Bioconductor: open software development for computational biology and bioinformatics

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106

<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

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

clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters

Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259

featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219

Welcome to the tidyverse

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019) · Journal of Open Source Software · doi:10.21105/joss.01686 · Cited by 22,138

HTSeq—a Python framework to work with high-throughput sequencing data

Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464

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

GSVA: gene set variation analysis for microarray and RNA-Seq data

Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440

STRING v10: protein–protein interaction networks, integrated over the tree of life

Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436

NCBI GEO: archive for functional genomics data sets—update

Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K. H., Sherman, P. M., Holko, M., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1193 · Cited by 10,418

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

Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876

Predicting effective microRNA target sites in mammalian mRNAs

Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P. (2015) · eLife · doi:10.7554/elife.05005 · Cited by 7,318

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

The consensus molecular subtypes of colorectal cancer

Guinney, J., Dienstmann, R., Wang, X., de Reyniès, A., Schlicker, A., Soneson, C., Marisa, L., Roepman, P., Nyamundanda, G., Angelino, P., et al. (2015) · Nature Medicine · doi:10.1038/nm.3967 · Cited by 5,170

The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309

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

Human DNA methylomes at base resolution show widespread epigenomic differences

Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.-M., et al. (2009) · Nature · doi:10.1038/nature08514 · Cited by 4,673

A draft sequence of the Neandertal genome

Green, R. E., Krause, J., Briggs, A. W., Maricic, T., Stenzel, U., Kircher, M., Patterson, N., Li, H., Zhai, W., Fritz, M. H.-Y., et al. (2010) · Science · doi:10.1126/science.1188021 · Cited by 4,318

Genome-wide methylation profiles reveal quantitative views of human aging rates

Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270

Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data

Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A., & Callahan, B. J. (2018) · Microbiome · doi:10.1186/s40168-018-0605-2 · Cited by 3,493

A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function

Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159

Transcriptome-wide Identification of RNA-Binding Protein and MicroRNA Target Sites by PAR-CLIP

Hafner, M., Landthaler, M., Burger, L., Khorshid, M., Hausser, J., Berninger, P., Rothballer, A., Ascano, M., Jungkamp, A.-C., Munschauer, M., et al. (2010) · Cell · doi:10.1016/j.cell.2010.03.009 · Cited by 2,873

A subcellular map of the human proteome

Thul, P. J., Åkesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., Alm, T., Asplund, A., Björk, L., Breckels, L. M., et al. (2017) · Science · doi:10.1126/science.aal3321 · Cited by 2,813

Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma

Noushmehr, H., Weisenberger, D. J., Diefes, K., Phillips, H. S., Pujara, K., Berman, B. P., Pan, F., Pelloski, C. E., Sulman, E. P., Bhat, K. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2010.03.017 · Cited by 2,337

Easy quantitative assessment of genome editing by sequence trace decomposition

Brinkman, E. K., Chen, T., Amendola, M., & van Steensel, B. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku936 · Cited by 2,390

Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation

Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013984 · Cited by 2,235

ChIP–seq: advantages and challenges of a maturing technology

Park, P. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2641 · Cited by 2,217

The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores

Irizarry, R. A., Ladd-Acosta, C., Wen, B., Wu, Z., Montano, C., Onyango, P., Cui, H., Gabo, K., Rongione, M., Webster, M., et al. (2009) · Nature Genetics · doi:10.1038/ng.298 · Cited by 2,177

Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy

Collisson, E. A., Sadanandam, A., Olson, P., Gibb, W. J., Truitt, M., Gu, S., Cooc, J., Weinkle, J., Kim, G. E., Jakkula, L., et al. (2011) · Nature Medicine · doi:10.1038/nm.2344 · Cited by 1,967

Direct multiplexed measurement of gene expression with color-coded probe pairs

Geiss, G. K., Bumgarner, R. E., Birditt, B., Dahl, T., Dowidar, N., Dunaway, D. L., Fell, H. P., Ferree, S., George, R. D., Grogan, T., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1385 · Cited by 2,066

A function for interleukin 2 in Foxp3-expressing regulatory T cells

Fontenot, J. D., Rasmussen, J. P., Gavin, M. A., & Rudensky, A. Y. (2005) · Nature Immunology · doi:10.1038/ni1263 · Cited by 1,819

Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes

The Type 1 Diabetes Genetics Consortium, Barrett, J. C., Clayton, D. G., Concannon, P., Akolkar, B., Cooper, J. D., Erlich, H. A., Julier, C., Morahan, G., Nerup, J., et al. (2009) · Nature Genetics · doi:10.1038/ng.381 · Cited by 1,746

Detecting differential usage of exons from RNA-seq data

Anders, S., Reyes, A., & Huber, W. (2012) · Genome Research · doi:10.1101/gr.133744.111 · Cited by 1,545

Antibiotics in early life alter the murine colonic microbiome and adiposity

Cho, I., Yamanishi, S., Cox, L., Methé, B. A., Zavadil, J., Li, K., Gao, Z., Mahana, D., Raju, K., Teitler, I., et al. (2012) · Nature · doi:10.1038/nature11400 · Cited by 1,613

A chromosome conformation capture ordered sequence of the barley genome

Mascher, M., Gundlach, H., Himmelbach, A., Beier, S., Twardziok, S. O., Wicker, T., Radchuk, V., Dockter, C., Hedley, P. E., Russell, J., et al. (2017) · Nature · doi:10.1038/nature22043 · Cited by 1,450

LNA-mediated microRNA silencing in non-human primates

Elmén, J., Lindow, M., Schütz, S., Lawrence, M., Petri, A., Obad, S., Lindholm, M., Hedtjärn, M., Hansen, H. F., Berger, U., et al. (2008) · Nature · doi:10.1038/nature06783 · Cited by 1,589

Stochastic State Transitions Give Rise to Phenotypic Equilibrium in Populations of Cancer Cells

Gupta, P. B., Fillmore, C. M., Jiang, G., Shapira, S. D., Tao, K., Kuperwasser, C., & Lander, E. S. (2011) · Cell · doi:10.1016/j.cell.2011.07.026 · Cited by 1,475

Enhancing CD8 T-cell memory by modulating fatty acid metabolism

Pearce, E. L., Walsh, M. C., Cejas, P. J., Harms, G. M., Shen, H., Wang, L.-S., Jones, R. G., & Choi, Y. (2009) · Nature · doi:10.1038/nature08097 · Cited by 1,559

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508

GOSemSim: an R package for measuring semantic similarity among GO terms and gene products

Yu, G., Li, F., Qin, Y., Bo, X., Wu, Y., & Wang, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq064 · Cited by 1,388

SC3: consensus clustering of single-cell RNA-seq data

Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500

Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers

kConFab, Lim, E., Vaillant, F., Wu, D., Forrest, N. C., Pal, B., Hart, A. H., Asselin-Labat, M.-L., Gyorki, D. E., Ward, T., et al. (2009) · Nature Medicine · doi:10.1038/nm.2000 · Cited by 1,492

Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations

Fulco, C. P., Nasser, J., Jones, T. R., Munson, G., Bergman, D. T., Subramanian, V., Grossman, S. R., Anyoha, R., Doughty, B. R., Patwardhan, T. A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0538-0 · Cited by 1,111

Local regulation of gene expression by lncRNA promoters, transcription and splicing

Engreitz, J. M., Haines, J. E., Perez, E. M., Munson, G., Chen, J., Kane, M., McDonel, P. E., Guttman, M., & Lander, E. S. (2016) · Nature · doi:10.1038/nature20149 · Cited by 1,253

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

Senescence is a developmental mechanism that contributes to embryonic growth and patterning

Storer, M., Mas, A., Robert-Moreno, A., Pecoraro, M., Ortells, M. C., Di Giacomo, V., Yosef, R., Pilpel, N., Krizhanovsky, V., Sharpe, J., et al. (2013) · Cell · doi:10.1016/j.cell.2013.10.041 · Cited by 1,186

A data-driven approach to preprocessing Illumina 450K methylation array data

Pidsley, R., Y Wong, C. C., Volta, M., Lunnon, K., Mill, J., & Schalkwyk, L. C. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-293 · Cited by 1,143

JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles

Mathelier, A., Fornes, O., Arenillas, D. J., Chen, C.-Y., Denay, G., Lee, J., Shi, W., Shyr, C., Tan, G., Worsley-Hunt, R., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1176 · Cited by 937

Ensembl 2019

Cunningham, F., Achuthan, P., Akanni, W., Allen, J., Amode, M. R., Armean, I. M., Bennett, R., Bhai, J., Billis, K., Boddu, S., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1113 · Cited by 925

Stromal gene expression defines poor-prognosis subtypes in colorectal cancer

Calon, A., Lonardo, E., Berenguer-Llergo, A., Espinet, E., Hernando-Momblona, X., Iglesias, M., Sevillano, M., Palomo-Ponce, S., Tauriello, D. V. F., Byrom, D., et al. (2015) · Nature Genetics · doi:10.1038/ng.3225 · Cited by 1,070

TFEB controls cellular lipid metabolism through a starvation-induced autoregulatory loop

Settembre, C., De Cegli, R., Mansueto, G., Saha, P. K., Vetrini, F., Visvikis, O., Huynh, T., Carissimo, A., Palmer, D., Jürgen Klisch, T., et al. (2013) · Nature Cell Biology · doi:10.1038/ncb2718 · Cited by 1,022

Substrate Fate in Activated Macrophages: A Comparison between Innate, Classic, and Alternative Activation

Rodríguez-Prados, J.-C., Través, P. G., Cuenca, J., Rico, D., Aragonés, J., Martín-Sanz, P., Cascante, M., & Boscá, L. (2010) · The Journal of Immunology · doi:10.4049/jimmunol.0901698 · Cited by 1,022

Reference maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines

Bock, C., Kiskinis, E., Verstappen, G., Gu, H., Boulting, G., Smith, Z. D., Ziller, M., Croft, G. F., Amoroso, M. W., Oakley, D. H., et al. (2011) · Cell · doi:10.1016/j.cell.2010.12.032 · Cited by 1,009

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

arrayQualityMetrics—a bioconductor package for quality assessment of microarray data

Kauffmann, A., Gentleman, R., & Huber, W. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btn647 · Cited by 946

Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis

Novershtern, N., Subramanian, A., Lawton, L. N., Mak, R. H., Haining, W. N., McConkey, M. E., Habib, N., Yosef, N., Chang, C. Y., Shay, T., et al. (2011) · Cell · doi:10.1016/j.cell.2011.01.004 · Cited by 942

MeCP2 binds to 5hmC enriched within active genes and accessible chromatin in the nervous system

Mellén, M., Ayata, P., Dewell, S., Kriaucionis, S., & Heintz, N. (2012) · Cell · doi:10.1016/j.cell.2012.11.022 · Cited by 943

Multiple-laboratory comparison of microarray platforms

Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., Gabrielson, E., Garcia, J. G. N., Geoghegan, J., Germino, G., et al. (2005) · Nature Methods · doi:10.1038/nmeth756 · Cited by 862

Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis

Shin, J., Berg, D. A., Zhu, Y., Shin, J. Y., Song, J., Bonaguidi, M. A., Enikolopov, G., Nauen, D. W., Christian, K. M., Ming, G.-L., et al. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.07.013 · Cited by 784

Regional and cellular gene expression changes in human Huntington's disease brain

Hodges, A., Strand, A. D., Aragaki, A. K., Kuhn, A., Sengstag, T., Hughes, G., Elliston, L. A., Hartog, C., Goldstein, D. R., Thu, D., et al. (2006) · Human Molecular Genetics · doi:10.1093/hmg/ddl013 · Cited by 804

Apoptotic Caspases Prevent the Induction of Type I Interferons by Mitochondrial DNA

Rongvaux, A., Jackson, R., Harman, C. C. D., Li, T., West, A. P., de Zoete, M. R., Wu, Y., Yordy, B., Lakhani, S. A., Kuan, C.-Y., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.037 · Cited by 847

Direct conversion of mouse fibroblasts to hepatocyte-like cells by defined factors

Sekiya, S., & Suzuki, A. (2011) · Nature · doi:10.1038/nature10263 · Cited by 846

mTORC1 controls mitochondrial activity and biogenesis through 4E-BP-dependent translational regulation

Morita, M., Gravel, S.-P., Chénard, V., Sikström, K., Zheng, L., Alain, T., Gandin, V., Avizonis, D., Arguello, M., Zakaria, C., et al. (2013) · Cell Metabolism · doi:10.1016/j.cmet.2013.10.001 · Cited by 842

Sex-specific transcriptional signatures in human depression

Labonté, B., Engmann, O., Purushothaman, I., Menard, C., Wang, J., Tan, C., Scarpa, J. R., Moy, G., Loh, Y.-H. E., Cahill, M., et al. (2017) · Nature Medicine · doi:10.1038/nm.4386 · Cited by 748

Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids

Woodruff, P. G., Boushey, H. A., Dolganov, G. M., Barker, C. S., Yang, Y. H., Donnelly, S., Ellwanger, A., Sidhu, S. S., Dao-Pick, T. P., Pantoja, C., et al. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707413104 · Cited by 828

Enhancer Evolution across 20 Mammalian Species

Villar, D., Berthelot, C., Aldridge, S., Rayner, T. F., Lukk, M., Pignatelli, M., Park, T. J., Deaville, R., Erichsen, J. T., Jasinska, A. J., et al. (2015) · Cell · doi:10.1016/j.cell.2015.01.006 · Cited by 825

A Python library for probabilistic analysis of single-cell omics data

Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789

Maternal care effects on the hippocampal transcriptome and anxiety-mediated behaviors in the offspring that are reversible in adulthood

Weaver, I. C. G., Meaney, M. J., & Szyf, M. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507526103 · Cited by 771

Exosomes secreted by nematode parasites transfer small RNAs to mammalian cells and modulate innate immunity

Buck, A. H., Coakley, G., Simbari, F., McSorley, H. J., Quintana, J. F., Le Bihan, T., Kumar, S., Abreu-Goodger, C., Lear, M., Harcus, Y., et al. (2014) · Nature Communications · doi:10.1038/ncomms6488 · Cited by 770

TP53 Variations in Human Cancers: New Lessons from the IARC TP53 Database and Genomics Data: Human Mutation

Bouaoun, L., Sonkin, D., Ardin, M., Hollstein, M., Byrnes, G., Zavadil, J., & Olivier, M. (2016) · Human Mutation · doi:10.1002/humu.23035 · Cited by 730

Intestinal label-retaining cells are secretory precursors expressing Lgr5

Buczacki, S. J. A., Zecchini, H. I., Nicholson, A. M., Russell, R., Vermeulen, L., Kemp, R., & Winton, D. J. (2013) · Nature · doi:10.1038/nature11965 · Cited by 761

Yap1 Activation Enables Bypass of Oncogenic Kras Addiction in Pancreatic Cancer

Kapoor, A., Yao, W., Ying, H., Hua, S., Liewen, A., Wang, Q., Zhong, Y., Wu, C.-J., Sadanandam, A., Hu, B., et al. (2014) · Cell · doi:10.1016/j.cell.2014.06.003 · Cited by 716

Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits

Wu, Y., Zeng, J., Zhang, F., Zhu, Z., Qi, T., Zheng, Z., Lloyd-Jones, L. R., Marioni, R. E., Martin, N. G., Montgomery, G. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03371-0 · Cited by 685

450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy

Joubert, B. R., Håberg, S. E., Nilsen, R. M., Wang, X., Vollset, S. E., Murphy, S. K., Huang, Z., Hoyo, C., Midttun, Ø., Cupul-Uicab, L. A., et al. (2012) · Environmental Health Perspectives · doi:10.1289/ehp.1205412 · Cited by 716

Biases in Illumina transcriptome sequencing caused by random hexamer priming

Hansen, K. D., Brenner, S. E., & Dudoit, S. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq224 · Cited by 713

Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology

Cox, J., & Mann, M. (2011) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061308-093216 · Cited by 685

CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T. H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., & Aerts, H. J. W. L. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.02.015 · Cited by 695

Control of mammary stem cell function by steroid hormone signalling

Asselin-Labat, M.-L., Vaillant, F., Sheridan, J. M., Pal, B., Wu, D., Simpson, E. R., Yasuda, H., Smyth, G. K., Martin, T. J., Lindeman, G. J., et al. (2010) · Nature · doi:10.1038/nature09027 · Cited by 693

Single-Cell Profiles of Retinal Ganglion Cells Differing in Resilience to Injury Reveal Neuroprotective Genes

Tran, N. M., Shekhar, K., Whitney, I. E., Jacobi, A., Benhar, I., Hong, G., Yan, W., Adiconis, X., Arnold, M. E., Lee, J. M., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.11.006 · Cited by 627

T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection

McKinney, E. F., Lee, J. C., Jayne, D. R. W., Lyons, P. A., & Smith, K. G. C. (2015) · Nature · doi:10.1038/nature14468 · Cited by 685

MutationalPatterns: comprehensive genome-wide analysis of mutational processes

Blokzijl, F., Janssen, R., van Boxtel, R., & Cuppen, E. (2018) · Genome Medicine · doi:10.1186/s13073-018-0539-0 · Cited by 660

Synthetic spike-in standards for RNA-seq experiments

Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B. (2011) · Genome Research · doi:10.1101/gr.121095.111 · Cited by 653

Systematic mapping of functional enhancer–promoter connections with CRISPR interference

Fulco, C. P., Munschauer, M., Anyoha, R., Munson, G., Grossman, S. R., Perez, E. M., Kane, M., Cleary, B., Lander, E. S., & Engreitz, J. M. (2016) · Science · doi:10.1126/science.aag2445 · Cited by 643

Stromal contribution to the colorectal cancer transcriptome

Isella, C., Terrasi, A., Bellomo, S. E., Petti, C., Galatola, G., Muratore, A., Mellano, A., Senetta, R., Cassenti, A., Sonetto, C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3224 · Cited by 640

Broad defects in the energy metabolism of leukocytes underlie immunoparalysis in sepsis

Cheng, S.-C., Scicluna, B. P., Arts, R. J. W., Gresnigt, M. S., Lachmandas, E., Giamarellos-Bourboulis, E. J., Kox, M., Manjeri, G. R., Wagenaars, J. A. L., Cremer, O. L., et al. (2016) · Nature Immunology · doi:10.1038/ni.3398 · Cited by 637

Ultraviolet-radiation-induced inflammation promotes angiotropism and metastasis in melanoma

Bald, T., Quast, T., Landsberg, J., Rogava, M., Glodde, N., Lopez-Ramos, D., Kohlmeyer, J., Riesenberg, S., van den Boorn-Konijnenberg, D., Hömig-Hölzel, C., et al. (2014) · Nature · doi:10.1038/nature13111 · Cited by 623

The IntAct molecular interaction database in 2010

Aranda, B., Achuthan, P., Alam-Faruque, Y., Armean, I., Bridge, A., Derow, C., Feuermann, M., Ghanbarian, A. T., Kerrien, S., Khadake, J., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp878 · Cited by 620

A humanized version of Foxp2 affects cortico-basal ganglia circuits in mice

Enard, W., Gehre, S., Hammerschmidt, K., Hölter, S. M., Blass, T., Somel, M., Brückner, M. K., Schreiweis, C., Winter, C., Sohr, R., et al. (2009) · Cell · doi:10.1016/j.cell.2009.03.041 · Cited by 605

Paternal obesity initiates metabolic disturbances in two generations of mice with incomplete penetrance to the F 2 generation and alters the transcriptional profile of testis and sperm microRNA content

Fullston, T., Teague, E. M. C. O., Palmer, N. O., DeBlasio, M. J., Mitchell, M., Corbett, M., Print, C. G., Owens, J. A., & Lane, M. (2013) · The FASEB Journal · doi:10.1096/fj.12-224048 · Cited by 600

The transcription factors Blimp-1 and IRF4 jointly control the differentiation and function of effector regulatory T cells

Cretney, E., Xin, A., Shi, W., Minnich, M., Masson, F., Miasari, M., Belz, G. T., Smyth, G. K., Busslinger, M., Nutt, S. L., et al. (2011) · Nature Immunology · doi:10.1038/ni.2006 · Cited by 605

Step-wise methylation of histone H3K9 positions heterochromatin at the nuclear periphery

Towbin, B. D., González-Aguilera, C., Sack, R., Gaidatzis, D., Kalck, V., Meister, P., Askjaer, P., & Gasser, S. M. (2012) · Cell · doi:10.1016/j.cell.2012.06.051 · Cited by 597

Identification of Regulatory Networks in HSCs and Their Immediate Progeny via Integrated Proteome, Transcriptome, and DNA Methylome Analysis

Cabezas-Wallscheid, N., Klimmeck, D., Hansson, J., Lipka, D. B., Reyes, A., Wang, Q., Weichenhan, D., Lier, A., von Paleske, L., Renders, S., et al. (2014) · Cell Stem Cell · doi:10.1016/j.stem.2014.07.005 · Cited by 589

Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease

Lunnon, K., Smith, R., Hannon, E., De Jager, P. L., Srivastava, G., Volta, M., Troakes, C., Al-Sarraj, S., Burrage, J., Macdonald, R., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3782 · Cited by 574

The histone demethylase KDM1A sustains the oncogenic potential of MLL-AF9 leukemia stem cells

Harris, W. J., Huang, X., Lynch, J. T., Spencer, G. J., Hitchin, J. R., Li, Y., Ciceri, F., Blaser, J. G., Greystoke, B. F., Jordan, A. M., et al. (2012) · Cancer Cell · doi:10.1016/j.ccr.2012.03.014 · Cited by 566

Thermoregulatory and metabolic defects in Huntington's disease transgenic mice implicate PGC-1α in Huntington's disease neurodegeneration

Weydt, P., Pineda, V. V., Torrence, A. E., Libby, R. T., Satterfield, T. F., Lazarowski, E. R., Gilbert, M. L., Morton, G. J., Bammler, T. K., Strand, A. D., et al. (2006) · Cell Metabolism · doi:10.1016/j.cmet.2006.10.004 · Cited by 562

Large Expression Differences in Genes for Iron and Zinc Homeostasis, Stress Response, and Lignin Biosynthesis Distinguish Roots of Arabidopsis thaliana and the Related Metal Hyperaccumulator Thlaspi caerulescens

van de Mortel, J. E., Almar Villanueva, L., Schat, H., Kwekkeboom, J., Coughlan, S., Moerland, P. D., Ver Loren van Themaat, E., Koornneef, M., & Aarts, M. G. M. (2006) · Plant Physiology · doi:10.1104/pp.106.082073 · Cited by 547

Evidence for lifespan extension and delayed age–related biomarkers in insulin receptor substrate 1 null mice

Selman, C., Lingard, S., Choudhury, A. I., Batterham, R. L., Claret, M., Clements, M., Ramadani, F., Okkenhaug, K., Schuster, E., Blanc, E., et al. (2008) · The FASEB Journal · doi:10.1096/fj.07-9261com · Cited by 532

H3K4me3 breadth is linked to cell identity and transcriptional consistency

Benayoun, B. A., Pollina, E. A., Ucar, D., Mahmoudi, S., Karra, K., Wong, E. D., Devarajan, K., Daugherty, A. C., Kundaje, A. B., Mancini, E., et al. (2014) · Cell · doi:10.1016/j.cell.2014.06.027 · Cited by 536