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

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Systems biology approaches to new vaccine development

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Shaping development through mechanical strain: the transcriptional basis of diet‐induced phenotypic plasticity in a cichlid fish

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Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

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Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

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Genomic toolboxes for conservation biologists

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Current RNA-seq methodology reporting limits reproducibility

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A Survey for Novel Imprinted Genes in the Mouse Placenta by mRNA-seq

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Dual RNA-Sequencing to Elucidate the Plant-Pathogen Duel

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RNA‐seq Data: Challenges in and Recommendations for Experimental Design and Analysis

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A MYB Triad Controls Primary and Phenylpropanoid Metabolites for Pollen Coat Patterning

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RNA-Seq analysis of resistant and susceptible potato varieties during the early stages of potato virus Y infection

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Transcriptional profiling of the Arabidopsis abscission mutant hae hsl2by RNA-Seq

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Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models

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Variation in RNA-Seq Transcriptome Profiles of Peripheral Whole Blood from Healthy Individuals with and without Globin Depletion

Shin, H., Shannon, C. P., Fishbane, N., Ruan, J., Zhou, M., Balshaw, R. F., Wilson-McManus, J., Ng, R. T., McManus, B. M., & Tebbutt, S. J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091041 · Cited by 89 · see citing works

Transcriptomics analysis revealing candidate networks and genes for the body size sexual dimorphism of Chinese tongue sole (Cynoglossus semilaevis)

Wang, N., Wang, R., Wang, R., & Chen, S. (2018) · Functional & Integrative Genomics · doi:10.1007/s10142-018-0595-y · Cited by 88 · see citing works

Effects of library size variance, sparsity, and compositionality on the analysis of microbiome dataPreprint

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Impact of low dose oral exposure to bisphenol A (BPA) on the neonatal rat hypothalamic and hippocampal transcriptome: a clarity-BPA consortium study

Arambula, S. E., Belcher, S. M., Planchart, A., Turner, S. D., & Patisaul, H. B. (2016) · Endocrinology · doi:10.1210/en.2016-1339 · Cited by 87 · see citing works

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 · see citing works

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

Comparison of differential accessibility analysis strategies for ATAC-seq data

Gontarz, P. M., Fu, S., Xing, X., Liu, S., Miao, B., Bazylianska, V., SHARMA, A. K., Madden, P. A. F., Cates, K. L., Yoo, A. S. J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-66998-4 · Cited by 80 · see citing works

Microbiome of Deep Dentinal Caries from Reversible Pulpitis to Irreversible Pulpitis

Zheng, J., Zhou, W., Niu, K., Xie, Y., Hu, X., Fu, J., Tian, D., Fu, K., Zhao, B., Kong, W., et al. (2019) · Journal of Endodontics · doi:10.1016/j.joen.2018.11.017 · Cited by 79 · see citing works

RNA-Sequencing: A tool to explore new frontiers in animal genetics

Wickramasinghe, S., Cánovas, Á., Rincón, G., & Medrano, J. F. F. (2014) · Livestock Science · doi:10.1016/j.livsci.2014.06.015 · Cited by 79 · see citing works

Phylogenetic Analysis of Gene Expression

Dunn, C. W., Luo, X., & Wu, Z. (2013) · Integrative and Comparative Biology · doi:10.1093/icb/ict068 · Cited by 78 · see citing works

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 · see citing works

Next-Generation Sequencing for MicroRNA Expression Profile

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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

Omics Approaches for the Assessment of Biological Responses to Nanoparticles.

Abdelkader, Y., Pérez-Dávalos, L., Leduc, R., Zahedi, R., & Labouta, H. (2023) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2023.114992 · Cited by 62 · see citing works

RNA-Seq Provides New Insights in the Transcriptome Responses Induced by the Carcinogen Benzo[a]pyrene

van Delft, J. H. M., Gaj, S., Lienhard, M., Albrecht, M. W., Кирпий, А. А., Brauers, K. J. J., Claessen, S. M. H., Lizárraga, D., Lehrach, H. R., Herwig, R., et al. (2012) · Toxicological Sciences · doi:10.1093/toxsci/kfs250 · Cited by 74 · see citing works

Dual RNA-seq of Parasite and Host Reveals Gene Expression Dynamics during Filarial Worm–Mosquito Interactions

Choi, Y.-J., Aliota, M. T., Mayhew, G. F., Erickson, S. M., & Christensen, B. M. (2014) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0002905 · Cited by 72 · see citing works

Dynamics of the chili pepper transcriptome during fruit development

Martínez-López, L. A., Ochoa‐Alejo, N., & Martínez, O. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-143 · Cited by 71 · see citing works

RNA-Seq analysis identifies genes associated with differential reproductive success under drought-stress in accessions of wild barley Hordeum spontaneum

Hübner, S., Korol, A. B., & Schmid, K. J. (2015) · BMC Plant Biology · doi:10.1186/s12870-015-0528-z · Cited by 70 · see citing works

Multi-study inference of regulatory networks for more accurate models of gene regulation

Castro, D. M., Veaux, N. D., Miraldi, E. R., & Bonneau, R. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006591 · Cited by 69 · see citing works

Mycobacterial infection induces a specific human innate immune response

Blischak, J., Tailleux, L., Mitrano, A., Barreiro, L. B., & Gilad, Y. (2015) · Scientific Reports · doi:10.1038/srep16882 · Cited by 65 · see citing works

Analysis of gene expression changes in peach leaves in response to P lum pox virus infection using RNA ‐ S eq

Rubio, M., Rodríguez‐Moreno, L., Ballester, A., de Moura, M. C., Bonghi, C., Candresse, T., & Martínez-Gómez, P. (2014) · Molecular Plant Pathology · doi:10.1111/mpp.12169 · Cited by 66 · see citing works

RNA-seq Transcriptional Profiling of Peripheral Blood Leukocytes from Cattle Infected with Mycobacterium bovis

McLoughlin, K. E., Nalpas, N., Rue-Albrecht, K., Browne, J. A., Magee, D. A., Killick, K. E., Park, S. D. E., Hokamp, K., Meade, K. G., O’Farrelly, C., et al. (2014) · Frontiers in Immunology · doi:10.3389/fimmu.2014.00396 · Cited by 67 · see citing works

Limited sex-biased neural gene expression patterns across strains in Zebrafish (Danio rerio)

Wong, R. Y., McLeod, M. M., & Godwin, J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-905 · Cited by 68 · see citing works

RNA-Seq Analysis of Abdominal Fat in Genetically Fat and Lean Chickens Highlights a Divergence in Expression of Genes Controlling Adiposity, Hemostasis, and Lipid Metabolism

Resnyk, C., Chen, C., Huang, H., Wu, C. H., Simon, J., Bihan-Duval, É. L., Duclos, M. J. M. J., & Cogburn, L. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139549 · Cited by 65 · see citing works

LEVERAGING BIOLOGICAL REPLICATES TO IMPROVE ANALYSIS IN CHIP-SEQ EXPERIMENTS

Yang, Y., Fear, J. M., Hu, J., Häecker, I., Zhou, L. J., Renne, R., Bloom, D. C., & McIntyre, L. M. (2014) · Computational and Structural Biotechnology Journal · doi:10.5936/csbj.201401002 · Cited by 62 · see citing works

So, you want to use next-generation sequencing in marine systems? Insight from the Pan-Pacific Advanced Studies Institute

Willette, D. A., Allendorf, F. W., Barber, P. H., Barshis, D. J., Carpenter, K. E., Crandall, E. D., Cresko, W. A., Fernández-Silva, I., Matz, M. V., Meyer, E., et al. (2014) · Bulletin of Marine Science · doi:10.5343/bms.2013.1008 · Cited by 63 · see citing works

Transcriptome changes specifically associated with apple (Malus domestica) root defense response during Pythium ultimum infection

Shin, S., Zheng, P., Fazio, G., Mazzola, M., Main, D., & Zhu, Y. (2016) · Physiological and Molecular Plant Pathology · doi:10.1016/j.pmpp.2016.03.003 · Cited by 63 · see citing works

Transcriptomic responses to environmental temperature by turtles with temperature-dependent and genotypic sex determination assessed by RNAseq inform the genetic architecture of embryonic gonadal development

Radhakrishnan, S., Literman, R., Neuwald, J. L., Severin, A. J., & Valenzuela, N. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172044 · Cited by 63 · see citing works

miRNA expression profiles and molecular networks in resting and LPS-activated BV-2 microglia—Effect of cannabinoids

Juknat, A., Gao, F., Coppola, G., Vogel, Z., & Kozela, E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212039 · Cited by 63 · see citing works

Comprehensive identification and quantification of microbial transcriptomes by genome-wide unbiased methods

Mäder, U., Nicolas, P., Richard, H., Bessières, P., & Aymerich, S. (2010) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2010.10.003 · Cited by 61 · see citing works

Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology

Fumagalli, D., Blanchet-Cohen, A., Brown, D. N., Desmedt, C., Gacquer, D., Michiels, S., Rothé, F., Majjaj, S., Salgado, R., Larsimont, D., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-1008 · Cited by 59 · see citing works

Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders

Hernandez, L. M., Kim, M., Hoftman, G. D., Haney, J. R., de la Torre-Ubieta, L., Paşaniuc, B., & Gandal, M. J. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.005 · Cited by 57 · see citing works

Meta‐analysis and meta‐regression of transcriptomic responses to water stress in Arabidopsis

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Glia Open Access Database ( GOAD ): A comprehensive gene expression encyclopedia of glia cells in health and disease

Holtman, I. R., Noback, M. A., Bijlsma, M., Duong, K. N., van der Geest, M. A., Ketelaars, P. T., Brouwer, N., Vainchtein, I. D., Eggen, B. J. L., & Boddeke, H. W. G. M. (2015) · Glia · doi:10.1002/glia.22810 · Cited by 57 · see citing works

A comparison of analog and Next-Generation transcriptomic tools for mammalian studies

Roy, N. C., Altermann, E., Park, Z. A., & McNabb, W. C. (2011) · Briefings in Functional Genomics · doi:10.1093/bfgp/elr005 · Cited by 58 · see citing works

Transcriptome Analysis in Domesticated Species: Challenges and Strategies

Hekman, J. P., Johnson, J. L., & Kukekova, A. V. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s29334 · Cited by 59 · see citing works

ISSUE HIGHLIGHTS

(2010) · Genetics · doi:10.1093/genetics/185.2.np · Cited by 1 · see citing works

Design of RNA Sequencing Experiments

Nettleton, D. S. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_5 · Cited by 5 · see citing works

Design and validation issues in RNA-seq experiments

Fang, Z., & Cui, X. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr004 · Cited by 229 · see citing works

Biological Perspectives of RNA-Sequencing Experimental Design

Pasmanik‐Chor, M. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1103-6_17 · Cited by 2 · see citing works

Modeling and Analysis of Next-Generation Sequencing Data

Lin, S., Scholtens, D. M., & Datta, S. (2022) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315153728-5