Moderated statistical tests for assessing differences in tag abundance

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

Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829

In silico prediction of protein-protein interactions in human macrophages

Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785

Significance analysis of microarrays applied to the ionizing radiation response

Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568

Genome sequencing in microfabricated high-density picolitre reactors

Margulies, M., Egholm, M., Altman, W. E., Attiya, S., Bader, J. S., Bemben, L. A., Berka, J., Braverman, M. S., Chen, Y.-J., Chen, Z., et al. (2005) · Nature · doi:10.1038/nature03959 · Cited by 7,693

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

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

Brenner, S., Johnson, M., Bridgham, J., Golda, G., Lloyd, D. H., Johnson, D., Luo, S., McCurdy, S., Foy, M., Ewan, M., et al. (2000) · Nature Biotechnology · doi:10.1038/76469 · Cited by 1,360

Gene Expression Profiles in Normal and Cancer Cells

Zhang, L., Zhou, W., Velculescu, V. E., Kern, S. E., Hruban, R. H., Hamilton, S. R., Vogelstein, B., & Kinzler, K. W. (1997) · Science · doi:10.1126/science.276.5316.1268 · Cited by 1,186

Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation

Lu, P., Vogel, C., Wang, R., Yao, X., & Marcotte, E. M. (2007) · Nature Biotechnology · doi:10.1038/nbt1270 · Cited by 1,139

Small-sample estimation of negative binomial dispersion, with applications to SAGE data

Robinson, M. D., & Smyth, G. K. (2007) · Biostatistics · doi:10.1093/biostatistics/kxm030 · Cited by 1,073

The colorectal microRNAome

Cummins, J. M., He, Y., Leary, R. J., Pagliarini, R., Diaz, L. A., Sjoblom, T., Barad, O., Bentwich, Z., Szafranska, A. E., Labourier, E., et al. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0511155103 · Cited by 744

Analysis Considerations for Utilizing RNA-Seq to Characterize the Brain Transcriptome

ZHENG, C. L., Kawane, S., Bottomly, D., & Wilmot, B. (2014) · International review of neurobiology · doi:10.1016/b978-0-12-801105-8.00002-3 · Cited by 4

Modified Significance Analysis of Microarrays in Heterogeneous Diseases

Tzeng, I. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11020062 · Cited by 4

Sequence Based Gene Expression Analysis

Matukumalli, L. K., & Schroeder, S. (2009) · Springer eBooks · doi:10.1007/978-0-387-92738-1_9 · Cited by 4

The Poisson Margin Test for Normalization-Free Significance Analysis of NGS Data

Kowalczyk, A., Bedő, J., Conway, T., & Beresford‐Smith, B. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2010.0272 · Cited by 4

Differential Gene Expression Profiling in Bed Bug (Cimex Lectularius L.) Fed on Ibuprofen and Caffeine in Reconstituted Human Blood

Narain, R. B., & Kamble, H. W. S. T. (2015) · Entomology Ornithology & Herpetology Current Research · doi:10.4172/2161-0983.1000160 · Cited by 4

Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets

Tichy, D., Pickl, J. M. A., Benner, A., & Sültmann, H. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx032 · Cited by 4

The Junction Usage Model (JUM): A method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patternsPreprint

Wang, Q., & Rio, D. C. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/116863 · Cited by 4

Inferring the genetic architecture of expression variation from replicated high throughput allele-specific expression experimentsPreprint

Zhang, X., & Emerson, J. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/699074 · Cited by 4

An endoribonuclease of the YicC-like family delays sporulation via sRNA degradation in Clostridioides difficile

Martins, D., Salgueiro, B. A., Sobral, D., Gragera, M., Hensel, Z., Henriques, A. O., Romão, C. V., & Serrano, M. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf644 · Cited by 4

Inflammation rewires the enteric nervous system through neurogenic monocyte recruitment

Kurapati, S., Shin, C., Szabo, K., Liu, Y., Ashraf, A. Z., Koscsó, B., Dash, C. P., Kruckow, K. L., Navarro, L. G., Clark, A. M., et al. (2025) · The Journal of Experimental Medicine · doi:10.1084/jem.20251761 · Cited by 4

Emc1 is essential for vision and zebrafish photoreceptor outer segment morphogenesis

McCann, T., Sundaramurthi, H., Walsh, C. M., Virdi, S., Álvarez, Y. Á., Sapetto-Rebow, B., Collery, R. F., Carter, S. P., Moran, A. L., Mulholland, R., et al. (2024) · The FASEB Journal · doi:10.1096/fj.202401977r · Cited by 3

Robust adjustment of sequence tag abundance

Baumann, D. D., & Doerge, R. W. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt575 · Cited by 3

Modern Approaches for Transcriptome Analyses in Plants

Riaño‐Pachón, D. M., Espitia-Navarro, H. F., Riascos, J. J., & Margarido, G. R. A. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-80352-0_2 · Cited by 4

Correlation analysis of the oral mucosal microbiome and diabetes mellitus using microbial DNA in elderly male subjects

Lee, J.-H., Lee, Y., Choi, S., Bae, E., & Lee, D. (2022) · Oral Biology Research · doi:10.21851/obr.46.01.202203.10 · Cited by 3

Jeffreys Prior for Negative Binomial and Zero Inflated Negative Binomial Distributions

Maity, A. K., & Paul, E. (2022) · Sankhya A · doi:10.1007/s13171-022-00286-3 · Cited by 3

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

Priol, C. L., Azencott, C., & Gidrol, X. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010342 · Cited by 3

Inhibition of BPHL inhibits proliferation in lung carcinoma cell lines

Ren, P., Zhai, J., Wang, X., Yin, Y., Lin, Z., Cai, K., & Wang, H. (2023) · Translational Lung Cancer Research · doi:10.21037/tlcr-23-225 · Cited by 3

Virus induced gene silencing confirms oligogenic inheritance of brown stem rot resistance in soybean

McCabe, C. E., Lincoln, L. M., O’Rourke, J. A., & Graham, M. A. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1292605 · Cited by 3

Heterologous expression of coffee HB12 confers tolerance to water deficit in transgenic plants through an ABA-independent route

Cruz, F. P., Loh, R., Arcuri, M. D. L. C., Dezar, C. A., Arge, L. W., Falcão, T. C. D. A., Romanel, E. A. D. C., Morgante, C. V., Cerqueira, J. V., Ribeiro, T. P., et al. (2024) · Environmental and Experimental Botany · doi:10.1016/j.envexpbot.2024.105983 · Cited by 3

Transcriptomic Profiling of the Allorecognition Response to Grafting in the Demosponge Amphimedon queenslandica

Grice, L. F., & Degnan, B. M. (2017) · Marine Drugs · doi:10.3390/md15050136 · Cited by 3

Detecting differentially expressed genes for syndromes by considering change in mean and dispersion simultaneously

Ma, C., & Ji, T. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2354-4 · Cited by 3

A Method Based on Differential Entropy-Like Function for Detecting Differentially Expressed Genes Across Multiple Conditions in RNA-Seq Studies

Wang, Z., Jin, S., & Zhang, C. (2019) · Entropy · doi:10.3390/e21030242 · Cited by 3

Risk Analysis of Blocked Rate Predictions for SDN Load Balancing Using Monte Carlo Simulation

Alawadi, A. H., & Molnár, S. (2019) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc47284.2019.8969746 · Cited by 3

Edge and modular significance assessment in individual-specific networks

Melograna, F., Li, Z., Galazzo, G., van Best, N., Mommers, M., Penders, J., Stella, F. A., & Steen, K. V. (2023) · Scientific Reports · doi:10.1038/s41598-023-34759-8 · Cited by 3

Grouped False-Discovery Rate for Removing the Gene-set-Level Bias of RNA-seq

Yang, T. Y., & Jeong, S. (2013) · Evolutionary Bioinformatics · doi:10.4137/ebo.s13099 · Cited by 3

tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities

Miok, V., Wilting, S. M., van de Wiel, M. A., Jaspers, A., van Noort, P. I., Brakenhoff, R. H., Snijders, P. J. F., Steenbergen, R. D. M., & van Wieringen, W. N. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-327 · Cited by 3

Exploring the feasibility of next-generation sequencing and microarray data meta-analysis

Wu, P.-Y., Phan, J. H., & Wang, M. D. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091877 · Cited by 3

Mapping and differential expression analysis from short‐read RNA‐Seq data in model organisms

Zhao, Q., Gratten, J., Restuadi, R., & Li, X. (2016) · Quantitative Biology · doi:10.1007/s40484-016-0060-7 · Cited by 5

A Markov chain representation of the multiple testing problem

Cabras, S. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216628903 · Cited by 3

Zero-Inflated Models for RNA-Seq Count Data

Alam, M., Mahi, N. A., & Begum, M. (2018) · Journal of Biomedical Analytics · doi:10.30577/jba.2018.v1n2.23 · Cited by 3

Gene expression is stable in a complete CIB1 knockout keratinocyte model

Imahorn, E., Aushev, M., Herms, S., Hoffmann, P., Cichon, S., Reichelt, J., Itin, P. H., & Burger, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-71889-9 · Cited by 3

The Poisson Margin Test for Normalisation Free Significance Analysis of NGS Data

Kowalczyk, A., Bedő, J., Conway, T., & Beresford‐Smith, B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12683-3_19 · Cited by 3

Differential gene expression analysis using coexpression and RNA-Seq data

Yang, E.-W., Girkes, T., & Jaing, T. (2013) · IEEE International Conference on Computational Advances in Bio and Medical Sciences · doi:10.1109/iccabs.2013.6629222 · Cited by 3

SCPattern: A statistical approach to identify and classify expression changes in single cell RNA-seq experiments with ordered conditionsPreprint

Leng, N., Chu, L., Choi, J., Kendziorski, C. M., Thomson, J. A., & Stewart, R. M. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/046110 · Cited by 3

Analysis of Allele‐Specific Expression

Li, Y., Chen, A., & Liu, Z. (2017) · Wiley eBooks · doi:10.1002/9781118782392.ch14 · Cited by 3

Inhibition of Lipin lipid phosphatase hyperactivity rescues TorsinA neurological diseasePreprint

Cascalho, A. C. C., Foroozandeh, J., Hennebel, L., Klein, C., Rous, S., Gonzalez, B. D., Pisani, A., Meringolo, M., Gallego, S. F., Verstreken, P., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/606947 · Cited by 3

PFDN6 contributes to colorectal cancer progression via transcriptional regulation

Xu, F., Kong, L., Sun, X., Hui, W., Jiang, L., Han, W., Xiao, Z., Li, N., Chen, D., Zheng, N., et al. (2024) · eGastroenterology · doi:10.1136/egastro-2023-100001 · Cited by 2

High-resolution deep mutational scanning of the melanocortin-4 receptor enables target characterization for drug discovery

Howard, C. J., Abell, N. S., Osuna, B. A., Jones, E. M., Chan, L. Y., Chan, H. W.-S., Artis, D. R., Asfaha, J. B., Bloom, J. S., Cooper, A. R., et al. (2024) · eLife · doi:10.7554/elife.104725.3 · Cited by 26

What Do Single-Cell Models Already Know About Perturbations?

Bjerregaard, A., Prada-Luengo, I., Das, V., & Krogh, A. (2025) · Genes · doi:10.3390/genes16121439 · Cited by 2

Adaptation across an extreme elevational gradient in Andean leaf-eared mice, the world’s highest-dwelling mammal

Liphardt, S. W., Bautista, N. M., Quiroga‐Carmona, M., Herrera, N. D., Blumer, M., Opazo, J. C., Hoffmann, F. G., Saleem, R., Somo, D. A., del Basto, F., et al. (2026) · Science · doi:10.1126/science.aec8347 · Cited by 2

A Double-weighted Normalization Method for Identifying Differential Expression of RNA-seq Data

Wu, X., 吴小惠, Zhao, C., & Su, Y. (2014) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 2

Epioncogene Networks: Identification of Epigenomic and Transcriptomic Cooperation by Multi-omics Integration of ChIP-Seq and RNA-Seq Data

Filipp, F. V. (2018) · RNA technologies · doi:10.1007/978-3-319-92967-5_7 · Cited by 2

Detecting differential expression from RNA-seq data with expression measurement uncertainty

Zhang, L., Chen, S., & Liu, X. (2015) · Frontiers of Computer Science · doi:10.1007/s11704-015-4308-6 · Cited by 2

Spatial transcriptomics of a giant pilomatricoma

Patil, A. T., Bennett, D. D., Xu, J. Y., Weisman, P. S., & Matson, D. R. (2023) · Journal of Cutaneous Pathology · doi:10.1111/cup.14524 · Cited by 2

Simplicity DiffExpress: A Bespoke Cloud-Based Interface for RNA-seq Differential Expression Modeling and Analysis

Palu, C. C., Alves, M. R., Wu, Y., Lawlor, B., Baranov, P. V., Kelly, B., & Walsh, P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00356 · Cited by 2

Gamma-based clustering via ordered means with application to gene-expression analysis

Newton, M. A., & Chung, L. M. (2010) · The Annals of Statistics · doi:10.1214/10-aos805 · Cited by 2

Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

Li, B., Li, Y., & Qin, Z. S. (2016) · Statistics in Biosciences · doi:10.1007/s12561-016-9156-x · Cited by 2

Using CAGE Data for Quantitative Expression

de Hoon, M., Bertin, N., & Chalk, A. M. (2019) · doi:10.1201/9780429065750-9 · Cited by 2

A two-step integrated approach to detect differentially expressed genes in RNA-Seq data

Mahi, N. A., & Begum, M. (2016) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720016500347 · Cited by 2

How to use the DEGseq Package

Wang, L., & Wang, X. (2016) · Cited by 2

Multiparity activates interferon pathways in peritoneal adipose tissue and decreases susceptibility to ovarian cancer metastasis in a murine allograft model

Loughran, E. A., Phan, R., Leonard, A. K., Tarwater, L., Asem, M., Liu, Y., Yang, J., Klymenko, Y., Johnson, J. J., Shi, Z., et al. (2017) · Cancer Letters · doi:10.1016/j.canlet.2017.09.028 · Cited by 2

Varying dietary protein and fat elicits differential transcriptomic expression within stress response pathways in preweaned Holstein heifers

Owens, C. E., Geiger, A. J., Akers, R. M., & Cockrum, R. (2018) · Journal of Dairy Science · doi:10.3168/jds.2018-14468 · Cited by 2

A Dynamic Transcriptional Analysis Reveals IL-6 Axis as a Prominent Mediator of Surgical Acute Response in Non-ischemic Mouse Heart

Badawi, S., Paccalet, A., Harhous, Z., Pillot, B., Augeul, L., Coppenolle, F. V., Lachuer, J., Kurdi, M., Silva, C. C. D., Ovize, M., et al. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.01370 · Cited by 2

A Statistical Method for Detecting Differentially Expressed SNVs Based on Next-Generation RNA-Seq Data

Fu, R., Wang, P., Ma, W., Taguchi, A., Wong, C., Zhang, Q., Gazdar, A. F., Hanash, S., Zhou, Q., Zhong, H., et al. (2016) · Biometrics · doi:10.1111/biom.12548 · Cited by 2

Visualization of Biological Data

Hochheiser, H., Eliceiri, K. W., & Goldberg, I. (2007) · Wiley eBooks · doi:10.1002/9783527619368.ch43 · Cited by 2

Control of stem-cell niche establishment in Arabidopsis flowers by REVOLUTA and the LEAFY-RAX1 modulePreprint

Denay, G., Tichtinsky, G., Masson, M. L., Chahtane, H., Sylvie, H., López‐Vidriero, I., Wenzl, C., Franco‐Zorrilla, J. M., Rüdiger, S., Lohmann, J. U., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/488114 · Cited by 2

Nutritional inter-dependencies and a carbazole-dioxygenase are key elements of a bacterial consortium relying on a Sphingomonas for the degradation of the fungicide thiabendazolePreprint

Vasileiadis, S., Perruchon, C., Benjamin, S., Adrian, L., Nicole, S., Trevisan, M., Plaza‐Bolaños, P., Agüera, A., Chatzinotas, A., & Karpouzas, D. G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.30.015693 · Cited by 2

Integration of Protein Interactome Networks With Congenital Heart Disease Variants Reveals Candidate Disease GenesPreprint

González‐Terán, B., Pittman, M., Felix, F., Richmond-Buccola, D., Thomas, R., Choudhary, K., Moroni, E., Colombo, G., Alexanian, M., Cole, B., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3737640 · Cited by 2

Statistical Tests for Detecting Differential RNA-Transcript Expression from Read CountsPreprint

Drewe, P., Stegle, O., Drewe, P., Bohnert, R., Borgwardt, K. M., & Rätsch, G. (2010) · Nature Precedings · doi:10.1038/npre.2010.4437 · Cited by 2

Power analysis of cell-type deconvolution methods across tissuesPreprint

Pournara, A. V., Miao, Z., Beker, O., Brāzma, A., & Papatheodorou, I. (2023) · Research Square · doi:10.21203/rs.3.rs-2538121/v1 · Cited by 2

Modeling Adverse Events Counts in the Bulgarian Pharmacovigilance DatabasePreprint

Antonov, L. S., Kirilov, B., & Dokova, K. G. (2023) · Preprints.org · doi:10.20944/preprints202309.0849.v1 · Cited by 2

High resolution deep mutational scanning of the melanocortin-4 receptor enables target characterization for drug discoveryPreprint

Howard, C. J., Abell, N. S., Osuna, B. A., Jones, E. M., Chan, L. Y., Chan, H., Artis, D. R., Asfaha, J. B., Bloom, J. S., Cooper, A. R., et al. (2024) · eLife · doi:10.7554/elife.104725.1 · Cited by 2

Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes

Bei, Y., & Hong, P. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1386-x · Cited by 1

Epigenetic and Transcriptional Regulation of the Reproductive Hypothalamus

Aylwin, C. F., & Lomniczi, A. (2020) · Masterclass in neuroendocrinology · doi:10.1007/978-3-030-40002-6_8 · Cited by 1

Multiple hypothesis screening using mixtures of non‐local distributions with applications to genomic studies

Denti, F., Peluso, S., Guindani, M., & Mira, A. (2023) · Statistics in Medicine · doi:10.1002/sim.9705 · Cited by 1

Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing

Marrella, M. A., & Biase, F. H. (2023) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-023-00861-0 · Cited by 1

Characterization of transcriptional and metabolic responses to a complex plant growth‐promoting soil inoculum

Brock, M. T., Nozue, K., Kliebenstein, D. J., Ewers, B. E., Mackay, D. S., Maignien, L., Wang, D., Weinig, C., & Maloof, J. (2025) · Plant Biology · doi:10.1111/plb.70034 · Cited by 1

A likelihood ratio test for detecting shifts in homeolog expression ratios in allopolyploids

Sun, J., Sese, J., & Shimizu, K. K. (2025) · New Phytologist · doi:10.1111/nph.70648 · Cited by 1

Pomalidomide enhances CD8+ T and NK cell mediated killing of HIV-infected cells

Pascoe, R. D., Gubser, C., Chang, J. J., Schröder, J., Utzschneider, D. T., Barrow, A. D., Lee, W. S., McMahon, J. H., Chiu, C. Y. H., Solomon, A. E., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.106004 · Cited by 1

The Level of Residual Dispersion Variation and the Power of Differential Expression Tests for RNA-Seq Data

Mi, G., & Di, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0120117 · Cited by 1

COMPARATIVE STUDIES OF DIFFERENTIAL GENE CALLING METHODS FOR RNA-SEQ DATA

Zheng, X. (2012) · Lincoln (University of Nebraska) · Cited by 1

Non-Parametric Bayesian Modelling of Digital Gene Expression Data

Vavoulis, D. V. (2014) · Journal of Computer Science & Systems Biology · doi:10.4172/jcsb.1000131 · Cited by 1

Finding differentially expressed regions of arbitrary length in quantitative genomic data based on marked point process model

Hatsuda, H. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts371 · Cited by 1

Statistical methods for the analysis of RNA sequencing data

Chu, M.-K. (2014) · Scholarship@Western (Western University) · Cited by 1

Detecting differentially expressed genes by smoothing effect of gene length on variance estimation

Tang, J., & Wang, F. (2015) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720015420044 · Cited by 1

A randomized approach to speed up the analysis of large-scale read-count data in the application of CNV detection

Wang, W., Sun, W., Wang, W., & Szatkiewicz, J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2077-6 · Cited by 1

Computational Analysis of RNA-Seq Data from Airway Epithelial Cells for Studying Lung Disease

Jackson, N. D., Ringel, L., & Seibold, M. A. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8570-8_15 · Cited by 1

Label Propagation Based Semi-supervised Feature Selection to Decode Clinical Phenotype of Huntington’s Disease

Jiang, X., Chen, M., Wang, W., Song, W., & Lin, G. N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26763-6_51 · Cited by 1

Molecular sampling at logarithmic rates for next-generation sequencing

Horn, C., & Salzman, J. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007537 · Cited by 1

Differential Methylation Analysis for Bisulfite Sequencing (BS-Seq) Data

Feng, H., Conneely, K. N., & Wu, H. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1994-0_16 · Cited by 1

Accurate prediction of stomach adenocarcinomas of poorest and best prognosis with a combination of gene expression and clinical signatures

Qiu, L., Kang, H., Yang, J., Zheng, Y., Chen, A., Chen, C., Wang, X., Fang, Q., Zhu, W., Sha, O., et al. (2022) · Genome Instability & Disease · doi:10.1007/s42764-022-00077-y · Cited by 1

Comparison of the broncoalveolar lavage fluid proteomics between foals and adult horses

Rivolta, A. A., Bujold, A. R., Wilmarth, P. A., Phinney, B. S., Navelski, J. P., Horohov, D. W., & Sanz, M. G. (2023) · PLoS ONE · doi:10.1371/journal.pone.0290778 · Cited by 1

Differential Expression Analysis Based on Ensemble Strategy on miRNA Profiles of Kidney Clear Cell Carcinoma

Zhao, E., Xi, Z., & Wu, Q. (2023) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2811283 · Cited by 1

Transwell Culture with Adipose Tissue-Derived Stem Cells and Fertilized Eggs Mimics the In Vivo Development of Fertilized Eggs to Blastocysts in the Fallopian Tube: An Animal Study

Hirakawa, T., Nakabayashi, K., Ito, N., Hata, K., Imi, S., Shibata, M., Urushiyama, D., Miyata, K., Yotsumoto, F., Yasunaga, S., et al. (2024) · Antioxidants · doi:10.3390/antiox13060704 · Cited by 1

Differentially expressed heterogeneous overdispersion genes testing for count data

Yuan, Y., Xu, Q., Wani, A. H., Dahrendorff, J., Wang, C. C. Q., Shen, A., Donglasan, J., Burgan, S. C., Graham, Z., Uddin, M., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300565 · Cited by 1

Systematic Evaluation and Application of IDR Domain-Mediated Transcriptional Activation of NUP98 in Saccharomyces cerevisiae

Wang, S., Wu, X., Qiao, Z., He, X., Li, Y., Zhang, T., Liu, W., Wang, M., Zhou, X., & Yu, Y. (2024) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00376 · Cited by 1

tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions

Esnaola, M., Castelo, R., & Ram, J. (2013) · Cited by 1

A Hierarchical Bayesian Model for RNA-Seq Data

Risso, D., Sales, G., Romualdi, C., & Chiogna, M. (2012) · Contributions to statistics · doi:10.1007/978-88-470-2871-5_17 · Cited by 1

DFseq: Distribution-Free Method to Detect Differential Gene Expression for RNA-Sequencing Data

Yang, S., Wachtel, M. S., & Wu, J. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2866994 · Cited by 1

Inference Procedures on the Ratio of Modified Generalized Poisson Distribution Means: Applications to RNA_SEQ Data

Shoukri, M. M., & Al-Eid, M. (2020) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029.2020.09.05 · Cited by 1

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Statistical analysis of high-throughput sequencing count data

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Understanding and improving high-throughput sequencing data production and analysis

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Characterization of the Regulatory Process of Pyrethroid Resistance in the House Fly, Musca domestica

Li, M. (2015) · Cited by 1

Statistical methods for transcriptomics: From microarrays to RNA-seq

Campos, S. T. (2014) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/48485 · Cited by 1

Differential expression analysis for multiple conditionsPreprint

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Detect Copy Number Variations from Read-depth of High-throughput Sequencing Data

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<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data

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The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

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License GPL (> = 3)

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Identifying the differentially expressed genes with RNA-Seq data

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Differential expression analysis for sequence count data via mixtures of negative binomials

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