Statistical Design and Analysis of RNA Sequencing Data

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Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

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Design of Experiments

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Accurate whole human genome sequencing using reversible terminator chemistry

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The Significance of Digital Gene Expression ProfilesPreprint

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CEDER: Accurate Detection of Differentially Expressed Genes by Combining Significance of Exons Using RNA-Seq

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Gene Expression Analysis Using Conventional and Imaging Methods

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RNA-seq in the tetraploid Xenopus laevis enables genome-wide insight in a classic developmental biology model organism

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Exploring the shallow end; estimating information content in transcriptomics studies

Kliebenstein, D. J. (2012) · Frontiers in Plant Science · doi:10.3389/fpls.2012.00213 · Cited by 16

The diverse applications of RNA‐seq for functional genomic studies in Aspergillus fumigatus

Rokas, A., Gibbons, J. G., Zhou, X., Beauvais, A., & Latgé, J. (2012) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2012.06755.x · Cited by 17

Yeast Systems Biology: The Challenge of Eukaryotic Complexity

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

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Transcriptomic landscape of prophase I sunflower male meiocytes

Florez-Zapata, N. M. V., Reyes-Valdés, M. H., Hernández-Godínez, F., & Martínez, O. (2014) · Frontiers in Plant Science · doi:10.3389/fpls.2014.00277 · Cited by 16

Power analysis for RNA-Seq differential expression studies using generalized linear mixed effects models

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Single‐Nucleus RNA‐Sequencing in Brain Tissue

Waag, R., & Bohacek, J. (2023) · Current Protocols · doi:10.1002/cpz1.919 · Cited by 15

iSeg: an efficient algorithm for segmentation of genomic and epigenomic data

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Bioinformatics as a Tool for the Structural and Evolutionary Analysis of Proteins

Hernández-Domínguez, E. M., Castillo-Ortega, L. S., García-Esquivel, Y., Mandujano-González, V., Dı́az-Godı́nez, G., & Álvarez-Cervantes, J. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.89594 · Cited by 17

Limited Gene Misregulation Is Exacerbated by Allele-Specific Upregulation in Lethal Hybrids between Drosophila melanogaster and Drosophila simulans

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Morphological Convergence Between an Allopolyploid and One of its Parental Species Correlates with Biased Gene Expression and DNA Loss

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Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

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NGS-Trex: Next Generation Sequencing Transcriptome profile explorer

Boria, I., Boatti, L., Pesole, G., & Mignone, F. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s7-s10 · Cited by 14

Next generation quantitative genetics in plants

Jiménez‐Gómez, J. M. (2011) · Frontiers in Plant Science · doi:10.3389/fpls.2011.00077 · Cited by 16

A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data

Lozoya, O. A., Santos, J. H., & Woychik, R. P. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00176 · Cited by 14

Identification of Circulating Gene Expression Signatures of Intracranial Aneurysm in Peripheral Blood Mononuclear Cells

Tutino, V. M., Zebraski, H. R., Rajabzadeh-Oghaz, H., Waqas, M., Jarvis, J. N., Bach, K., Mokin, M. V., Snyder, K. V., Siddiqui, A. H., & Poppenberg, K. E. (2021) · Diagnostics · doi:10.3390/diagnostics11061092 · Cited by 14

Clustering high-throughput sequencing data with Poisson mixture models

Rau, A., Celeux, G., Martin‐Magniette, M., & Maugis-Rabusseau, C. (2011) · HAL · Cited by 14

Biological Averaging in RNA-SeqPreprint

Biswas, S., Agrawal, Y., Mucyn, T. S., Dangl, J. L., & Jones, C. D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.0670 · Cited by 14

Expression profiling of the retina of pde6c, a zebrafish model of retinal degeneration

Zhang, L., Zhang, X., Zhang, G., Pang, C. P., Leung, Y. F., Zhang, M., & Zhong, W. (2017) · Scientific Data · doi:10.1038/sdata.2017.182 · Cited by 14

Conservation and Divergence of the Trihelix Genes in Brassica and Expression Profiles of BnaTH Genes in Brassica napus under Abiotic Stresses

Zhang, C., Lu, L., Gong, R., Su, X., Liu, F., Zhang, R., & Hu, J. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232415766 · Cited by 13

Changes in C57BL6 Mouse Hippocampal Transcriptome Induced by Hypergravity Mimic Acute Corticosterone-Induced Stress

Pulga, A., Porte, Y., & Morel, J. (2016) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2016.00153 · Cited by 13

RNA-seq analysis of impact of PNN on gene expression and alternative splicing in corneal epithelial cells.

Akin, D., Newman, J. R. B., McIntyre, L. M., & Sugrue, S. P. (2016) · Europe PMC (PubMed Central) · Cited by 13

What Is the Transcriptome and How it is Evaluated?

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Transcriptomic analysis identifies a role of PI3K–Akt signalling in the responses of skeletal muscle to acute hypoxia in vivo

Gan, Z., Powell, F. L., Zambon, A. C., Buchholz, K. S., Fu, Z., Ocorr, K. A., Bodmer, R., Moya, E. A., Stowe, J. C., Haddad, G. G., et al. (2017) · The Journal of Physiology · doi:10.1113/jp274556 · Cited by 13

Whole-Transcriptome Sequencing for High-Resolution Transcriptomic Analysis in Mycobacterium tuberculosis

Benjak, A., Sala, C., & Hartkoorn, R. C. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2450-9_2 · Cited by 12

Replicates, Read Numbers, and Other Important Experimental Design Considerations for Microbial RNA-seq Identified Using Bacillus thuringiensis Datasets

Manga, P., Klingeman, D. M., Lu, T.-Y. S., Mehlhorn, T. L., Pelletier, D. A., Hauser, L., Wilson, C. M., & Brown, S. D. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.00794 · Cited by 12

Hydrogel scaffolds promote neural gene expression and structural reorganization in human astrocyte cultures

Knight, V. B., & Serrano, E. E. (2017) · PeerJ · doi:10.7717/peerj.2829 · Cited by 13

Transcriptomic variation of the flower–fruit transition in Physalis and Solanum

Gao, H., Li, J., Wang, L., Zhang, J., & He, C. (2020) · Planta · doi:10.1007/s00425-020-03434-x · Cited by 12

Assessing the validity and reproducibility of genome-scale predictions

Sugden, L. A., Tackett, M., Savva, Y. A., Thompson, W. A., & Lawrence, C. E. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt508 · Cited by 11

Quality assessment of long read data in multisample lrRNA-seq experiments using SQANTI-readsPreprint

Keil, N., Monzó, C., McIntyre, L. M., & Conesa, A. (2025) · Genome Research · doi:10.1101/gr.280021.124 · Cited by 11

Study Design for Sequencing Studies

Honaas, L. A., Altman, N., & Krzywinski, M. I. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3578-9_3 · Cited by 11

RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling

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Differential Expression for RNA Sequencing (RNA-Seq) Data: Mapping, Summarization, Statistical Analysis, and Experimental Design

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Rapid flow-sorting to simultaneously resolve multiplex massively parallel sequencing products

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FQStat: a parallel architecture for very high-speed assessment of sequencing quality metrics

Chanumolu, S. K., Albahrani, M., & Otu, H. H. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3015-y · Cited by 10

Statistical strategies for microRNAseq batch effect reduction.

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Uniformly consistently estimating the proportion of false null hypotheses via Lebesgue–Stieltjes integral equations

Chen, X. (2019) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2019.06.003 · Cited by 9

RNA-Seq as a Tool to Study the Tumor Microenvironment

Panichnantakul, P., Bourgey, M., Montpetit, A., Bourque, G., & Riazalhosseini, Y. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3801-8_22 · Cited by 9

Identification and verification of seed development related miRNAs in kernel almond by small RNA sequencing and qPCR

Jafari, M., Shiran, B., Rabiei, G., Ravash, R., Tabatabaei, B. E. S., & Martínez-Gómez, P. (2021) · PLoS ONE · doi:10.1371/journal.pone.0260492 · Cited by 8

Functional Analysis of Sporophytic Transcripts Repressed by the Female Gametophyte in the Ovule of Arabidopsis thaliana

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Marginal likelihood estimation of negative binomial parameters with applications to RNA-seq data

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Gene network variation and alternative paths to convergent evolution in turtles

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A Polyketide Synthetase Gene Cluster Is Responsible for Antibacterial Activity of Burkholderia contaminans MS14

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Transcriptome Analysis of Ten-DPA Fiber in an Upland Cotton (Gossypium hirsutum) Line with Improved Fiber Traits from Phytochrome A1 RNAi Plants

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Cloud-Based Tools for Next-Generation Sequencing Data Analysis

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A comparison of strategies for generating artificial replicates in RNA-seq experiments

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Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

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In the setting of β-cell stress, the pancreatic duct gland transcriptome shows characteristics of an activated regenerative response

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Unveiling Tissue‐Specific RNA Landscapes in Mouse Organs During Fasting and Feeding Using Nanopore Direct RNA Sequencing

Jiang, C., Li, P., & Cao, H. (2024) · Advanced Science · doi:10.1002/advs.202408054 · Cited by 6

Comparison of direct cDNA and PCR-cDNA Nanopore sequencing of RNA from Escherichia coli isolates

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Experimental Designs for Next Generation Phenotyping

Peternelli, L. A., & de Resende, M. D. V. (2015) · Springer eBooks · doi:10.1007/978-3-319-13677-6_2 · Cited by 6

Comprehensive analysis of differences of N6-methyladenosine of lncRNAs between atrazine-induced and normal Xenopus laevis testis

Qi, X., Geng, X., Zhang, J., Qu, B., Zhang, X., Jia, Q., Yin, W., Bo, C., Liu, Y., Li, H., et al. (2021) · Genes and Environment · doi:10.1186/s41021-021-00223-0 · Cited by 6

CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients

Jin, Y., Arimura, H., Cui, Y., Kodama, T., Mizuno, S., & Ansai, S. (2023) · Cancers · doi:10.3390/cancers15082220 · Cited by 6

A Bayes testing approach to metagenomic profiling in bacteria

Clarke, B. S., Valdes, C., Dobra, A., & Clarke, J. (2015) · Statistics and Its Interface · doi:10.4310/sii.2015.v8.n2.a5 · Cited by 6

Screening the Molecular Framework Underlying Local Dendritic mRNA Translation

Namjoshi, S. V., & Raab‐Graham, K. F. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00045 · Cited by 7

Plant Genetic Engineering and GM Crops: Merits and Demerits

Parray, J. A., Mir, M. Y., & Shameem, N. (2019) · Springer eBooks · doi:10.1007/978-981-13-8840-8_4 · Cited by 6

Transcriptional analysis of cleft palate in TGFβ3 mutant mice

Liu, J., Chanumolu, S. K., White, K. M., Albahrani, M., Otu, H. H., & Nawshad, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-71636-0 · Cited by 6

Sequencing dropout-and-batch effect normalization for single-cell mRNA profiles: a survey and comparative analysis

Lan, T., Hutvágner, G., Lan, Q., Liu, T., & Li, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa248 · Cited by 7

Benchmarking association analyses of continuous exposures with RNA-seq in observational studies

Sofer, T., Kurniansyah, N., Aguet, F., Ardlie, K., Durda, P., Nickerson, D. A., Smith, J. D., Liu, Y., Gharib, S. A., Redline, S. S., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab194 · Cited by 5

From sequence to information

Popa, O., Oldenburg, E., & Ebenhöh, O. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0448 · Cited by 6

A Bayesian test to identify variance effectsPreprint

Dumitrascu, B., Darnell, G., Ayroles, J. F., & Engelhardt, B. E. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.01616 · Cited by 6

Molecular Identification and Phylogenic Analysis of Phosphate Solubilizing Bacteria Aneurinibacillus migulanus From Rhizosphere Imperata Cylindrica, Karst Citatah, Bandung Barat, Jawa Barat, Indonesia

Hafsari, A. R., & Purnawan, L. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1665/1/012006 · Cited by 5

Design of RNA Sequencing Experiments

Nettleton, D. S. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_5 · Cited by 6

RNASeq in C. elegans Following Manganese Exposure

Parmalee, N. L., Maqbool, S. B., Ye, B., Calder, B. R., Bowman, A. B., & Aschner, M. (2015) · Current Protocols in Toxicology · doi:10.1002/0471140856.tx1120s65 · Cited by 5

A Beginner’s Guide on Integrating *Omics Approaches to Study Marine Microbial Communities: Details and Discussions From Sample Collection to Bioinformatics Analysis

Wilms, S. N. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.692538 · Cited by 5

A transcriptomics approach to understanding polymorphic and transcript level differences linked to isoquinoline alkaloid production in triploid varieties of narcissus pseudonarcissus

Pulman, J. A. (2015) · University of Liverpool · doi:10.17638/02006379 · Cited by 5

Differential gene expression is not required for facultative sex allocation: a transcriptome analysis of brain tissue in the parasitoid waspNasonia vitripennis

Cook, N., Boulton, R. A., Green, J., Trivedi, U. H., Tauber, E., Pannebakker, B. A., Ritchie, M. G., & Shuker, D. M. (2018) · Royal Society Open Science · doi:10.1098/rsos.171718 · Cited by 5

Development and Characterization of EST-SSR Markers for the Endangered Tree Magnolia patungensis (Magnoliaceae)

Wagutu, G. K., Fan, X., Njeri, H. K., Wen, X., Liu, Y., & Chen, Y. (2020) · Annales Botanici Fennici · doi:10.5735/085.057.0114 · Cited by 5

SQANTI-reads: a tool for the quality assessment of long read data in multi-sample lrRNA-seq experimentsPreprint

Keil, N., Monzó, C., McIntyre, L. M., & Conesa, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.23.609463 · Cited by 5

Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets

Sprang, M., Möllmann, J. S., Andrade-Navarro, M. A., & Fontaine, J. (2024) · Genome biology · doi:10.1186/s13059-024-03331-6 · Cited by 4

Transcriptomic analysis identifies candidate genes for shell colour formation in the Venus clamCyclina sinensis

Wei, M., Zhang, M., Wu, Y., Wang, Y., Qiu, P., Zhang, J., Wu, L., Song, J., Sun, Y., Tan, L., et al. (2022) · Journal of Molluscan Studies · doi:10.1093/mollus/eyac005 · Cited by 4

Bayesian Hierarchical Model for Differential Gene Expression Using RNA-Seq Data

Lee, J. H., Ji, Y., Liang, S., Cai, G., & Müller, P. (2013) · Statistics in Biosciences · doi:10.1007/s12561-013-9096-7 · Cited by 4

Statistical modeling for differential transcriptome analysis using RNA-Seq technology

Miecznikowski, J. C., Liu, S., & Ren, X. (2012) · Journal of Solid Tumors · doi:10.5430/jst.v2n5p33 · Cited by 4

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

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Preparation of single cell suspensions enriched in mouse brain vascular cells for single-cell RNA sequencing

Yamazaki, A., Shue, F., Yamazaki, Y., Martens, Y. A., Bu, G., & Liu, C. (2021) · STAR Protocols · doi:10.1016/j.xpro.2021.100715 · Cited by 4

Tandem Oligonucleotide Probe Annealing and Elongation To Discriminate Viral Sequence

Taskova, M., Uhd, J., Miotke, L. K., Kubit, M. A., Bell, J., Ji, H. P., & Astakhova, K. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b00646 · Cited by 4

REGULAÇÃO GÊNICA DA VIA AMPK PELO EXERCÍCIO FÍSICO: REVISÃO SISTEMÁTICA E ANÁLISE IN SILICO

Pacheco, C., dos Santos, L. H. P., Alves, J. O., de Queiroz, A. N., Soares, P. M., & Ceccatto, V. M. (2017) · Revista Brasileira de Medicina do Esporte · doi:10.1590/1517-869220172304169935 · Cited by 4

Transcriptomic similarities and differences between the limb bud AER and unique carapacial ridge of turtle embryos

Cordero, G. A. (2020) · Evolution & Development · doi:10.1111/ede.12351 · Cited by 4

Challenges Analyzing RNA-Seq Gene Expression Data

López-Kleine, L., & González-Prieto, C. (2016) · Open Journal of Statistics · doi:10.4236/ojs.2016.64053 · Cited by 4

GENE SET TESTING TO CHARACTERIZE MULTIVARIATELY DIFFERENTIALLY EXPRESSED GENES

Stevens, J. R., & Isom, S. C. (2012) · Conference on Applied Statistics in Agriculture · doi:10.4148/2475-7772.1032 · Cited by 4

Correction: RNA-Seq Analysis of the Sclerotinia homoeocarpa – Creeping Bentgrass Pathosystem

Orshinsky, A. M., Hu, J., Opiyo, S. O., Reddyvari-Channarayappa, V., Mitchell, T. K., & Boehm, M. J. (2012) · PLoS ONE · doi:10.1371/annotation/36af97b5-137b-4629-946b-748b63438b03 · Cited by 4

Expression Profiling and Discovery of microRNA

Rajesh, N., Gupta, M. K., Donde, R., Sabarinathan, S., Gouda, G., Dash, G. K., Ponnana, M., Behera, L., & Vadde, R. (2021) · Springer eBooks · doi:10.1007/978-981-16-3993-7_20 · Cited by 3

Wood transcriptome analysis and expression variation of lignin biosynthetic pathway transcripts in Ailanthus excelsa Roxb., a multi-purpose tropical tree species

Dasgupta, M. G., Parveen, A. B. M., Rajasugunasekar, D., & Ulaganathan, K. (2021) · Journal of Biosciences · doi:10.1007/s12038-021-00218-7 · Cited by 3

What Is the Transcriptome and How It Is Evaluated

Assis, A. F., de Oliveira, E. H. D., Donate, P. B., Giuliatti, S., Nguyen, C., & Passos, G. A. S. (2022) · Springer eBooks · doi:10.1007/978-3-030-87821-4_1 · Cited by 5

RNA-seq: herramienta transcriptómica útil para el estudio de interacciones planta-patógeno

Sedano, J. C. S., & López, C. E. (2012) · Redalyc (Universidad Autónoma del Estado de México) · Cited by 3

Simultaneous confidence intervals of estimable functions based on quasi-likelihood in generalized linear models for over-dispersed data

Li, B. (2020) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2020.1807548 · Cited by 3

Understanding the Transcriptomic Landscape to Drive New Innovations in Musculoskeletal Regenerative Medicine

Thomas, S. M., Ackert‐Bicknell, C. L., Zuscik, M. J., & Payne, K. A. (2022) · Current Osteoporosis Reports · doi:10.1007/s11914-022-00726-x · Cited by 5

Assessing Numerical Dependence in Gene Expression Summaries with the Jackknife Expression Difference

Stevens, J. R., & Nicholas, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039570 · Cited by 3

Gene Expression Analysis Using Conventional and Imaging Methods

Kadam, U. S., Lossie, A. C., Schulz, B., & Irudayaraj, J. (2013) · Springer eBooks · doi:10.1007/978-3-662-45775-7_6 · Cited by 3

Identification and correction of time-series transcriptomic anomalies

Campione, S. A., Kelliher, C., Roth, C., Cho, C.-Y., Deckard, A., Motta, F. C., & Haase, S. B. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf524 · Cited by 2

Single-Cell and Regional Gene Expression Analysis in Alzheimer’s Disease

Kwong, R., Lupton, M. K., & Janitz, M. (2012) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-012-9797-3 · Cited by 2

Nucleic Acid Profiling in Tumor Exosomes

Trivedi, M. S., & Abreu, M. M. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812774-2.00006-7 · Cited by 2

Minority Gene Expression Profiling: Probing the Genetic Signatures of Pathogenesis Using Ribosome Profiling

Vila‐Sanjurjo, A., Juárez, D., Loyola, S., Torres, M., & Leguía, M. (2019) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiz565 · Cited by 2

RNA Sequencing: A Potent Transcription Profiling Tool

Kaisa, K., Kumar, H., Panigrahi, M., Dutt, T., & Bhushan, B. (2020) · International Journal of Current Microbiology and Applied Sciences · doi:10.20546/ijcmas.2020.910.107 · Cited by 1

Unbiased transcriptome analysis of human cleft palate reveals evolutionally conserved molecular signatures of development: experimental study

Jo, T., Kim, J., Choi, J., Kim, J., & Jeong, W. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001841 · Cited by 2

A tail-based test to detect differential expression in RNA-sequencing data

Chen, J., Mi, X., Ning, J., He, X., & Hu, J. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220951907 · Cited by 2

ISSUE HIGHLIGHTS

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

Design of RNA Sequencing Experiments

Nettleton, D. S. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_5 · Cited by 6

Design and validation issues in RNA-seq experiments

Fang, Z., & Cui, X. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr004 · Cited by 243

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

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