RNA-Seq: a revolutionary tool for transcriptomics

Mapping and quantifying mammalian transcriptomes by RNA-Seq

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Serial Analysis of Gene Expression

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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

Temporal Specification and Bilaterality of Human Neocortical Topographic Gene Expression

Pletikos, M., Sousa, A. M. M., Sedmak, G., Meyer, K. A., Zhu, Y., Cheng, F., Li, M., Kawasawa, Y. I., & Šestan, N. (2014) · Neuron · doi:10.1016/j.neuron.2013.11.018 · Cited by 228

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228

Fusion between Intestinal Epithelial Cells and Macrophages in a Cancer Context Results in Nuclear Reprogramming

Powell, A. E., Anderson, E. C., Davies, P. S., Silk, A. D., Pelz, C., Impey, S., & Wong, M. H. (2011) · Cancer Research · doi:10.1158/0008-5472.can-10-3223 · Cited by 228

What determines growth potential and juvenile quality of farmed fish species?

Valente, L. M. P., Moutou, K. A., Conceição, L. E. C., Engrola, S. A. D., Fernandes, J. M. O., & Johnston, I. A. (2013) · Reviews in Aquaculture · doi:10.1111/raq.12020 · Cited by 232

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Lu, M., & Zhan, X. (2018) · The EPMA Journal · doi:10.1007/s13167-018-0128-8 · Cited by 235

Sequencing technology does not eliminate biological variability

Hansen, K. D., Wu, Z., Irizarry, R. A., & Leek, J. T. (2011) · Nature Biotechnology · doi:10.1038/nbt.1910 · Cited by 226

Quantitative Imaging with Fluorescent Biosensors

Okumoto, S., Jones, A. M., & Frommer, W. B. (2012) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-042110-103745 · Cited by 224

GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data

Chen, L., Reeve, J., Zhang, L., Huang, S., Wang, X., & Chen, J. (2018) · PeerJ · doi:10.7717/peerj.4600 · Cited by 225

Genome-wide effects on Escherichia coli transcription from ppGpp binding to its two sites on RNA polymerase

Sanchez‐Vazquez, P., Dewey, C. N., Kitten, N., Ross, W., & Gourse, R. L. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1819682116 · Cited by 223

Current achievements and future prospects in the genetic breeding of chrysanthemum: a review

Su, J., Jiang, J., Zhang, F., Liu, Y., Ding, L., Chen, S., & Chen, F. (2019) · Horticulture Research · doi:10.1038/s41438-019-0193-8 · Cited by 224

Diagnostic utility of transcriptome sequencing for rare Mendelian diseases

Lee, H., Huang, A., Wang, L.-K., Yoon, A. J., Renteria, G., Eskin, A., Signer, R. H., Dorrani, N., Nieves‐Rodriguez, S., Wan, J., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0672-1 · Cited by 229

Deep sequencing-based discovery of the Chlamydia trachomatis transcriptome

Albrecht, M., Sharma, C. M., Reinhardt, R., Vogel, J., & Rudel, T. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp1032 · Cited by 223

Differential damage and repair of DNA-adducts induced by anti-cancer drug cisplatin across mouse organs

Yimit, A., Adebalı, O., Sancar, A., & Jiang, Y. (2019) · Nature Communications · doi:10.1038/s41467-019-08290-2 · Cited by 234

From single cells to deep phenotypes in cancer

Bendall, S. C., & Nolan, G. P. (2012) · Nature Biotechnology · doi:10.1038/nbt.2283 · Cited by 225

New viruses for cancer therapy: meeting clinical needs

Miest, T. S., & Cattaneo, R. (2013) · Nature Reviews Microbiology · doi:10.1038/nrmicro3140 · Cited by 222

Evaluating Methods for Isolating Total RNA and Predicting the Success of Sequencing Phylogenetically Diverse Plant Transcriptomes

Johnson, M. T. J., Carpenter, E. J., Tian, Z., Bruskiewich, R., Burris, J. N., Carrigan, C. T., Chase, M. W., Clarke, N. D., Covshoff, S., dePamphilis, C. W., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050226 · Cited by 218

Next-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications

Kulski, J. K. (2016) · InTech eBooks · doi:10.5772/61964 · Cited by 248

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 235

The human testis-specific proteome defined by transcriptomics and antibody-based profiling

Djureinovic, D., Fagerberg, L., Hallström, B. M., Danielsson, A., Lindskog, C., Uhlén, M., & Pontén, F. (2014) · Molecular Human Reproduction · doi:10.1093/molehr/gau018 · Cited by 219

Systems biology primer: the basic methods and approaches

Tavassoly, I., Goldfarb, J., & Iyengar, R. (2018) · Essays in Biochemistry · doi:10.1042/ebc20180003 · Cited by 228

Targeted enrichment strategies for next‐generation plant biology

Cronn, R., Knaus, B. J., Liston, A., Maughan, P. J., Parks, M., Syring, J. V., & Udall, J. A. (2012) · American Journal of Botany · doi:10.3732/ajb.1100356 · Cited by 218

Accurate assembly of transcripts through phase-preserving graph decomposition

Shao, M., & Kingsford, C. (2017) · Nature Biotechnology · doi:10.1038/nbt.4020 · Cited by 221

Feasibility, limitation and possible solutions of RNAi‐based technology for insect pest control

Zhang, H., Li, H., & Miao, X. (2012) · Insect Science · doi:10.1111/j.1744-7917.2012.01513.x · Cited by 229

The design and analysis of transposon insertion sequencing experiments

Chao, M. C., Abel, S., Davis, B. M., & Waldor, M. K. (2016) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2015.7 · Cited by 221

Genetic assimilation: a review of its potential proximate causes and evolutionary consequences

Ehrenreich, I. M., & Pfennig, D. W. (2015) · Annals of Botany · doi:10.1093/aob/mcv130 · Cited by 212

Perspectives on ENCODE

Abascal, F., Acosta, R., Addleman, N. J., Adrian, J., Afzal, V., Aken, B. L., Ai, R., Akiyama, J. A., Jammal, O. A., Amrhein, H., et al. (2020) · Nature · doi:10.1038/s41586-020-2449-8 · Cited by 225

Deep sequencing of the zebrafish transcriptome response to mycobacterium infection

Hegedűs, Z., Zakrzewska, A., Ágoston, V. C., Ordas, A., Rácz, P., Mink, M., Spaink, H. P., & Meijer, A. H. (2009) · Molecular Immunology · doi:10.1016/j.molimm.2009.07.002 · Cited by 216

Spatially resolved transcriptome profiling in model plant species

Giacomello, S., Salmén, F., Terebieniec, B. K., Vicković, S., Navarro, J. F., Alexeyenko, A., Reimegård, J., McKee, L. S., Mannapperuma, C., Bulone, V., et al. (2017) · Nature Plants · doi:10.1038/nplants.2017.61 · Cited by 214

Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion

Zhao, S., Zhang, Y., Gamini, R., Zhang, B., & von Schack, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-23226-4 · Cited by 234

Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons

on behalf of the MATCH study group, Smid, M., van den Braak, R. R. J. C., van de Werken, H. J. G., van Riet, J., van Galen, A., de Weerd, V., van der Vlugt‐Daane, M., Bril, S. I., Lalmahomed, Z. S., et al. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2246-7 · Cited by 217

Application of Next Generation Sequencing in Laboratory Medicine

Zhong, Y., Xu, F., Wu, J., Schubert, J. A., & Li, M. M. (2020) · Annals of Laboratory Medicine · doi:10.3343/alm.2021.41.1.25 · Cited by 231

A molecular cell atlas of the human lung from single cell RNA sequencingPreprint

Travaglini, K. J., Nabhan, A. N., Penland, L., Sinha, R., Gillich, A., Sit, R. V., Chang, S., Conley, S. D., Mori, Y., Seita, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/742320 · Cited by 216

Phylogenomics — principles, opportunities and pitfalls of big‐data phylogenetics

Young, A. D., & Gillung, J. P. (2019) · Systematic Entomology · doi:10.1111/syen.12406 · Cited by 238

Ensuring Reproduction at High Temperatures: The Heat Stress Response during Anther and Pollen Development

Giorno, F., Wolters‐Arts, M., Mariani, C., & Rieu, I. (2013) · Plants · doi:10.3390/plants2030489 · Cited by 214

Rate of meristem maturation determines inflorescence architecture in tomato

Park, S. J., Jiang, K., Schatz, M. C., & Lippman, Z. B. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114963109 · Cited by 214

Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?

Meyer, S. U., Pfaffl, M. W., & Ulbrich, S. E. (2010) · Biotechnology Letters · doi:10.1007/s10529-010-0380-z · Cited by 217

Transcriptional profiles of bovine in vivo pre-implantation development

Jiang, Z., Sun, J., Dong, H., Luo, O. J., Zheng, X., Obergfell, C. J., Tang, Y., Bi, J., O'Neill, R. J. W., Ruan, Y., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-756 · Cited by 215

Transcriptome analysis in different rice cultivars provides novel insights into desiccation and salinity stress responses

Shankar, R., Bhattacharjee, A., & Jain, M. (2016) · Scientific Reports · doi:10.1038/srep23719 · Cited by 212

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

Tebani, A., Afonso, C., Marret, S., & Bekri, S. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17091555 · Cited by 228

Statistical Significance for Hierarchical Clustering

Kimes, P. K., Liu, Y., Hayes, D. N., & Marron, J. S. (2017) · Biometrics · doi:10.1111/biom.12647 · Cited by 213

Integrative Personal Omics Profiles during Periods of Weight Gain and Loss

Piening, B., Zhou, W., Contrepois, K., Röst, H. L., Gu, G. J., Mishra, T., Hanson, B., Bautista, E. J., Leopold, S. R., Yeh, C. Y., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2017.12.013 · Cited by 212

Analysis of Transcriptome Complexity Through RNA Sequencing in Normal and Failing Murine Hearts

Lee, J.-H., Gao, C., Peng, G., Greer, C., Ren, S., Wang, Y., & Xiao, X. (2011) · Circulation Research · doi:10.1161/circresaha.111.249433 · Cited by 209

Bioinformatics and Systems Biology of the Lipidome

Subramaniam, S., Fahy, E., Gupta, S., Sud, M., Byrnes, R. W., Cotter, D., Dinasarapu, A. R., & Maurya, M. R. (2011) · Chemical Reviews · doi:10.1021/cr200295k · Cited by 218

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

Suravajhala, P., Kogelman, L. J. A., & Kadarmideen, H. N. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0217-x · Cited by 232

Cross-tissue Analysis of Gene and Protein Expression in Normal and Cancer Tissues

Kosti, I., Jain, N., Aran, D., Butte, A. J., & Sirota, M. (2016) · Scientific Reports · doi:10.1038/srep24799 · Cited by 210

Trends in Next-Generation Sequencing and a New Era for Whole Genome Sequencing

Park, S. T., & Kim, J. (2016) · International Neurourology Journal · doi:10.5213/inj.1632742.371 · Cited by 233

The Dimensions, Dynamics, and Relevance of the Mammalian Noncoding Transcriptome

Deveson, I. W., Hardwick, S. A., Mercer, T. R., & Mattick, J. S. (2017) · Trends in Genetics · doi:10.1016/j.tig.2017.04.004 · Cited by 215

VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Cornwell, M., Vangala, M., Taing, L., Herbert, Z. T., Köster, J., Li, B., Sun, H., Li, T., Zhang, J., Qiu, X., et al. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2139-9 · Cited by 207

Comprehensive annotation of the transcriptome of the human fungal pathogen Candida albicans using RNA-seqPreprint

Bruno, V. M., Wang, Z., Marjani, S. L., Euskirchen, G. M., Martin, J. V., Sherlock, G., & Snyder, M. P. (2010) · Genome Research · doi:10.1101/gr.109553.110 · Cited by 212

Predictive and prognostic molecular markers for cancer medicine

Mehta, S. Y., Shelling, A. N., Muthukaruppan, A., Lasham, A., Blenkiron, C., Laking, G. R., & Print, C. G. (2010) · Therapeutic Advances in Medical Oncology · doi:10.1177/1758834009360519 · Cited by 213

Genome-wide annotation of genes and noncoding RNAs of foxtail millet in response to simulated drought stress by deep sequencing

Qi, X., Xie, S., Liu, Y., Yi, F., & Yu, J. (2013) · Plant Molecular Biology · doi:10.1007/s11103-013-0104-6 · Cited by 209

The emerging role of circular RNAs in transcriptome regulation

Huang, S., Yang, B., Chen, B. J., Bliim, N., Ueberham, U., Arendt, T., & Janitz, M. (2017) · Genomics · doi:10.1016/j.ygeno.2017.06.005 · Cited by 208

i-cisTarget: an integrative genomics method for the prediction of regulatory features and cis-regulatory modules

Herrmann, C., de Sande, B. V., Potier, D., & Aerts, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks543 · Cited by 211

Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis

Der, E. B., Ranabothu, S., Suryawanshi, H., Akat, K. M., Clancy, R. M., Morozov, P. S., Kustagi, M., Czuppa, M., Izmirly, P., Belmont, M. H., et al. (2017) · JCI Insight · doi:10.1172/jci.insight.93009 · Cited by 212

Sizing and shaping the nucleus: mechanisms and significance

Jevtić, P., Edens, L. J., Vukovic, L. D., & Levy, D. L. (2014) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2014.01.003 · Cited by 204

Biases in small RNA deep sequencing data

Raabe, C. A., Tang, T., Brosius, J., & Rozhdestvensky, T. S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1021 · Cited by 206

Use of Whole-Exome Sequencing for Diagnosis of Limb-Girdle Muscular Dystrophy

Ghaoui, R., Cooper, S. T., Lek, M., Jones, K., Corbett, A. J., Reddel, S., Needham, M., Liang, C., Waddell, L. B., Nicholson, G. A., et al. (2015) · JAMA Neurology · doi:10.1001/jamaneurol.2015.2274 · Cited by 213

Next generation sequencing of microbial transcriptomes: challenges and opportunities

van Vliet, A. H. M. (2009) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2009.01767.x · Cited by 204

Thermostable group II intron reverse transcriptase fusion proteins and their use in cDNA synthesis and next-generation RNA sequencing

Mohr, S., Ghanem, E., Smith, W., Sheeter, D. A., Qin, Y., King, O., Polioudakis, D., Iyer, V. R., Hunicke‐Smith, S., Swamy, S., et al. (2013) · RNA · doi:10.1261/rna.039743.113 · Cited by 209

Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data

Kim, J. K., & Marioni, J. C. (2013) · Genome biology · doi:10.1186/gb-2013-14-1-r7 · Cited by 216

Epigenetics and its role in male infertility

Dada, R., Kumar, M., Jesudasan, R. A., Fernández, J. L., Gosálvez, J., & Agarwal, A. K. (2012) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-012-9715-0 · Cited by 217

The Functional Consequences of Variation in Transcription Factor Binding

Cusanovich, D. A., Pavlovic, B. J., Pritchard, J. K., & Gilad, Y. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004226 · Cited by 209

The Medicago truncatula gene expression atlas web server

He, J., Benedito, V. A., Wang, M., Murray, J. D., Zhao, P. X., Tang, Y., & Udvardi, M. K. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-441 · Cited by 205

RNA Sequencing of the Human Milk Fat Layer Transcriptome Reveals Distinct Gene Expression Profiles at Three Stages of Lactation

Lemay, D. G., Ballard, O., Hughes, M., Morrow, A. L., Horseman, N. D., & Nommsen‐Rivers, L. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067531 · Cited by 205

Potential role of multiple carbon fixation pathways during lipid accumulation in Phaeodactylum tricornutum

Valenzuela, J. J., Mazurie, A., Carlson, R. P., Gerlach, R., Cooksey, K. E., Peyton, B., & Fields, M. W. (2012) · Biotechnology for Biofuels · doi:10.1186/1754-6834-5-40 · Cited by 204

Identification and Properties of 1,119 Candidate LincRNA Loci in the Drosophila melanogaster Genome

Young, R. S., Marques, A. C., Tibbit, C., Haerty, W., Bassett, A. R., Liu, J., & Ponting, C. P. (2012) · Genome Biology and Evolution · doi:10.1093/gbe/evs020 · Cited by 203

Emerging Functions of Circular RNAs.

Cortés-López, M., & Miura, P. (2016) · Europe PMC (PubMed Central) · Cited by 204

RNA sequencing shows no dosage compensation of the active X-chromosome

Xiong, Y., Chen, X., Chen, Z., Wang, X., Shi, S., Wang, X., Zhang, J., & He, X. (2010) · Nature Genetics · doi:10.1038/ng.711 · Cited by 208

Systems Biology in Immunology: A Computational Modeling Perspective

Germain, R. N., Meier‐Schellersheim, M., Nita‐Lazar, A., & Fraser, I. D. C. (2011) · Annual Review of Immunology · doi:10.1146/annurev-immunol-030409-101317 · Cited by 208

A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data

Skelly, D. A., Johansson, M., Madeoy, J., Wakefield, J., & Akey, J. M. (2011) · Genome Research · doi:10.1101/gr.119784.110 · Cited by 203

Transcriptome profiling of chemosensory appendages in the malaria vector Anopheles gambiae reveals tissue- and sex-specific signatures of odor coding

Pitts, R. J., Rinker, D. C., Jones, P. L., Rokas, A., & Zwiebel, L. J. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-271 · Cited by 199

Functional Annotation of Animal Genomes (FAANG): Current Achievements and Roadmap

Giuffra, E., & Tuggle, C. K. (2018) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-020518-114913 · Cited by 210

MicroRNAs in learning, memory, and neurological diseases: Figure 1.Preprint

Wang, W., Kwon, E. J., & Tsai, L. (2012) · Learning & Memory · doi:10.1101/lm.026492.112 · Cited by 210

Experimental approaches for the discovery and characterization of regulatory small RNA

Sharma, C. M., & Vogel, J. (2009) · Current Opinion in Microbiology · doi:10.1016/j.mib.2009.07.006 · Cited by 199

Integration strategy of network pharmacology in Traditional Chinese Medicine: a narrative review.

Wu, J., Fangqing, Z., Zhuangzhuang, L. I., Weiyi, J., & Yue, S. (2022) · PubMed Central · doi:10.19852/j.cnki.jtcm.20220408.003 · Cited by 213

The NIH Roadmap Epigenomics Program data Resource

Chadwick, L. H. (2012) · Epigenomics · doi:10.2217/epi.12.18 · Cited by 193

Mechanisms of specificity in neuronal activity-regulated gene transcription

Lyons, M. R., & West, A. E. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.05.003 · Cited by 209

Epigenetic mechanisms in diabetic vascular complications

Reddy, M. A., & Natarajan, R. D. (2011) · Cardiovascular Research · doi:10.1093/cvr/cvr024 · Cited by 202

Improving photosynthesis for algal biofuels: toward a green revolution

Stephenson, P. G., Moore, C. M., Terry, M. J., Zubkov, M. V., & Bibby, T. S. (2011) · Trends in biotechnology · doi:10.1016/j.tibtech.2011.06.005 · Cited by 197

Molecular approaches unravel the mechanism of acid soil tolerance in plants

Bian, M., Zhou, M., Sun, D., & Li, C. (2013) · The Crop Journal · doi:10.1016/j.cj.2013.08.002 · Cited by 202

Chronic cocaine-regulated epigenomic changes in mouse nucleus accumbens

Feng, J., Wilkinson, M., Liu, X., Purushothaman, I., Ferguson, D., Vialou, V., Maze, I., Shao, N., Kennedy, P., Koo, J., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-4-r65 · Cited by 198

Identifying differentially expressed transcripts from RNA-seq data with biological variation

Glaus, P., Honkela, A., & Rattray, M. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts260 · Cited by 199

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Yang, Y., Sun, H., Zhang, Y., Zhang, T., Gong, J., Wei, Y., Duan, Y., Shu, M., Yang, Y., Wu, D., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109442 · Cited by 203

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

Second-Generation Sequencing Supply an Effective Way to Screen RNAi Targets in Large Scale for Potential Application in Pest Insect Control

Wang, Y., Zhang, H., Li, H., & Miao, X. (2011) · PLoS ONE · doi:10.1371/journal.pone.0018644 · Cited by 195

Deep sequencing analysis of the Methanosarcina mazei Gö1 transcriptome in response to nitrogen availability

Jäger, D., Sharma, C. M., Thomsen, J. K., Ehlers, C., Vogel, J., & Schmitz, R. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909051106 · Cited by 197

Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data

Yip, S. H., Sham, P. C., & Wang, J. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby011 · Cited by 201

The genetics of drought tolerance in conifers

Moran, E., Lauder, J., Musser, C., Stathos, A. M., & Shu, M. (2017) · New Phytologist · doi:10.1111/nph.14774 · Cited by 196

Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections

Salmén, F., Ståhl, P. L., Mollbrink, A., Navarro, J. F., Vicković, S., Frisén, J., & Lundeberg, J. (2018) · Nature Protocols · doi:10.1038/s41596-018-0045-2 · Cited by 196

Protective Effects of Natural Polysaccharides on Intestinal Barrier Injury: A Review

Huo, J., Wu, Z., Sun, W., Wang, Z., Wu, J., Huang, M., Wang, B., & Sun, B. (2022) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.1c05966 · Cited by 197

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