RNA-Seq: a revolutionary tool for transcriptomics

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

Serial Analysis of Gene Expression

Velculescu, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995) · Science · doi:10.1126/science.270.5235.484 · Cited by 3,713

SOAP: Short oligonucleotide alignment program

Li, R., Li, Y., Kristiansen, K., & Wang, J. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn025 · Cited by 3,597

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

Mapping short DNA sequencing reads and calling variants using mapping quality scores

Li, H., Ruan, J., & Durbin, R. (2008) · Genome Research · doi:10.1101/gr.078212.108 · Cited by 2,638

The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

Nagalakshmi, U., Wang, Z., Waern, K., Shou, C., Raha, D., Gerstein, M., & Snyder, M. (2008) · Science · doi:10.1126/science.1158441 · Cited by 2,479

Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis

Lister, R., O’Malley, R. C., Tonti‐Filippini, J., Gregory, B. D., Berry, C. C., Millar, A. H., & Ecker, J. R. (2008) · Cell · doi:10.1016/j.cell.2008.03.029 · Cited by 2,424

Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays.

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TRANSLATIONAL REGULATION OFGCN4AND THE GENERAL AMINO ACID CONTROL OF YEAST

Hinnebusch, A. G. (2005) · Annual Review of Microbiology · doi:10.1146/annurev.micro.59.031805.133833 · Cited by 1,297

A Global View of Gene Activity and Alternative Splicing by Deep Sequencing of the Human Transcriptome

Sultan, M., Schulz, M. H., Richard, H., Magen, A., Klingenhoff, A., Scherf, M., Seifert, M., Borodina, T., Soldatov, A., Parkhomchuk, D., et al. (2008) · Science · doi:10.1126/science.1160342 · Cited by 1,293

New insights into the blood‐stage transcriptome of Plasmodium falciparum using RNA‐Seq

Otto, T. D., Wilinski, D., Assefa, S. A., Keane, T., Sarry, L. R., Böhme, U., Lemieux, J. E., Barrell, B. G., Pain, A., Berriman, M., et al. (2010) · Molecular Microbiology · doi:10.1111/j.1365-2958.2009.07026.x · Cited by 394

Recurrent SETBP1 mutations in atypical chronic myeloid leukemia

Piazza, R., Valletta, S., Winkelmann, N., Redaelli, S., Spinelli, R., Pirola, A., Antolini, L., Mologni, L., Donadoni, C., Papaemmanuil, E., et al. (2012) · Nature Genetics · doi:10.1038/ng.2495 · Cited by 400

A multidimensional systems biology analysis of cellular senescence in aging and disease

Avelar, R. A., Ortega, J. G., Tacutu, R., Tyler, E. J., Bennett, D., Binetti, P., Budovsky, A., Chatsirisupachai, K., Johnson, E., Murray, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-01990-9 · Cited by 399

Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds

Shi, C.-Y., Yang, H., Wei, C., Yu, O. S., Zhang, Z., Jiang, C., Sun, J., Li, Y., Chen, Q., Xia, T., et al. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-131 · Cited by 399

Transcriptome Analysis of Psoriasis in a Large Case–Control Sample: RNA-Seq Provides Insights into Disease Mechanisms

Li, B., Tsoi, L. C., Swindell, W. R., Gudjonsson, J. E., Tejasvi, T., Johnston, A. W. B., Ding, J., Stuart, P. E., Xing, X., Kochkodan, J. J., et al. (2014) · Journal of Investigative Dermatology · doi:10.1038/jid.2014.28 · Cited by 396

An Expanded Maize Gene Expression Atlas based on RNA Sequencing and its Use to Explore Root Development

Stelpflug, S. C., Sekhon, R. S., Vaillancourt, B., Hirsch, C. N., Buell, C. R., de León, N., & Kaeppler, S. M. (2015) · The Plant Genome · doi:10.3835/plantgenome2015.04.0025 · Cited by 404

Toxicity of Metal Oxide Nanoparticles: Mechanisms, Characterization, and Avoiding Experimental Artefacts

Djurišić, A. B., Leung, Y. H., Ng, A. M. C., Xu, X. Y., Lee, P. K. H., Degger, N., & Wu, R. S. S. (2014) · Small · doi:10.1002/smll.201303947 · Cited by 404

Robustly detecting differential expression in RNA sequencing data using observation weights

Zhou, X., Lindsay, H., & Robinson, M. D. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku310 · Cited by 387

Function of lncRNAs and approaches to lncRNA-protein interactions

Zhu, J., Fu, H., Wu, Y., & Zheng, X. (2013) · Science China Life Sciences · doi:10.1007/s11427-013-4553-6 · Cited by 389

Abiotic Stress - Plant Responses and Applications in Agriculture

Vahdati, K., & Leslie, C. A. (2013) · InTech eBooks · doi:10.5772/45842 · Cited by 385

Web Apollo: a web-based genomic annotation editing platform

Lee, E., Helt, G. A., Reese, J., Muñoz-Torres, M. C., Childers, C. P., Buels, R., Stein, L. D., Holmes, I. H., Elsik, C. G., & Lewis, S. (2013) · Genome biology · doi:10.1186/gb-2013-14-8-r93 · Cited by 376

Fine mapping of genome activation in bovine embryos by RNA sequencing

Graf, A., Krebs, S., Zakhartchenko, V., Schwalb, B., Blum, H., & Wolf, E. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1321569111 · Cited by 390

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

Reuse of public genome-wide gene expression data

Rung, J., & Brāzma, A. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3394 · Cited by 383

A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package

Meng, J., Lu, Z., Liu, H., Zhang, L., Zhang, S., Chen, Y., Rao, M. K., & Huang, Y. (2014) · Methods · doi:10.1016/j.ymeth.2014.06.008 · Cited by 375

Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions

Evans, C., Hardin, J., & Stoebel, D. M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx008 · Cited by 368

Burden-driven feedback control of gene expression

Ceroni, F., Boo, A., Furini, S., Gorochowski, T. E., Borkowski, O., Ladak, Y., Awan, A. R., Gilbert, C., Stan, G.-B. V., & Ellis, T. (2018) · Nature Methods · doi:10.1038/nmeth.4635 · Cited by 375

Gene Mapping via Bulked Segregant RNA-Seq (BSR-Seq)

Liu, S., Yeh, C., Tang, H. M., Nettleton, D. S., & Schnable, P. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036406 · Cited by 379

Normalization, testing, and false discovery rate estimation for RNA-sequencing data

Li, J., Witten, D. M., Johnstone, I. M., & Tibshirani, R. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr031 · Cited by 369

Profiling gene expression responses of coral larvae (Acropora millepora) to elevated temperature and settlement inducers using a novel RNA-Seq procedure

Meyer, E., Aglyamova, G. V., & Matz, M. V. (2011) · Molecular Ecology · doi:10.1111/j.1365-294x.2011.05205.x · Cited by 379

Dynamic Transcriptome Landscape of Maize Embryo and Endosperm Development

Chen, J., Zeng, B., Zhang, M., Xie, S., Wang, G., Hauck, A. L., & Lai, J. (2014) · PLANT PHYSIOLOGY · doi:10.1104/pp.114.240689 · Cited by 367

A Virulent Strain of Deformed Wing Virus (DWV) of Honeybees (Apis mellifera) Prevails after Varroa destructor-Mediated, or In Vitro, Transmission

Ryabov, E. V., Wood, G. R., Fannon, J. M., Moore, J. D., Bull, J. C., Chandler, D., Mead, A., Burroughs, N. J., & Evans, D. J. (2014) · PLoS Pathogens · doi:10.1371/journal.ppat.1004230 · Cited by 363

CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains

Wright, P. R., Georg, J., Mann, M., Sorescu, D. A., Richter, A. S., Lott, S. C., Kleinkauf, R., Hess, W. R., & Backofen, R. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku359 · Cited by 364

The Expanding Landscape of Alternative Splicing Variation in Human Populations

Park, E., Pan, Z., Zhang, Z., Lin, L., & Xing, Y. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.11.002 · Cited by 385

RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings

Ren, S., Peng, Z., Mao, J., Yu, Y., Yin, C., Gao, X., Cui, Z., Zhang, J., Kang, Y., Xu, W., et al. (2012) · Cell Research · doi:10.1038/cr.2012.30 · Cited by 362

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

Next-generation sequencing for cancer diagnostics: a practical perspective.

Meldrum, C. J., Doyle, M., & Tothill, R. W. (2011) · Europe PMC (PubMed Central) · Cited by 356

Genome Editing: Targeting Susceptibility Genes for Plant Disease Resistance

Zaidi, S. S., Mukhtar, M. S., & Mansoor, S. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.04.005 · Cited by 357

TSEBRA: transcript selector for BRAKER

Gabriel, L., Hoff, K. J., Brůna, T., Borodovsky, M., & Stanke, M. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04482-0 · Cited by 410

A single-base resolution map of an archaeal transcriptomePreprint

Wurtzel, O., Sapra, R., Chen, F., Zhu, Y., Simmons, B. A., & Sorek, R. (2009) · Genome Research · doi:10.1101/gr.100396.109 · Cited by 356

The European technical report on aquatic effect-based monitoring tools under the water framework directive

Wernersson, A.-S., Carere, M., Maggi, C., Tušil, P., Soldán, P., James, A., Sánchez, W., Dulio, V., Broeg, K., Reifferscheid, G., et al. (2015) · Environmental Sciences Europe · doi:10.1186/s12302-015-0039-4 · Cited by 354

Perspectives on deciphering mechanisms underlying plant heat stress response and thermotolerance

Bokszczanin, K. Ł. (2013) · Frontiers in Plant Science · doi:10.3389/fpls.2013.00315 · Cited by 353

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

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

Targeted capture in evolutionary and ecological genomics

Jones, M. R., & Good, J. M. (2015) · Molecular Ecology · doi:10.1111/mec.13304 · Cited by 370

Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment

Skene, N., & Grant, S. G. N. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00016 · Cited by 345

Computational solutions for omics data

Berger, B., Peng, J., & Singh, M. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3433 · Cited by 353

Next-generation sequencing in the clinic: Promises and challenges

Xuan, J., Yu, Y., Qing, T., Guo, L., & Shi, L. M. (2012) · Cancer Letters · doi:10.1016/j.canlet.2012.11.025 · Cited by 355

Full‐length transcriptome sequences and splice variants obtained by a combination of sequencing platforms applied to different root tissues of S alvia miltiorrhiza and tanshinone biosynthesis

Xu, Z., Peters, R. J., Weirather, J. L., Luo, H., Liao, B., Zhang, X., Zhu, Y., Ji, A., Zhang, B., Hu, S., et al. (2015) · The Plant Journal · doi:10.1111/tpj.12865 · Cited by 345

Genetic, environmental, and epigenetic factors involved in CAKUT

Nicolaou, N., Renkema, K. Y., Bongers, E. M. H. F., Giles, R. H., & Knoers, N. V. A. M. (2015) · Nature Reviews Nephrology · doi:10.1038/nrneph.2015.140 · Cited by 344

CircNet: a database of circular RNAs derived from transcriptome sequencing data

Liu, Y., Li, J., Sun, C.-H., Andrews, E. H., Chao, R.-F., Lin, F.-M., Weng, S.-L., Hsu, S., Huang, C., Cheng, C., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv940 · Cited by 342

Characterization of the human ESC transcriptome by hybrid sequencing

Au, K. F., Sebastiano, V., Afshar, P. T., Durruthy, J. D., Lee, L., Williams, B. A., van Bakel, H., Schadt, E. E., Reijo-Pera, R. A., Underwood, J. G., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320101110 · Cited by 341

MORC Family ATPases Required for Heterochromatin Condensation and Gene Silencing

Moissiard, G., Cokus, S. J., Cary, J. B., Feng, S., Billi, A. C., Stroud, H., Husmann, D., Zhan, Y., Lajoie, B. R., McCord, R. P., et al. (2012) · Science · doi:10.1126/science.1221472 · Cited by 333

Optimization of de novo transcriptome assembly from next-generation sequencing data

Surget-Groba, Y., & Montoya-Burgos, J. I. (2010) · Genome Research · doi:10.1101/gr.103846.109 · Cited by 339

The Allen Human Brain Atlas

Shen, E., Overly, C. C., & Jones, A. R. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2012.09.005 · Cited by 333

Development and applications of single-cell transcriptome analysis

Tang, F., Lao, K., & Surani, M. A. (2011) · Nature Methods · doi:10.1038/nmeth.1557 · Cited by 342

Biomarkers in Psychiatry: Concept, Definition, Types and Relevance to the Clinical Reality

García-Gutiérrez, M. S., Navarrete, F., Sala, F., Gasparyan, A., Austrich-Olivares, A., & Manzanares, J. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00432 · Cited by 331

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi

Keilwagen, J., Hartung, F., Paulini, M., Twardziok, S. O., & Grau, J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2203-5 · Cited by 342

Recent advances in metabolic engineering of Saccharomyces cerevisiae: New tools and their applications

Lian, J., Mishra, S., & Zhao, H. (2018) · Metabolic Engineering · doi:10.1016/j.ymben.2018.04.011 · Cited by 342

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

Detection of splice junctions from paired-end RNA-seq data by SpliceMap

Au, K. F., Jiang, H., Lin, L., Xing, Y., & Wong, W. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq211 · Cited by 341

Neurotoxicity in acute and repeated organophosphate exposure

Naughton, S. X., & Terry, A. V. (2018) · Toxicology · doi:10.1016/j.tox.2018.08.011 · Cited by 335

Stability, delivery and functions of human sperm RNAs at fertilization

Sendler, E. D., Johnson, G. D., Mao, S., Goodrich, R. J., Diamond, M. P., Hauser, R. B., & Krawetz, S. A. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt132 · Cited by 338

Key players in the immune response to biomaterial scaffolds for regenerative medicine

Chung, L., Maestas, D. R., Housseau, F., & Elisseeff, J. H. (2017) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2017.07.006 · Cited by 333

Candida parapsilosis: from genes to the bedside

Tóth, R., Nosek, J., Mora-Montes, H. M., Gabaldon, T., Bliss, J. M., Nosanchuk, J. D., Turner, S. A., Butler, G., Vágvölgyi, C., & Gácser, A. (2019) · Clinical Microbiology Reviews · doi:10.1128/cmr.00111-18 · Cited by 327

Global transcriptome profiles of Camellia sinensis during cold acclimation

Wang, X., Zhao, Q., Ma, C.-L., Zhang, Z. H., Cao, H., Kong, Y., Yue, C., Hao, X., Chen, L., Ma, J., et al. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-415 · Cited by 324

Promise of personalized omics to precision medicine

Chen, R., & Snyder, M. P. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1198 · Cited by 338

A multivariate approach to the integration of multi-omics datasets

Meng, C., Küster, B., Culhane, A. C., & Gholami, A. M. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-162 · Cited by 336

RNA‐Seq: A Method for Comprehensive Transcriptome Analysis

Nagalakshmi, U., Waern, K., & Snyder, M. P. (2010) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb0411s89 · Cited by 327

Bulked sample analysis in genetics, genomics and crop improvement

Zou, C., Wang, P., & Xu, Y. (2016) · Plant Biotechnology Journal · doi:10.1111/pbi.12559 · Cited by 325

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

Recommendation on Design, Execution, and Reporting of Animal Atherosclerosis Studies: A Scientific Statement From the American Heart Association

Daugherty, A., Tall, A. R., Daemen, M. J. A. P., Falk, E., Fisher, E. A. A., García‐Cardeña, G., Lusis, A. J., Owens, A. P., Rosenfeld, M. E., & Virmani, R. (2017) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atv.0000000000000062 · Cited by 321

The use of skin models in drug development

Mathes, S. H., Ruffner, H., & Graf‐Hausner, U. (2014) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2013.12.006 · Cited by 328

Altered distributions of Gemini of coiled bodies and mitochondria in motor neurons of TDP-43 transgenic mice

Shan, X., Chiang, P., Price, D. L., & Wong, P. C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1003459107 · Cited by 323

Next-generation sequencing data interpretation: enhancing reproducibility and accessibility

Nekrutenko, A., & Taylor, J. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3305 · Cited by 328

FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing

Underwood, J. G., Uzilov, A. V., Katzman, S. J., Onodera, C. S., Mainzer, J. E., Mathews, D. H., Lowe, T. M., Salama, S. R., & Haussler, D. H. (2010) · Nature Methods · doi:10.1038/nmeth.1529 · Cited by 315

Historical perspective, development and applications of next-generation sequencing in plant virology

Barba, M., Czosnek, H., & Hadidi, A. (2014) · Viruses · doi:10.3390/v6010106 · Cited by 317

RNA-Seq identifies novel myocardial gene expression signatures of heart failure

Liu, Y., Morley, M. P., Brandimarto, J., Hannenhalli, S. S., Hu, Y., Ashley, E. A., Tang, W. H. W., Moravec, C. S., Margulies, K. B., Cappola, T. P., et al. (2014) · Genomics · doi:10.1016/j.ygeno.2014.12.002 · Cited by 324

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Liu, S., Wang, Z., Zhu, R., Wang, F., Cheng, Y., & Liu, Y. (2021) · Journal of Visualized Experiments · doi:10.3791/62528 · Cited by 315

BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud

Torre, D., Lachmann, A., & Ma’ayan, A. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.007 · Cited by 301

Non‐coding RNAs: Meet thy masters

Costa, F. F. (2010) · BioEssays · doi:10.1002/bies.200900112 · Cited by 314

Long noncoding RNAs in development and disease of the central nervous system

Ng, S., Lin, L., Soh, B.-S., & Stanton, L. W. (2013) · Trends in Genetics · doi:10.1016/j.tig.2013.03.002 · Cited by 314

Cloud-scale RNA-sequencing differential expression analysis with Myrna

Langmead, B., Hansen, K. D., & Leek, J. T. (2010) · Genome biology · doi:10.1186/gb-2010-11-8-r83 · Cited by 320

SNP Discovery through Next-Generation Sequencing and Its Applications

Kumar, S. D., Banks, T. W., & Cloutier, S. (2012) · International Journal of Plant Genomics · doi:10.1155/2012/831460 · Cited by 337

Clinical Validation of a Next-Generation Sequencing Screen for Mutational Hotspots in 46 Cancer-Related Genes

Singh, R. R., Patel, K. P., Routbort, M. J., Reddy, N. G., Barkoh, B. A., Handal, B. A., Kanagal‐Shamanna, R., Greaves, W. O., Medeiros, L. J., Aldape, K., et al. (2013) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2013.05.003 · Cited by 317

Direct and indirect effects of H-NS and Fis on global gene expression control in Escherichia coli

Kahramanoglou, C., Seshasayee, A. S. N., Prieto, A. I., Ibberson, D., Schmidt, S., Zimmermann, J., Beneš, V., Fraser, G. M., & Luscombe, N. M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq934 · Cited by 312

The Genetics of Chronic Itch: Gene Expression in the Skin of Patients with Atopic Dermatitis and Psoriasis with Severe Itch

Nattkemper, L. A., Tey, H. L., Valdés‐Rodríguez, R., Lee, H. Y., Mollanazar, N. K., Albornoz, C. A., Sanders, K. M., & Yosipovitch, G. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2017.12.029 · Cited by 310

The role of alternative splicing in cancer: From oncogenesis to drug resistance

Sciarrillo, R., Wojtuszkiewicz, A., Assaraf, Y. G., Jansen, G., Kaspers, G. J. L., Giovannetti, E., & Cloos, J. J. (2020) · Drug Resistance Updates · doi:10.1016/j.drup.2020.100728 · Cited by 317

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae

Nookaew, I., Papini, M., Pornputtapong, N., Scalcinati, G., Fagerberg, L., Uhlén, M., & Nielsen, J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks804 · Cited by 309

Cumulus and granulosa cell markers of oocyte and embryo quality

Uyar, A., Torrealday, S., & Seli, E. U. (2013) · Fertility and Sterility · doi:10.1016/j.fertnstert.2013.01.129 · Cited by 317

Transcribed dark matter: meaning or myth?

Ponting, C. P., & Belgard, T. G. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq362 · Cited by 315

High‐throughput sequencing for biology and medicine

Soon, W. W., Hariharan, M., & Snyder, M. P. (2013) · Molecular Systems Biology · doi:10.1038/msb.2012.61 · Cited by 313

Systems biology in cardiovascular disease: a multiomics approach

Joshi, A., Rienks, M., Theofilatos, K., & Mayr, M. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00477-1 · Cited by 309

Genotyping‐by‐sequencing approaches to characterize crop genomes: choosing the right tool for the right application

Scheben, A., Batley, J., & Edwards, D. (2016) · Plant Biotechnology Journal · doi:10.1111/pbi.12645 · Cited by 323

Expression Profile of Ectopic Olfactory Receptors Determined by Deep Sequencing

Flegel, C., Manteniotis, S., Osthold, S., Hatt, H. H., & Gisselmann, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055368 · Cited by 306

Implementing personalized cancer genomics in clinical trials

Simon, R. M., & Roychowdhury, S. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd3979 · Cited by 309

Moving micronutrients from the soil to the seeds: Genes and physiological processes from a biofortification perspective

Waters, B. M., & Sankaran, R. P. (2010) · Plant Science · doi:10.1016/j.plantsci.2010.12.003 · Cited by 301

Advanced Biotechnology: Metabolically Engineered Cells for the Bio‐Based Production of Chemicals and Fuels, Materials, and Health‐Care Products

Becker, J., & Wittmann, C. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201409033 · Cited by 295

Vitamin D Receptor Signaling Mechanisms: Integrated Actions of a Well-Defined Transcription Factor

Carlberg, C., & Campbell, M. J. (2013) · Steroids · doi:10.1016/j.steroids.2012.10.019 · Cited by 293

Simultaneous generation of many RNA-seq libraries in a single reaction

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