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lumi: a pipeline for processing Illumina microarray
Open accessAbstract
Summary: Illumina microarray is becoming a popular microarray platform. The BeadArray technology from Illumina makes its preprocessing and quality control different from other microarray technologies. Unfortunately, most other analyses have not taken advantage of the unique properties of the BeadArray system, and have just incorporated preprocessing methods originally designed for Affymetrix microarrays. lumi is a Bioconductor package especially designed to process the Illumina microarray data. It includes data input, quality control, variance stabilization, normalization and gene annotation portions. In specific, the lumi package includes a variance-stabilizing transformation (VST) algorithm that takes advantage of the technical replicates available on every Illumina microarray. Different normalization method options and multiple quality control plots are provided in the package. To better annotate the Illumina data, a vendor independent nucleotide universal identifier (nuID) was devised to identify the probes of Illumina microarray. The nuID annotation packages and output of lumi processed results can be easily integrated with other Bioconductor packages to construct a statistical data analysis pipeline for Illumina data. Availability: The lumi Bioconductor package, www.bioconductor.org Contact: dupan@northwestern.edu
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Du, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W. A., Hou, L., & Lin, S. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-587 · Cited by 2,136
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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
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Using iPSC-derived neurons to uncover cellular phenotypes associated with Timothy syndrome
Paşca, S. P., Portmann, T., Voineagu, I., Yazawa, M., Shcheglovitov, A., Paşca, A. M., Cord, B., Palmer, T. D., Chikahisa, S., Nishino, S., et al. (2011) · Nature Medicine · doi:10.1038/nm.2576 · Cited by 585
Retinoid X receptor gamma signaling accelerates CNS remyelination
Huang, J. K., Jarjour, A. A., Nait Oumesmar, B., Kerninon, C., Williams, A., Krezel, W., Kagechika, H., Bauer, J., Zhao, C., Evercooren, A. B.-V., et al. (2011) · Nature Neuroscience · doi:10.1038/nn.2702 · Cited by 547
Ellison, G. M., Vicinanza, C., Smith, A. J., Aquila, I., Leone, A., Waring, C. D., Henning, B. J., Stirparo, G. G., Papait, R., Scarfò, M., et al. (2013) · Cell · doi:10.1016/j.cell.2013.07.039 · Cited by 515
Dayeh, T., Volkov, P., Salö, S., Hall, E., Nilsson, E., Olsson, A. H., Kirkpatrick, C. L., Wollheim, C. B., Eliasson, L., Rönn, T., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004160 · Cited by 523
IDH1(R132H) mutation increases murine haematopoietic progenitors and alters epigenetics
Sasaki, M., Knobbe, C. B., Munger, J. C., Lind, E. F., Brenner, D., Brüstle, A., Harris, I. S., Holmes, R., Wakeham, A., Haight, J., et al. (2012) · Nature · doi:10.1038/nature11323 · Cited by 513
Buddingh, E. P., Kuijjer, M. L., Duim, R. A. J., Bürger, H., Agelopoulos, K., Myklebost, O., Serra, M., Mertens, F., Hogendoorn, P. C. W., Lankester, A. C., et al. (2011) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-10-2047 · Cited by 508
Necroptosis activation in Alzheimer's disease
Caccamo, A., Branca, C., Piras, I. S., Ferreira, E., Huentelman, M. J., Liang, W. S., Readhead, B., Dudley, J. T., Spangenberg, E. E., Green, K. N., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4608 · Cited by 482
A compendium of uniformly processed human gene expression and splicing quantitative trait loci
Kerimov, N., Hayhurst, J. D., Peikova, K., Manning, J. R., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00924-w · Cited by 440
Allen, M., Carrasquillo, M. M., Funk, C., Heavner, B. D., Zou, F., Younkin, C. S., Burgess, J. D., Chai, H.-S., Crook, J., Eddy, J. A., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.89 · Cited by 439
DNA methylation contributes to natural human variation
Heyn, H., Moran, S., Hernando-Herraez, I., Sayols, S., Gomez, A., Sandoval, J., Monk, D., Hata, K., Marques-Bonet, T., Wang, L., et al. (2013) · Genome Research · doi:10.1101/gr.154187.112 · Cited by 408
Lehne, B., Drong, A. W., Loh, M., Zhang, W., Scott, W. R., Tan, S.-T., Afzal, U., Scott, J., Jarvelin, M.-R., Elliott, P., et al. (2015) · Genome biology · doi:10.1186/s13059-015-0600-x · Cited by 380
Recommendations for the design and analysis of epigenome-wide association studies
Michels, K. B., Binder, A. M., Dedeurwaerder, S., Epstein, C. B., Greally, J. M., Gut, I., Houseman, E. A., Izzi, B., Kelsey, K. T., Meissner, A., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2632 · Cited by 378
Silvin, A., Uderhardt, S., Piot, C., Da Mesquita, S., Yang, K., Geirsdottir, L., Mulder, K., Eyal, D., Liu, Z., Bridlance, C., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.07.004 · Cited by 368
Batch effect removal methods for microarray gene expression data integration: a survey
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter, C., Weiss-Solis, D. Y., Duque, R., Bersini, H., & Nowe, A. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs037 · Cited by 333
A Myc enhancer cluster regulates normal and leukaemic haematopoietic stem cell hierarchies
Bahr, C., von Paleske, L., Uslu, V. V., Remeseiro, S., Takayama, N., Ng, S. W., Murison, A., Langenfeld, K., Petretich, M., Scognamiglio, R., et al. (2018) · Nature · doi:10.1038/nature25193 · Cited by 328
The miR-144/451 locus is required for erythroid homeostasis
Rasmussen, K. D., Simmini, S., Abreu-Goodger, C., Bartonicek, N., Di Giacomo, M., Bilbao-Cortes, D., Horos, R., Von Lindern, M., Enright, A. J., & O’Carroll, D. (2010) · Journal of Experimental Medicine · doi:10.1084/jem.20100458 · Cited by 330
The Epidermis Comprises Autonomous Compartments Maintained by Distinct Stem Cell Populations
Page, M. E., Lombard, P., Ng, F., Göttgens, B., & Jensen, K. B. (2013) · Cell Stem Cell · doi:10.1016/j.stem.2013.07.010 · Cited by 320
Arendt, B. M., Comelli, E. M., Ma, D. W. L., Lou, W., Teterina, A., Kim, T., Fung, S. K., Wong, D. K. H., McGilvray, I., Fischer, S. E., et al. (2015) · Hepatology · doi:10.1002/hep.27695 · Cited by 313
Sampaziotis, F., Justin, A. W., Tysoe, O. C., Sawiak, S., Godfrey, E. M., Upponi, S. S., Gieseck, R. L., de Brito, M. C., Berntsen, N. L., Gómez-Vázquez, M. J., et al. (2017) · Nature Medicine · doi:10.1038/nm.4360 · Cited by 296
Social environment is associated with gene regulatory variation in the rhesus macaque immune system
Tung, J., Barreiro, L. B., Johnson, Z. P., Hansen, K. D., Michopoulos, V., Toufexis, D., Michelini, K., Wilson, M. E., & Gilad, Y. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1202734109 · Cited by 277
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Zaidi, M. R., Davis, S., Noonan, F. P., Graff-Cherry, C., Hawley, T. S., Walker, R. L., Feigenbaum, L., Fuchs, E., Lyakh, L., Young, H. A., et al. (2011) · Nature · doi:10.1038/nature09666 · Cited by 293
Kong, Y., Rose, C. M., Cass, A. A., Williams, A. G., Darwish, M., Lianoglou, S., Haverty, P. M., Tong, A.-J., Blanchette, C., Albert, M. L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13035-2 · Cited by 285
Metabolic adaptation establishes disease tolerance to sepsis
Weis, S., Carlos, A. R., Moita, M. R., Singh, S., Blankenhaus, B., Cardoso, S., Larsen, R., Rebelo, S., Schäuble, S., Del Barrio, L., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.031 · Cited by 284
Differences in smoking associated DNA methylation patterns in south asians and europeans
Elliott, H. R., Tillin, T., McArdle, W. L., Ho, K., Duggirala, A., Frayling, T. M., Davey Smith, G., Hughes, A. D., Chaturvedi, N., & Relton, C. L. (2014) · Clinical Epigenetics · doi:10.1186/1868-7083-6-4 · Cited by 282
Ronn, T., Volkov, P., Gillberg, L., Kokosar, M., Perfilyev, A., Jacobsen, A. L., Jorgensen, S. W., Brons, C., Jansson, P.-A., Eriksson, K.-F., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv124 · Cited by 270
Australian Asthma Genetics Consortium (AAGC), Bønnelykke, K., the EArly Genetics and Lifecourse Epidemiology (EAGLE) Consortium, Matheson, M. C., Pers, T. H., Granell, R., Strachan, D. P., Alves, A. C., Linneberg, A., Curtin, J. A., et al. (2013) · Nature Genetics · doi:10.1038/ng.2694 · Cited by 244
Fetuin B is a secreted hepatocyte factor linking steatosis to impaired glucose metabolism
Meex, R. C., Hoy, A. J., Morris, A., Brown, R. D., Lo, J. C. Y., Burke, M., Goode, R. J. A., Kingwell, B. A., Kraakman, M. J., Febbraio, M. A., et al. (2015) · Cell Metabolism · doi:10.1016/j.cmet.2015.09.023 · Cited by 255
Partial exhaustion of CD8 T cells and clinical response to teplizumab in new-onset type 1 diabetes
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Hall, E., Volkov, P., Dayeh, T., Esguerra, J. L. S., Salö, S., Eliasson, L., Rönn, T., Bacos, K., & Ling, C. (2014) · Genome Biology · doi:10.1186/s13059-014-0522-z · Cited by 249
IL-15, gluten and HLA-DQ8 drive tissue destruction in coeliac disease
Abadie, V., Kim, S. M., Lejeune, T., Palanski, B. A., Ernest, J. D., Tastet, O., Voisine, J., Discepolo, V., Marietta, E. V., Hawash, M. B. F., et al. (2020) · Nature · doi:10.1038/s41586-020-2003-8 · Cited by 231
Optimizing the noise versus bias trade-off for Illumina Whole Genome Expression Beadchips
Shi, W., Oshlack, A., & Smyth, G. K. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq871 · Cited by 225
Influence of donor age on induced pluripotent stem cells
Lo Sardo, V., Ferguson, W., Erikson, G. A., Topol, E. J., Baldwin, K. K., & Torkamani, A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3749 · Cited by 223
de la Rica, L., Rodríguez-Ubreva, J., García, M., Islam, A. B., Urquiza, J. M., Hernando, H., Christensen, J., Helin, K., Gómez-Vaquero, C., & Ballestar, E. (2013) · Genome Biology · doi:10.1186/gb-2013-14-9-r99 · Cited by 211
“In order to process Illumina Infinium HumanMethylation450 methylation data, we used the methods supplied in limma [56], genefilter, and lumi [57] packages from Bioconductor repository.”
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Liang, L., Willis-Owen, S. A. G., Laprise, C., Wong, K. C. C., Davies, G. A., Hudson, T. J., Binia, A., Hopkin, J. M., Yang, I. V., Grundberg, E., et al. (2015) · Nature · doi:10.1038/nature14125 · Cited by 213
Dyson, M. T., Roqueiro, D., Monsivais, D., Ercan, C. M., Pavone, M. E., Brooks, D. C., Kakinuma, T., Ono, M., Jafari, N., Dai, Y., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004158 · Cited by 211
Chow, M. L., Pramparo, T., Winn, M. E., Barnes, C. C., Li, H.-R., Weiss, L., Fan, J.-B., Murray, S., April, C., Belinson, H., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002592 · Cited by 202
Watson, C. T., Roussos, P., Garg, P., Ho, D. J., Azam, N., Katsel, P. L., Haroutunian, V., & Sharp, A. J. (2016) · Genome Medicine · doi:10.1186/s13073-015-0258-8 · Cited by 201
Gray, S. P., Di Marco, E., Kennedy, K., Chew, P., Okabe, J., El-Osta, A., Calkin, A. C., Biessen, E. A. L., Touyz, R. M., Cooper, M. E., et al. (2016) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/atvbaha.115.307012 · Cited by 189
MEK inhibitors activate Wnt signalling and induce stem cell plasticity in colorectal cancer
Zhan, T., Ambrosi, G., Wandmacher, A. M., Rauscher, B., Betge, J., Rindtorff, N., Häussler, R. S., Hinsenkamp, I., Bamberg, L., Hessling, B., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09898-0 · Cited by 189
Ptasinska, A., Assi, S. A., Mannari, D., James, S. R., Williamson, D., Dunne, J., Hoogenkamp, M., Wu, M., Care, M., McNeill, H., et al. (2012) · Leukemia · doi:10.1038/leu.2012.49 · Cited by 184
Hongisto, V., Jernström, S., Fey, V., Mpindi, J.-P., Kleivi Sahlberg, K., Kallioniemi, O., & Perälä, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077232 · Cited by 181
Gutowska-Owsiak, D., Schaupp, A. L., Salimi, M., Taylor, S., & Ogg, G. S. (2011) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2011.10400.x · Cited by 168
The in vivo endothelial cell translatome is highly heterogeneous across vascular beds
Cleuren, A. C. A., van der Ent, M. A., Jiang, H., Hunker, K. L., Yee, A., Siemieniak, D. R., Molema, G., Aird, W. C., Ganesh, S. K., & Ginsburg, D. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912409116 · Cited by 152
Sex-biased methylome and transcriptome in human prefrontal cortex
Xu, H., Wang, F., Liu, Y., Yu, Y., Gelernter, J., & Zhang, H. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddt516 · Cited by 151
Tumor-associated B-cells induce tumor heterogeneity and therapy resistance
Somasundaram, R., Zhang, G., Fukunaga-Kalabis, M., Perego, M., Krepler, C., Xu, X., Wagner, C., Hristova, D., Zhang, J., Tian, T., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00452-4 · Cited by 151
Giraldo, M., Lopera, F., Siniard, A. L., Corneveaux, J. J., Schrauwen, I., Carvajal, J., Muñoz, C., Ramirez-Restrepo, M., Gaiteri, C., Myers, A. J., et al. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.02.016 · Cited by 147
Thériault, S., Gaudreault, N., Lamontagne, M., Rosa, M., Boulanger, M.-C., Messika-Zeitoun, D., Clavel, M.-A., Capoulade, R., Dagenais, F., Pibarot, P., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03260-6 · Cited by 135
miR-139-5p is a regulator of metastatic pathways in breast cancer
Krishnan, K., Steptoe, A. L., Martin, H. C., Pattabiraman, D. R., Nones, K., Waddell, N., Mariasegaram, M., Simpson, P. T., Lakhani, S. R., Vlassov, A., et al. (2013) · RNA · doi:10.1261/rna.042143.113 · Cited by 134
Identification of a CpG Island Methylator Phenotype in Adrenocortical Carcinomas
Barreau, O., Assié, G., Wilmot-Roussel, H., Ragazzon, B., Baudry, C., Perlemoine, K., René-Corail, F., Bertagna, X., Dousset, B., Hamzaoui, N., et al. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-2993 · Cited by 132
Radtke, K. M., Schauer, M., Gunter, H. M., Ruf-Leuschner, M., Sill, J., Meyer, A., & Elbert, T. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.63 · Cited by 128
Generation of folliculogenic human epithelial stem cells from induced pluripotent stem cells
Yang, R., Zheng, Y., Burrows, M., Liu, S., Wei, Z., Nace, A., Guo, W., Kumar, S., Cotsarelis, G., & Xu, X. (2014) · Nature Communications · doi:10.1038/ncomms4071 · Cited by 125
Prenatal tobacco smoke exposure is associated with childhood DNA cpg methylation
Breton, C. V., Siegmund, K. D., Joubert, B. R., Wang, X., Qui, W., Carey, V., Nystad, W., Håberg, S. E., Ober, C., Nicolae, D., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0099716 · Cited by 119
Utilizing RNA-Seq data for de novo coexpression network inference
Iancu, O. D., Kawane, S., Bottomly, D., Searles, R., Hitzemann, R., & McWeeney, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts245 · Cited by 114
qSVA framework for RNA quality correction in differential expression analysis
Jaffe, A. E., Tao, R., Norris, A. L., Kealhofer, M., Nellore, A., Shin, J. H., Kim, D., Jia, Y., Hyde, T. M., Kleinman, J. E., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617384114 · Cited by 114
Briggs, C. E., Wang, Y., Kong, B., Woo, T.-U. W., Iyer, L. K., & Sonntag, K. C. (2015) · Brain Research · doi:10.1016/j.brainres.2015.05.021 · Cited by 113
Kasela, S., Kisand, K., Tserel, L., Kaleviste, E., Remm, A., Fischer, K., Esko, T., Westra, H.-J., Fairfax, B. P., Makino, S., et al. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006643 · Cited by 113
Kawaji, H., Lizio, M., Itoh, M., Kanamori-Katayama, M., Kaiho, A., Nishiyori-Sueki, H., Shin, J. W., Kojima-Ishiyama, M., Kawano, M., Murata, M., et al. (2014) · Genome Research · doi:10.1101/gr.156232.113 · Cited by 109
Batch effect correction for genome-wide methylation data with Illumina Infinium platform
Sun, Z., Chai, H. S., Wu, Y., White, W. M., Donkena, K. V., Klein, C. J., Garovic, V. D., Therneau, T. M., & Kocher, J.-P. A. (2011) · BMC Medical Genomics · doi:10.1186/1755-8794-4-84 · Cited by 108
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kConFab Investigators, Waddell, N., Arnold, J., Cocciardi, S., da Silva, L., Marsh, A., Riley, J., Johnstone, C. N., Orloff, M., Assie, G., et al. (2010) · Breast Cancer Research and Treatment · doi:10.1007/s10549-009-0653-1 · Cited by 99
Guauque-Olarte, S., Droit, A., Tremblay-Marchand, J., Gaudreault, N., Kalavrouziotis, D., Dagenais, F., Seidman, J. G., Body, S. C., Pibarot, P., Mathieu, P., et al. (2016) · Physiological Genomics · doi:10.1152/physiolgenomics.00041.2016 · Cited by 82
Monsivais, D., Dyson, M. T., Yin, P., Coon, J. S., Navarro, A., Feng, G., Malpani, S. S., Ono, M., Ercan, C. M., Wei, J. J., et al. (2014) · Molecular Endocrinology · doi:10.1210/me.2013-1421 · Cited by 91
Mooney, M. A., Ryabinin, P., Wilmot, B., Bhatt, P., Mill, J., & Nigg, J. T. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0710-4 · Cited by 91
Arwert, E. N., Milford, E. L., Rullan, A., Derzsi, S., Hooper, S., Kato, T., Mansfield, D., Melcher, A., Harrington, K. J., & Sahai, E. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0527-7 · Cited by 90
Exposure to childhood abuse is associated with human sperm DNA methylation
Roberts, A. L., Gladish, N., Gatev, E., Jones, M. J., Chen, Y., MacIsaac, J. L., Tworoger, S. S., Austin, S. B., Tanrikut, C., Chavarro, J. E., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0252-1 · Cited by 84
Pramparo, T., Lombardo, M. V., Campbell, K., Barnes, C. C., Marinero, S., Solso, S., Young, J., Mayo, M., Dale, A., Ahrens‐Barbeau, C., et al. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156108 · Cited by 88
Variability in the generation of induced pluripotent stem cells: Importance for disease modeling
Vitale, A. M., Matigian, N. A., Ravishankar, S., Bellette, B., Wood, S. A., Wolvetang, E. J., & Mackay-Sim, A. (2012) · Stem Cells Translational Medicine · doi:10.5966/sctm.2012-0043 · Cited by 79
Sharma, A., Jamil, M. A., Nuesgen, N., Schreiner, F., Priebe, L., Hoffmann, P., Herns, S., Nöthen, M. M., Fröhlich, H., Oldenburg, J., et al. (2015) · Clinical Epigenetics · doi:10.1186/s13148-015-0112-2 · Cited by 81
Calcium Signaling Pathway Genes RUNX2 and CACNA1C Are Associated With Calcific Aortic Valve Disease
Guauque-Olarte, S., Messika-Zeitoun, D., Droit, A., Lamontagne, M., Tremblay-Marchand, J., Lavoie-Charland, E., Gaudreault, N., Arsenault, B. J., Dubé, M.-P., Tardif, J.-C., et al. (2015) · Circulation: Cardiovascular Genetics · doi:10.1161/circgenetics.115.001145 · Cited by 72
Transcriptomic profiles of aging in purified human immune cells
Reynolds, L. M., Ding, J., Taylor, J. R., Lohman, K., Soranzo, N., de la Fuente, A., Liu, T. F., Johnson, C., Barr, R. G., Register, T. C., et al. (2015) · BMC Genomics · doi:10.1186/s12864-015-1522-4 · Cited by 62
Wu, I., Shin, S. C., Cao, Y., Bender, I. K., Jafari, N., Feng, G., Lin, S., Cidlowski, J. A., Schleimer, R. P., & Lu, N. Z. (2013) · Cell Death & Disease · doi:10.1038/cddis.2012.193 · Cited by 64
Regulation of extra-embryonic endoderm stem cell differentiation by Nodal and Cripto signaling
Julio, M. K.-D., Alvarez, M. J., Galli, A., Chu, J., Price, S. M., Califano, A., & Shen, M. M. (2011) · Development · doi:10.1242/dev.065656 · Cited by 62
Liu, L., Xie, Y., Yang, H., Lin, A., Dong, M., Wang, H., Zhang, C., Liu, Z., Cheng, Q., Zhang, J., et al. (2023) · iMeta · doi:10.1002/imt2.130 · Cited by 62
Epigenome-wide association study of DNA methylation in panic disorder
Shimada-Sugimoto, M., Otowa, T., Miyagawa, T., Umekage, T., Kawamura, Y., Bundo, M., Iwamoto, K., Tochigi, M., Kasai, K., Kaiya, H., et al. (2017) · Clinical Epigenetics · doi:10.1186/s13148-016-0307-1 · Cited by 61
Pentraxin-2 suppresses c-Jun/AP-1 signaling to inhibit progressive fibrotic disease
Nakagawa, N., Barron, L., Gomez, I. G., Johnson, B. G., Roach, A. M., Kameoka, S., Jack, R. M., Lupher, M. L., Gharib, S. A., & Duffield, J. S. (2016) · JCI Insight · doi:10.1172/jci.insight.87446 · Cited by 59
van Koppen, A., Verschuren, L., van den Hoek, A. M., Verheij, J., Morrison, M. C., Li, K., Nagabukuro, H., Costessi, A., Caspers, M. P. M., van den Broek, T. J., et al. (2018) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2017.10.001 · Cited by 58
Khulan, B., Manning, J. R., Dunbar, D. R., Seckl, J. R., Raikkonen, K., Eriksson, J. G., & Drake, A. J. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.94 · Cited by 59
Verschoor, C. P., Lin, D. T. S., Kobor, M. S., Mian, O., Ma, J., Pare, G., & Ybazeta, G. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01150-1 · Cited by 58
Proitsi, P., Lee, S. H., Lunnon, K., Keohane, A., Powell, J., Troakes, C., Al-Sarraj, S., Furney, S., Soininen, H., Kłoszewska, I., et al. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.08.002 · Cited by 57
Garcia-Gomez, A., Li, T., de la Calle-Fabregat, C., Rodríguez-Ubreva, J., Ciudad, L., Català-Moll, F., Godoy-Tena, G., Martín-Sánchez, M., San-Segundo, L., Muntión, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20715-x · Cited by 57
Liu, S., Tetzlaff, M. T., Wang, T., Yang, R., Xie, L., Zhang, G., Krepler, C., Xiao, M., Beqiri, M., Xu, W., et al. (2015) · Pigment Cell & Melanoma Research · doi:10.1111/pcmr.12379 · Cited by 54
Natri, H. M., Bobowik, K. S., Kusuma, P., Crenna Darusallam, C., Jacobs, G. S., Hudjashov, G., Lansing, J. S., Sudoyo, H., Banovich, N. E., Cox, M. P., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008749 · Cited by 48
Li, C., Singh, B., Graves-Deal, R., Ma, H., Starchenko, A., Fry, W. H., Lu, Y., Wang, Y., Bogatcheva, G., Khan, M. P., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618297114 · Cited by 47
Irf8-Regulated Genomic Responses Drive Pathological Inflammation during Cerebral Malaria
Berghout, J., Langlais, D., Radovanovic, I., Tam, M., MacMicking, J. D., Stevenson, M. M., & Gros, P. (2013) · PLoS Pathogens · doi:10.1371/journal.ppat.1003491 · Cited by 44
Paternal adverse childhood experiences: Associations with infant DNA methylation
Merrill, S. M., Moore, S. R., Gladish, N., Giesbrecht, G. F., Dewey, D., Konwar, C., MacIssac, J. L., Kobor, M. S., & Letourneau, N. L. (2021) · Developmental Psychobiology · doi:10.1002/dev.22174 · Cited by 42
Mallika, C., Guo, Q., & Li, J. Y. H. (2015) · Developmental Biology · doi:10.1016/j.ydbio.2015.08.010 · Cited by 43
“Background correction, variance stabilizing transformation and normalization were performed using the background correction, VST and RSN methods implemented in the lumi package (Du et al., 2008).”
MethodVergani, E., Dugo, M., Cossa, M., Frigerio, S., Di Guardo, L., Gallino, G., Mattavelli, I., Vergani, B., Lalli, L., Tamborini, E., et al. (2020) · Cell Communication and Signaling · doi:10.1186/s12964-020-00601-1 · Cited by 39
Natri, H. M., Hudjashov, G., Jacobs, G., Kusuma, P., Saag, L., Darusallam, C. C., Metspalu, M., Sudoyo, H., Cox, M. P., Gallego Romero, I., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.017 · Cited by 33
Metadata
- Authors
- Du, P., Kibbe, W. A., & Lin, S. M.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-05-08
- Volume
- 24
- Issue
- 13
- Pages
- 1547-1548
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2106608030
- Cited by
- 2,019
- References
- 0
- FWCI
- 25.81
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
