Balestrazzi, A., Fevereiro, P. S., & Araújo, S. D. S. (2018) · Genes · doi:10.3390/genes9100463 · Cited by 26

miRNA-Seq normalization comparisons need improvement

Zhou, X., Oshlack, A., & Robinson, M. D. (2013) · RNA · doi:10.1261/rna.037895.112 · Cited by 26

Advancing brain barriers RNA sequencing: guidelines from experimental design to publication

Francisco, D. M. F., Marchetti, L., Rodríguez‐Lorenzo, S., Frías-Anaya, E., de Figueiredo, R. M., BtRAIN Network, Heymans, M., Culot, M., Santa-Maria, A. R., Deli, M. A., et al. (2020) · Fluids and Barriers of the CNS · doi:10.1186/s12987-020-00207-2 · Cited by 24

Evaluating whole transcriptome amplification for gene profiling experiments using RNA-Seq

Faherty, S. L., Campbell, C. R., Larsen, P. A., & Yoder, A. D. (2015) · BMC Biotechnology · doi:10.1186/s12896-015-0155-7 · Cited by 25

Transcriptomic analysis of early B-cell development in the chicken embryo

Nuthalapati, N., Evans, J. D., Taylor, R. L., Branton, S. L., Nanduri, B., & Pharr, G. T. (2019) · Poultry Science · doi:10.3382/ps/pez354 · Cited by 24

Insight into Monascus pigments production promoted by glycerol based on physiological and transcriptome analyses

Shi, J. D., Zhao, W., Jin, L., Wang, W., Yu, X., & Feng, Y. (2020) · Process Biochemistry · doi:10.1016/j.procbio.2020.12.006 · Cited by 24

The vitellogenin receptor has an essential role in vertical transmission of rice stripe virus during oogenesis in the small brown plant hopper

He, K., Lin, K., Ding, S., Wang, G., & Li, F. (2018) · Pest Management Science · doi:10.1002/ps.5256 · Cited by 23

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An Integrated Bioinformatics Analysis Repurposes an Antihelminthic Drug Niclosamide for Treating HMGA2-Overexpressing Human Colorectal Cancer

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A Survey of MicroRNA Length Variants Contributing to miRNome Complexity in Peach (Prunus Persica L.)

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Discovery of Organophosphate Resistance-Related Genes Associated With Well-known Resistance Mechanisms ofPlutella xylostella(L.) (Lepidoptera: Plutellidae) by RNA-Seq

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Transcriptional Profiling of Rice Treated with MoHrip1 Reveal the Function of Protein Elicitor in Enhancement of Disease Resistance and Plant Growth

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Genome‐scale analysis to identify prognostic markers and predict the survival of lung adenocarcinoma

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The role of antigen expression in shaping the repertoire of HLA presented ligands

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Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities

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Circular RNAs exhibit extensive intraspecific variation in maize

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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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Experimental Design and Power Calculation for RNA-seq Experiments

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Differential Circular RNA Expression Profiling During Osteogenic Differentiation in Human Adipose-Derived Stem Cells

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Whole-transcriptome sequencing (RNA-seq) study of the ZFL zebrafish liver cell line after acute exposure to Cd2+ ions

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Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data

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Antibody reproducibility challenges: the solution lies in the sequence

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Additional file 1 of Detecting antibody reactivities in Phage ImmunoPrecipitation Sequencing data

Chen, A., Kammers, K., Larman, H. B., Scharpf, R. B., & Ruczinski, I. (2022) · Figshare · doi:10.6084/m9.figshare.21130049

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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