A scaling normalization method for differential expression analysis of RNA-seq data

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

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works

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 · see citing works

RNA-Seq: a revolutionary tool for transcriptomics

Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · see citing works

Bioconductor: open software development for computational biology and bioinformatics

Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome Biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 13,133 · see citing works

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603 · see citing works

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,185 · see citing works

Alternative isoform regulation in human tissue transcriptomes

Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181 · see citing works

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

Yang, Y., Dudoit, S., Luu, P., Lin, D., Peng, V., Ngai, J., & Speed, T. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.4.e15 · Cited by 3,722 · see citing works

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 · see citing works

BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis

Durinck, S., Moreau, Y., Kasprzyk, A., Davis, S. R., Moor, B. D., Brāzma, A., & Huber, W. K. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti525 · Cited by 2,327 · see citing works

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 · see citing works

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 · see citing works

Stem cell transcriptome profiling via massive-scale mRNA sequencing

Cloonan, N., Forrest, A. R. R., Kolle, G., Gardiner, B. B. A., Faulkner, G. J., Brown, M. K., Taylor, D. F., Steptoe, A. L., Wani, S., Bethel, G., et al. (2008) · Nature Methods · doi:10.1038/nmeth.1223 · Cited by 1,035 · cited 5× · see citing works

Moderated statistical tests for assessing differences in tag abundance

Robinson, M. D., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm453 · Cited by 856 · see citing works

Evidence Based Selection of Housekeeping Genes

de Jonge, H. J. M., Fehrmann, R. S. N., de Bont, E. S. J. M., Hofstra, R. M. W., Gerbens, F., Kamps, W. A., de Vries, E. G. E., van der Zee, A. G. J., Meerman, G. J. T., & ter Elst, A. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000898 · Cited by 748 · see citing works

Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms.

't Hoen, P. A. C., Ariyurek, Y., Thygesen, H. H., Vreugdenhil, E., Vossen, R. H. A. M., de Menezes, R. X., Boer, J. M., van Ommen, G.-J. B., & den Dunnen, J. T. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn705 · Cited by 742 · see citing works

Human housekeeping genes are compact

Eisenberg, E., & Levanon, E. Y. (2003) · Trends in Genetics · doi:10.1016/s0168-9525(03)00140-9 · Cited by 699 · see citing works

PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls

Rozowsky, J. S., Euskirchen, G. M., Auerbach, R. K., Zhang, Z. D., Gibson, T., Bjornson, R. D., Carriero, N., Snyder, M. P., & Gerstein, M. (2009) · Nature Biotechnology · doi:10.1038/nbt.1518 · Cited by 558 · see citing works

Transcript length bias in RNA-seq data confounds systems biology

Oshlack, A., & Wakefield, M. J. (2009) · Biology Direct · doi:10.1186/1745-6150-4-14 · Cited by 497 · see citing works

Transcriptome-Wide Identification of Novel Imprinted Genes in Neonatal Mouse Brain

Wang, X., Sun, Q., McGrath, S. D., Mardis, E. R., Soloway, P. D., & Clark, A. G. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003839 · Cited by 184 · see citing works

In-depth characterization of the microRNA transcriptome in a leukemia progression modelPreprint

Kuchenbauer, F. C., Morin, R. D., Argiropoulos, B., Petriv, O. I., Griffith, M., Heuser, M., Yung, E., Piper, J., Delaney, A. D., Prabhu, A., et al. (2008) · Genome Research · doi:10.1101/gr.077578.108 · Cited by 173 · see citing works

Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data

Balwierz, P. J., Carninci, P., Daub, C. O., Kawai, J., Hayashizaki, Y., Belle, W. V., Beisel, C., & van Nimwegen, E. (2009) · Genome biology · doi:10.1186/gb-2009-10-7-r79 · Cited by 125 · see citing works

Determination of tag density required for digital transcriptome analysis: Application to an androgen-sensitive prostate cancer model

Li, H., Lovci, M., Kwon, Y., Rosenfeld, M. G., Fu, X., & Yeo, G. W.-M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0807121105 · Cited by 94 · see citing works

Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes

Oshlack, A., Emslie, D., Corcoran, L. M., & Smyth, G. K. (2007) · Genome biology · doi:10.1186/gb-2007-8-1-r2 · Cited by 69 · see citing works

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Vêncio, R. Z. N., Brentani, H., Patrão, D. F. D. C., & Pereira, C. A. D. B. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-119 · Cited by 65 · see citing works

limma powers differential expression analyses for RNA-sequencing and microarray studies

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works

Differential expression analysis for sequence count data

Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works

Gene ontology analysis for RNA-seq: accounting for selection bias

Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107 · see citing works

voom: precision weights unlock linear model analysis tools for RNA-seq read counts

Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155 · see citing works

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195 · see citing works

Microbiome Datasets Are Compositional: And This Is Not Optional

Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., & Egozcue, J. J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02224 · Cited by 3,044 · see citing works

A survey of best practices for RNA-seq data analysis

Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works

Multivariable association discovery in population-scale meta-omics studies

Mallick, H., Rahnavard, A., McIver, L. J., Ma, S., Zhang, Y., Nguyen, L. H., Tickle, T. L., Weingart, G., Ren, B., Schwager, E. H., et al. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009442 · Cited by 2,596 · see citing works

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression

Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Yazar, S., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00913-z · Cited by 2,607 · see citing works

Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852 · see citing works

ComBat-seq: batch effect adjustment for RNA-seq count data

Zhang, Y., Parmigiani, G., & Johnson, W. E. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa078 · Cited by 1,566 · see citing works

Differential expression in RNA-seq: A matter of depth

Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011) · Genome Research · doi:10.1101/gr.124321.111 · Cited by 1,632 · see citing works

The human transcriptome across tissues and individuals

Melé, M., Ferreira, P. G., Reverter, F., DeLuca, D. S., Monlong, J., Sammeth, M., Young, T. R., Goldmann, J. M., Pervouchine, D. D., Sullivan, T. J., et al. (2015) · Science · doi:10.1126/science.aaa0355 · Cited by 1,304 · see citing works

An atlas of the protein-coding genes in the human, pig, and mouse brain

Sjöstedt, E., Zhong, W., Fagerberg, L., Karlsson, M., Mitsios, N., Adori, C., Oksvold, P., Edfors, F., Limiszewska, A., Hikmet, F., et al. (2020) · Science · doi:10.1126/science.aay5947 · Cited by 1,151 · see citing works

Gene expression elucidates functional impact of polygenic risk for schizophrenia

Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works

Computational and analytical challenges in single-cell transcriptomics

Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works

The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight

Garrett-Bakelman, F. E., Darshi, M., Green, S. J., Gur, R. C., Lin, L., Macias, B. R., McKenna, M. J., Meydan, C., Mishra, T., Nasrini, J., et al. (2019) · Science · doi:10.1126/science.aau8650 · Cited by 1,128 · see citing works

Proteomics of SARS-CoV-2-infected host cells reveals therapy targets

Bojkova, D., Klann, K., Koch, B., Widera, M., Krause, D., Ciesek, S., Cinatl, J., & Münch, C. (2020) · Nature · doi:10.1038/s41586-020-2332-7 · Cited by 1,010 · see citing works

Pooling across cells to normalize single-cell RNA sequencing data with many zero counts

L. Lun, A. T., Bach, K., & Marioni, J. C. (2016) · Genome Biology · doi:10.1186/s13059-016-0947-7 · Cited by 1,108 · see citing works

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

SEQC/MAQC-III Consortium (2014) · Nature Biotechnology · doi:10.1038/nbt.2957 · Cited by 983 · see citing works

Gene co-expression analysis for functional classification and gene–disease predictions

van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works

Computational methods for transcriptome annotation and quantification using RNA-seq

Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C. (2011) · Nature Methods · doi:10.1038/nmeth.1613 · Cited by 1,042 · see citing works

Differential abundance testing on single-cell data using k-nearest neighbor graphs

Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01033-z · Cited by 938 · see citing works

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Schurch, N. J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G. G., Owen-Hughes, T., et al. (2016) · RNA · doi:10.1261/rna.053959.115 · Cited by 966 · see citing works

RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types

Monaco, G., Lee, B., Xu, W., Mustafah, S., Hwang, Y. Y., Carré, C., Burdin, N., Visan, L., Ceccarelli, M., Poidinger, M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.041 · Cited by 958 · see citing works

RNA Sequencing and Analysis

Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924 · see citing works

Bias, robustness and scalability in single-cell differential expression analysis

Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770 · see citing works

A comparison of methods for differential expression analysis of RNA-seq data

Soneson, C., & Delorenzi, M. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-91 · Cited by 887 · see citing works

GC-Content Normalization for RNA-Seq Data

Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-480 · Cited by 867 · see citing works

Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury

Llorens-Bobadilla, E., Zhao, S., Baser, A., Saiz-Castro, G., Zwadlo, K., & Martin-Villalba, A. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.07.002 · Cited by 778 · see citing works

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

Rapaport, F., Khanin, R., Liang, Y., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., & Betel, D. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r95 · Cited by 733 · see citing works

Macrophage-derived CXCL9 and CXCL10 are required for antitumor immune responses following immune checkpoint blockade

House, I. G., Savas, P., Lai, J., Chen, A. X. Y., Oliver, A. J., Teo, Z. L., Todd, K. L., Henderson, M. A., Giuffrida, L., Petley, E. V., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-1868 · Cited by 790 · see citing works

TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge

Nugent, A. A., Lin, K., van Lengerich, B., Lianoglou, S., Przybyla, L., Davis, S. S., Llapashtica, C., Wang, J., Kim, D. J., Xia, D., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.007 · Cited by 745 · see citing works

A general and flexible method for signal extraction from single-cell RNA-seq data

Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., & Vert, J.-P. (2018) · Nature Communications · doi:10.1038/s41467-017-02554-5 · Cited by 718 · see citing works

The m1A landscape on cytosolic and mitochondrial mRNA at single-base resolution

Safra, M., Sas-Chen, A., Nir, R., Winkler, R., Nachshon, A., Bar-Yaacov, D., Erlacher, M., Rossmanith, W., Stern-Ginossar, N., & Schwartz, S. (2017) · Nature · doi:10.1038/nature24456 · Cited by 646 · see citing works

Removing technical variability in RNA-seq data using conditional quantile normalization

Hansen, K. D., Irizarry, R. A., & WU, Z. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr054 · Cited by 607 · see citing works

A genome-wide transcriptomic analysis of protein-coding genes in human blood cells

Uhlen, M., Karlsson, M. J., Zhong, W., Tebani, A., Pou, C., Mikes, J., Lakshmikanth, T., Forsström, B., Edfors, F., Odeberg, J., et al. (2019) · Science · doi:10.1126/science.aax9198 · Cited by 594 · see citing works

Multi-omic machine learning predictor of breast cancer therapy response

Sammut, S.-J., Crispin-Ortuzar, M., Chin, S.-F., Provenzano, E., Bardwell, H. A., Ma, W., Cope, W., Dariush, A., Dawson, S.-J., Abraham, J. E., et al. (2022) · Nature · doi:10.1038/s41586-021-04278-5 · Cited by 654 · see citing works

MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma

Rodig, S. J., Gusenleitner, D., Jackson, D. G., Gjini, E., Giobbie-Hurder, A., Jin, C., Chang, H., Lovitch, S. B., Horak, C., Weber, J. S., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aar3342 · Cited by 579 · see citing works

Sexual-dimorphism in human immune system aging

Márquez, E. J., Chung, C.-H., Marches, R., Rossi, R. J., Nehar-Belaid, D., Eroglu, A., Mellert, D. J., Kuchel, G. A., Banchereau, J., & Ucar, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14396-9 · Cited by 584 · see citing works

A simple diet- and chemical-induced murine NASH model with rapid progression of steatohepatitis, fibrosis and liver cancer

Tsuchida, T., Lee, Y. A., Fujiwara, N., Ybanez, M., Allen, B., Martins, S., Fiel, M. I., Goossens, N., Chou, H.-I., Hoshida, Y., et al. (2018) · Journal of Hepatology · doi:10.1016/j.jhep.2018.03.011 · Cited by 583 · see citing works

Lysosome activation clears aggregates and enhances quiescent neural stem cell activation during aging

Leeman, D. S., Hebestreit, K., Ruetz, T., Webb, A. E., McKay, A., Pollina, E. A., Dulken, B. W., Zhao, X., Yeo, R. W., Ho, T. T., et al. (2018) · Science · doi:10.1126/science.aag3048 · Cited by 528 · see citing works

HDAC8 mutations in Cornelia de Lange syndrome affect the cohesin acetylation cycle

Deardorff, M. A., Bando, M., Nakato, R., Watrin, E., Itoh, T., Minamino, M., Saitoh, K., Komata, M., Katou, Y., Clark, D., et al. (2012) · Nature · doi:10.1038/nature11316 · Cited by 577 · see citing works

Predicting drug response and synergy using a deep learning model of human cancer cells

Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ma, J., & Ideker, T. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.09.014 · Cited by 544 · see citing works

Normalizing single-cell RNA sequencing data: challenges and opportunities

Vallejos, C. A., Risso, D., Scialdone, A., Dudoit, S., & Marioni, J. C. (2017) · Nature Methods · doi:10.1038/nmeth.4292 · Cited by 436 · see citing works

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P., & De Preter, K. (2020) · Nature Communications · doi:10.1038/s41467-020-19015-1 · Cited by 446 · see citing works

Inheritable silencing of endogenous genes by hit-and-run targeted epigenetic editing

Amabile, A., Migliara, A., Capasso, P., Biffi, M., Cittaro, D., Naldini, L., & Lombardo, A. (2016) · Cell · doi:10.1016/j.cell.2016.09.006 · Cited by 516 · see citing works

WRN helicase is a synthetic lethal target in microsatellite unstable cancers

Chan, E. M., Shibue, T., McFarland, J. M., Gaeta, B., Ghandi, M., Dumont, N., Gonzalez, A., McPartlan, J. S., Li, T., Zhang, Y., et al. (2019) · Nature · doi:10.1038/s41586-019-1102-x · Cited by 458 · see citing works

Controlling Bias and inflation in epigenome- and transcriptome-wide association studies using the empirical null distribution

the BIOS Consortium, van Iterson, M., van Zwet, E. W., & Heijmans, B. T. (2017) · Genome biology · doi:10.1186/s13059-016-1131-9 · Cited by 421 · see citing works

Atopic Dermatitis Is an IL-13–Dominant Disease with Greater Molecular Heterogeneity Compared to Psoriasis

Tsoi, L. C., Rodriguez, E., Degenhardt, F., Baurecht, H., Wehkamp, U., Volks, N., Szymczak, S., Swindell, W. R., Sarkar, M. K., Raja, K., et al. (2019) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.12.018 · Cited by 469 · see citing works

Loss of p53 drives neuron reprogramming in head and neck cancer

Amit, M., Takahashi, H., Dragomir, M. P., Lindemann, A., Gleber-Netto, F. O., Pickering, C. R., Anfossi, S., Osman, A. A., Cai, Y., Wang, R., et al. (2020) · Nature · doi:10.1038/s41586-020-1996-3 · Cited by 483 · see citing works

IRE1α–XBP1 controls T cell function in ovarian cancer by regulating mitochondrial activity

Song, M., Sandoval, T. A., Chae, C.-S., Chopra, S., Tan, C., Rutkowski, M. R., Raundhal, M., Chaurio, R. A., Payne, K. K., Konrad, C., et al. (2018) · Nature · doi:10.1038/s41586-018-0597-x · Cited by 413 · see citing works

Quiescence Modulates Stem Cell Maintenance and Regenerative Capacity in the Aging Brain

Kalamakis, G., Brüne, D., Ravichandran, S., Bolz, J., Fan, W., Ziebell, F., Stiehl, T., Catalá-Martinez, F., Kupke, J., Zhao, S., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.040 · Cited by 399 · see citing works

Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression

Chen, Y.-J., Roumeliotis, T. I., Chang, Y.-H., Chen, C.-T., Han, C.-L., Lin, M.-H., Chen, H.-W., Chang, G.-C., Chang, Y.-L., Wu, C.-T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.012 · Cited by 382 · see citing works

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 · see citing works

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 · see citing works

Analysis of microbial compositions: a review of normalization and differential abundance analysis

Lin, H., & Peddada, S. D. (2020) · npj Biofilms and Microbiomes · doi:10.1038/s41522-020-00160-w · Cited by 341 · see citing works

Genome-wide mapping of methylated adenine residues in pathogenic Escherichia coli using single-molecule real-time sequencing

Fang, G., Munera, D., Friedman, D. I., Mandlik, A., Chao, M. C., Banerjee, O., Feng, Z., Losic, B., Mahajan, M. C., Jabado, O. J., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2432 · Cited by 360 · see citing works

MicroRNAs and their isomiRs function cooperatively to target common biological pathways

Cloonan, N., Wani, S., Xu, Q., Gu, J., Lea, K., Heater, S., Barbacioru, C., Steptoe, A. L., Martin, H. C., Nourbakhsh, E., et al. (2011) · Genome Biology · doi:10.1186/gb-2011-12-12-r126 · Cited by 345 · see citing works

An epigenetic switch ensures transposon repression upon dynamic loss of DNA methylation in embryonic stem cells

Walter, M., Teissandier, A., Pérez-Palacios, R., & Bourc'his, D. (2016) · eLife · doi:10.7554/elife.11418 · Cited by 303 · see citing works

LC3-Associated Phagocytosis in Myeloid Cells Promotes Tumor Immune Tolerance

Cunha, L. D., Yang, M., Carter, R., Guy, C., Harris, L., Crawford, J. C., Quarato, G., Boada-Romero, E., Kalkavan, H., Johnson, M. D. L., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.061 · Cited by 334 · see citing works

Microglial NF-κB drives tau spreading and toxicity in a mouse model of tauopathy

Wang, C., Fan, L., Khawaja, R. R., Liu, B., Zhan, L., Kodama, L., Chin, M., Li, Y., Le, D., Zhou, Y., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29552-6 · Cited by 333 · see citing works

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

Robertson, C. C., Inshaw, J. R. J., Onengut-Gumuscu, S., Chen, W.-M., Santa Cruz, D. F., Yang, H., Cutler, A. J., Crouch, D. J. M., Farber, E., Bridges, S. L., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00880-5 · Cited by 302 · see citing works

RNA-seq: technical variability and sampling

McIntyre, L. M., Lopiano, K. K., Morse, A. M., Amin, V., Oberg, A. L., Young, L. J., & Nuzhdin, S. V. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-293 · Cited by 285 · see citing works

Zebrafish mRNA sequencing deciphers novelties in transcriptome dynamics during maternal to zygotic transition

Aanes, H., Winata, C. L., Lin, C. H., Chen, J. P., Srinivasan, K. G., Lee, S. G. P., Lim, A. Y. M., Hajan, H. S., Collas, P., Bourque, G., et al. (2011) · Genome Research · doi:10.1101/gr.116012.110 · Cited by 285 · see citing works

Power analysis and sample size estimation for RNA-Seq differential expression

Ching, T., Huang, S., & Garmire, L. X. (2014) · RNA · doi:10.1261/rna.046011.114 · Cited by 263 · see citing works

QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments

Hartley, S. W., & Mullikin, J. C. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0670-5 · Cited by 259 · see citing works

Sequencing of 640,000 exomes identifies <i>GPR75</i> variants associated with protection from obesity

Akbari, P., Gilani, A., Sosina, O., Kosmicki, J. A., Khrimian, L., Fang, Y.-Y., Persaud, T., Garcia, V., Sun, D., Li, A., et al. (2021) · Science · doi:10.1126/science.abf8683 · Cited by 276 · see citing works

SCnorm: robust normalization of single-cell RNA-seq data

Bacher, R., Chu, L.-F., Leng, N., Gasch, A. P., Thomson, J. A., Stewart, R. M., Newton, M., & Kendziorski, C. (2017) · Nature Methods · doi:10.1038/nmeth.4263 · Cited by 282 · see citing works

A comparison of statistical methods for detecting differentially expressed genes from RNA‐seq data

Kvam, V. M., Liu, P., & Si, Y. (2012) · American Journal of Botany · doi:10.3732/ajb.1100340 · Cited by 281 · see citing works

Cell-Type-Based Analysis of MicroRNA Profiles in the Mouse Brain

He, M., Liu, Y., Wang, X., Zhang, M. Q., Hannon, G. J., & Huang, Z. J. (2012) · Neuron · doi:10.1016/j.neuron.2011.11.010 · Cited by 277 · see citing works

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

de Klein, N., Tsai, E. A., Vochteloo, M., Baird, D., Huang, Y., Chen, C.-Y., van Dam, S., Oelen, R., Deelen, P., Bakker, O. B., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01300-6 · Cited by 272 · see citing works

Translation control of the immune checkpoint in cancer and its therapeutic targeting

Xu, Y., Poggio, M., Jin, H. Y., Shi, Z., Forester, C. M., Wang, Y., Stumpf, C. R., Xue, L., Devericks, E., So, L., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0321-2 · Cited by 261 · see citing works

The genetic basis of parental care evolution in monogamous mice

Bendesky, A., Kwon, Y.-M., Lassance, J.-M., Lewarch, C. L., Yao, S., Peterson, B. K., He, M. X., Dulac, C., & Hoekstra, H. E. (2017) · Nature · doi:10.1038/nature22074 · Cited by 268 · see citing works

An autoimmune stem-like CD8 T cell population drives type 1 diabetes

Gearty, S. V., Dündar, F., Zumbo, P., Espinosa-Carrasco, G., Shakiba, M., Sanchez-Rivera, F. J., Socci, N. D., Trivedi, P., Lowe, S. W., Lauer, P., et al. (2022) · Nature · doi:10.1038/s41586-021-04248-x · Cited by 257 · see citing works

A systematic evaluation of single cell RNA-seq analysis pipelines

Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I. (2019) · Nature Communications · doi:10.1038/s41467-019-12266-7 · Cited by 265 · see citing works

A broken promise: microbiome differential abundance methods do not control the false discovery rate

Hawinkel, S., Mattiello, F., Bijnens, L., & Thas, O. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbx104 · Cited by 199 · see citing works

Elevated paternal glucocorticoid exposure alters the small noncoding RNA profile in sperm and modifies anxiety and depressive phenotypes in the offspring

Short, A. K., Fennell, K. A., Perreau, V. M., Fox, A., O’Bryan, M. K., Kim, J. H., Bredy, T. W., Pang, T. Y., & Hannan, A. J. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.109 · Cited by 230 · see citing works

Microglia affect α-synuclein cell-to-cell transfer in a mouse model of Parkinson’s disease

George, S., Rey, N. L., Tyson, T., Esquibel, C., Meyerdirk, L., Schulz, E., Pierce, S., Burmeister, A. R., Madaj, Z., Steiner, J. A., et al. (2019) · Molecular Neurodegeneration · doi:10.1186/s13024-019-0335-3 · Cited by 229 · see citing works

Genome and Transcriptome Sequencing in Prospective Metastatic Triple-Negative Breast Cancer Uncovers Therapeutic Vulnerabilities

Craig, D. W., O'Shaughnessy, J. A., Kiefer, J. A., Aldrich, J., Sinari, S., Moses, T. M., Wong, S., Dinh, J., Christoforides, A., Blum, J. L., et al. (2013) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-12-0781 · Cited by 220 · see citing works

Dynamics and genomic landscape of CD8+ T cells undergoing hepatic priming

Bénéchet, A. P., De Simone, G., Di Lucia, P., Cilenti, F., Barbiera, G., Le Bert, N., Fumagalli, V., Lusito, E., Moalli, F., Bianchessi, V., et al. (2019) · Nature · doi:10.1038/s41586-019-1620-6 · Cited by 222 · see citing works

Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies

Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218 · see citing works

Mechanisms of mechanical overload-induced skeletal muscle hypertrophy: current understanding and future directions

Roberts, M. D., McCarthy, J. J., Hornberger, T. A., Phillips, S. M., Mackey, A. L., Nader, G. A., Boppart, M. D., Kavazis, A. N., Reidy, P. T., Ogasawara, R., et al. (2023) · Physiological Reviews · doi:10.1152/physrev.00039.2022 · Cited by 214 · see citing works

Synergy between the KEAP1/NRF2 and PI3K Pathways Drives Non-Small-Cell Lung Cancer with an Altered Immune Microenvironment

Best, S. A., De Souza, D. P., Kersbergen, A., Policheni, A. N., Dayalan, S., Tull, D., Rathi, V., Gray, D. H., Ritchie, M. E., McConville, M. J., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.02.006 · Cited by 217 · see citing works

Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data

Lun, A. T. L., & Marioni, J. C. (2017) · Biostatistics · doi:10.1093/biostatistics/kxw055 · Cited by 159 · see citing works

Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation

Mendiola, A. S., Ryu, J. K., Bardehle, S., Meyer-Franke, A., Ang, K. K.-H., Wilson, C., Baeten, K. M., Hanspers, K., Merlini, M., Thomas, S., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0654-0 · Cited by 199 · see citing works

Spatial sorting enables comprehensive characterization of liver zonation

Ben-Moshe, S., Shapira, Y., Moor, A. E., Halpern, K. B., & Itzkovitz, S. (2019) · doi:10.1101/529784 · Cited by 205 · see citing works

Spatial sorting enables comprehensive characterization of liver zonation

Ben-Moshe, S., Shapira, Y., Moor, A. E., Manco, R., Veg, T., Bahar Halpern, K., & Itzkovitz, S. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0109-9 · Cited by 205 · see citing works

Brain delivery and activity of a lysosomal enzyme using a blood–brain barrier transport vehicle in mice

Ullman, J. C., Arguello, A., Getz, J. A., Bhalla, A., Mahon, C. S., Wang, J., Giese, T., Bedard, C., Kim, D. J., Blumenfeld, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay1163 · Cited by 205 · see citing works

The chromatin accessibility signature of human immune aging stems from CD8+ T cells

Ucar, D., Márquez, E. J., Chung, C.-H., Marches, R., Rossi, R. J., Uyar, A., Wu, T.-C., George, J., Stitzel, M. L., Palucka, A. K., et al. (2017) · Journal of Experimental Medicine · doi:10.1084/jem.20170416 · Cited by 200 · see citing works

Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing

Robles, J. A., Qureshi, S. E., Stephen, S. J., Wilson, S. R., Burden, C. J., & Taylor, J. M. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-484 · Cited by 198 · see citing works

Intermuscular adipose tissue directly modulates skeletal muscle insulin sensitivity in humans

Sachs, S., Zarini, S., Kahn, D. E., Harrison, K. A., Perreault, L., Phang, T., Newsom, S. A., Strauss, A., Kerege, A., Schoen, J. A., et al. (2019) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00243.2018 · Cited by 192 · see citing works

Inhibiting glutaminase in acute myeloid leukemia: metabolic dependency of selected AML subtypes

Matre, P., Velez, J., Jacamo, R., Qi, Y., Su, X., Cai, T., Chan, S. M., Lodi, A., Sweeney, S. R., Ma, H., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.12944 · Cited by 185 · see citing works

NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

He, L., Davila-Velderrain, J., Sumida, T. S., Hafler, D. A., Kellis, M., & Kulminski, A. M. (2021) · Communications Biology · doi:10.1038/s42003-021-02146-6 · Cited by 181 · see citing works

A Comprehensive Mouse Transcriptomic BodyMap across 17 Tissues by RNA-seq

Li, B., Qing, T., Zhu, J., Wen, Z., Yu, Y., Fukumura, R., Zheng, Y., Gondo, Y., & Shi, L. (2017) · Scientific Reports · doi:10.1038/s41598-017-04520-z · Cited by 184 · see citing works

Transcriptional Heterogeneity in Naive and Primed Human Pluripotent Stem Cells at Single-Cell Resolution

Messmer, T., von Meyenn, F., Savino, A., Santos, F., Mohammed, H., Lun, A. T. L., Marioni, J. C., & Reik, W. (2019) · Cell Reports · doi:10.1016/j.celrep.2018.12.099 · Cited by 156 · see citing works

A second X chromosome contributes to resilience in a mouse model of Alzheimer’s disease

Davis, E. J., Broestl, L., Abdulai-Saiku, S., Worden, K., Bonham, L. W., Miñones-Moyano, E., Moreno, A. J., Wang, D., Chang, K., Williams, G., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaz5677 · Cited by 176 · see citing works

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq

Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., & Yosef, N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.010 · Cited by 173 · see citing works

Exercise alters mouse sperm small noncoding RNAs and induces a transgenerational modification of male offspring conditioned fear and anxiety

Short, A. K., Yeshurun, S., Powell, R., Perreau, V. M., Fox, A., Kim, J. H., Pang, T. Y., & Hannan, A. J. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.82 · Cited by 166 · see citing works