{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T05:56:58Z","timestamp":1778133418980,"version":"3.51.4"},"reference-count":133,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-021-00086-z","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T16:04:08Z","timestamp":1624291448000},"page":"395-402","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":161,"title":["Undisclosed, unmet and neglected challenges in multi-omics studies"],"prefix":"10.1038","volume":"1","author":[{"given":"Sonia","family":"Tarazona","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2959-0134","authenticated-orcid":false,"given":"Angeles","family":"Arzalluz-Luque","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9597-311X","authenticated-orcid":false,"given":"Ana","family":"Conesa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"86_CR1","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s11306-005-0012-0","volume":"1","author":"TWM Fan","year":"2005","unstructured":"Fan, T. W. M., Bandura, L. L., Higashi, R. M. & Lane, A. N. Metabolomics-edited transcriptomics analysis of Se anticancer action in human lung cancer cells. Metabolomics 1, 325\u2013339 (2005).","journal-title":"Metabolomics"},{"key":"86_CR2","doi-asserted-by":"publisher","first-page":"e8760","DOI":"10.1371\/journal.pone.0008760","volume":"5","author":"SK Panguluri","year":"2010","unstructured":"Panguluri, S. K. et al. Genomic profiling of messenger RNAs and microRNAs reveals potential mechanisms of TWEAK-induced skeletal muscle wasting in mice. PLoS ONE 5, e8760 (2010).","journal-title":"PLoS ONE"},{"key":"86_CR3","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1101\/gr.121541.111","volume":"21","author":"L Song","year":"2011","unstructured":"Song, L. et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res. 21, 1757\u20131767 (2011).","journal-title":"Genome Res."},{"key":"86_CR4","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-017-0126-8","volume":"10","author":"S Kim","year":"2017","unstructured":"Kim, S., Jhong, J.-H., Lee, J. & Koo, J.-Y. Meta-analytic support vector machine for integrating multiple omics data. BioData Min. 10, 2 (2017).","journal-title":"BioData Min."},{"key":"86_CR5","doi-asserted-by":"publisher","first-page":"i237","DOI":"10.1093\/bioinformatics\/btq182","volume":"26","author":"CJ Vaske","year":"2010","unstructured":"Vaske, C. J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237\u2013i245 (2010).","journal-title":"Bioinformatics"},{"key":"86_CR6","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1093\/biostatistics\/kxx017","volume":"19","author":"Q Mo","year":"2017","unstructured":"Mo, Q. et al. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19, 71\u201386 (2017).","journal-title":"Biostatistics"},{"key":"86_CR7","doi-asserted-by":"publisher","first-page":"e8124","DOI":"10.15252\/msb.20178124","volume":"14","author":"R Argelaguet","year":"2018","unstructured":"Argelaguet, R. et al. Multi-omics factor analysis\u2014a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).","journal-title":"Mol. Syst. Biol."},{"key":"86_CR8","doi-asserted-by":"publisher","first-page":"e1005752","DOI":"10.1371\/journal.pcbi.1005752","volume":"13","author":"F Rohart","year":"2017","unstructured":"Rohart, F., Gautier, B., Singh, A. & L\u00ea Cao, K.-A. mixOmics: an R package for \u2018omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).","journal-title":"PLoS Comput. Biol."},{"key":"86_CR9","doi-asserted-by":"publisher","first-page":"477","DOI":"10.3389\/fgene.2018.00477","volume":"9","author":"L Zhang","year":"2018","unstructured":"Zhang, L. et al. Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma. Front. Genet. 9, 477 (2018).","journal-title":"Front. Genet."},{"key":"86_CR10","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1186\/s12864-019-6285-x","volume":"20","author":"T Ma","year":"2019","unstructured":"Ma, T. & Zhang, A. Integrate multi-omics data with biological interaction networks using multi-view factorization autoencoder (MAE). BMC Genomics 20, 944 (2019).","journal-title":"BMC Genomics"},{"key":"86_CR11","doi-asserted-by":"publisher","first-page":"166","DOI":"10.3389\/fgene.2019.00166","volume":"10","author":"Z Huang","year":"2019","unstructured":"Huang, Z. et al. SALMON: survival analysis learning with multi-omics neural networks on breast cancer. Front. Genet. 10, 166 (2019).","journal-title":"Front. Genet."},{"key":"86_CR12","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s12859-015-0857-9","volume":"17","author":"M Bersanelli","year":"2016","unstructured":"Bersanelli, M. et al. Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17, 15 (2016).","journal-title":"BMC Bioinformatics"},{"key":"86_CR13","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1093\/bib\/bbz136","volume":"21","author":"R De Bin","year":"2020","unstructured":"De Bin, R., Boulesteix, A.-L., Benner, A., Becker, N. & Sauerbrei, W. Combining clinical and molecular data in regression prediction models: insights from a simulation study. Brief. Bioinform. 21, 1904\u20131919 (2020).","journal-title":"Brief. Bioinform."},{"key":"86_CR14","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1093\/bib\/bbz138","volume":"21","author":"M Pierre-Jean","year":"2020","unstructured":"Pierre-Jean, M., Deleuze, J.-F., Le Floch, E. & Mauger, F. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration. Brief. Bioinform. 21, 2011\u20132030 (2020).","journal-title":"Brief. Bioinform."},{"key":"86_CR15","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1093\/bib\/bbv108","volume":"17","author":"C Meng","year":"2016","unstructured":"Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17, 628\u2013641 (2016).","journal-title":"Brief. Bioinform."},{"key":"86_CR16","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s40170-016-0143-y","volume":"4","author":"JM Buescher","year":"2016","unstructured":"Buescher, J. M. & Driggers, E. M. Integration of omics: more than the sum of its parts. Cancer Metab. 4, 4 (2016).","journal-title":"Cancer Metab."},{"key":"86_CR17","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s13059-017-1215-1","volume":"18","author":"Y Hasin","year":"2017","unstructured":"Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).","journal-title":"Genome Biol."},{"key":"86_CR18","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1038\/nrc3721","volume":"14","author":"VN Kristensen","year":"2014","unstructured":"Kristensen, V. N. et al. Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer 14, 299\u2013313 (2014).","journal-title":"Nat. Rev. Cancer"},{"key":"86_CR19","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1093\/bib\/bbz121","volume":"21","author":"A Sathyanarayanan","year":"2020","unstructured":"Sathyanarayanan, A. et al. A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping. Brief. Bioinform. 21, 1920\u20131936 (2020).","journal-title":"Brief. Bioinform."},{"key":"86_CR20","doi-asserted-by":"publisher","first-page":"9960","DOI":"10.18632\/aging.202752","volume":"13","author":"H Zeng","year":"2021","unstructured":"Zeng, H. et al. Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma. Aging 13, 9960\u20139975 (2021).","journal-title":"Aging"},{"key":"86_CR21","doi-asserted-by":"publisher","unstructured":"Kirienko, M. et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur. J. Nucl. Med. Mol. Imaging https:\/\/doi.org\/10.1007\/s00259-021-05371-7 (2021).","DOI":"10.1007\/s00259-021-05371-7"},{"key":"86_CR22","doi-asserted-by":"publisher","first-page":"590742","DOI":"10.3389\/fimmu.2021.590742","volume":"12","author":"JM Zielinski","year":"2021","unstructured":"Zielinski, J. M., Luke, J. J., Guglietta, S. & Krieg, C. High throughput multi-omics approaches for clinical trial evaluation and drug discovery. Front. Immunol. 12, 590742 (2021).","journal-title":"Front. Immunol."},{"key":"86_CR23","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1038\/nrg2897","volume":"11","author":"D Houle","year":"2010","unstructured":"Houle, D., Govindaraju, D. R. & Omholt, S. Phenomics: the next challenge. Nat. Rev. Genet. 11, 855\u2013866 (2010).","journal-title":"Nat. Rev. Genet."},{"key":"86_CR24","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1111\/tpj.14190","volume":"97","author":"RFHM van Bezouw","year":"2019","unstructured":"van Bezouw, R. F. H. M., Keurentjes, J. J. B., Harbinson, J. & Aarts, M. G. M. Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency. Plant J. Cell Mol. Biol. 97, 112\u2013133 (2019).","journal-title":"Plant J. Cell Mol. Biol."},{"key":"86_CR25","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1093\/biostatistics\/kxw010","volume":"17","author":"R Zhu","year":"2016","unstructured":"Zhu, R., Zhao, Q., Zhao, H. & Ma, S. Integrating multidimensional omics data for cancer outcome. Biostatistics 17, 605\u2013618 (2016).","journal-title":"Biostatistics"},{"key":"86_CR26","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13059-021-02262-w","volume":"22","author":"L Balzano-Nogueira","year":"2021","unstructured":"Balzano-Nogueira, L. et al. Integrative analyses of TEDDY omics data reveal lipid metabolism abnormalities, increased intracellular ROS and heightened inflammation prior to autoimmunity for type 1 diabetes. Genome Biol. 22, 39 (2021).","journal-title":"Genome Biol."},{"key":"86_CR27","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1093\/bioinformatics\/btp543","volume":"25","author":"R Shen","year":"2009","unstructured":"Shen, R., Olshen, A. B. & Ladanyi, M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906\u20132912 (2009).","journal-title":"Bioinformatics"},{"key":"86_CR28","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-2-63","volume":"2","author":"B Yener","year":"2008","unstructured":"Yener, B. et al. Multiway modeling and analysis in stem cell systems biology. BMC Syst. Biol. 2, 63 (2008).","journal-title":"BMC Syst. Biol."},{"key":"86_CR29","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.chemolab.2010.06.004","volume":"104","author":"A Conesa","year":"2010","unstructured":"Conesa, A., Prats-Montalb\u00e1n, J. M., Tarazona, S., Nueda, M. J. & Ferrer, A. A multiway approach to data integration in systems biology based on Tucker3 and N-PLS. Chemom. Intell. Lab. Syst. 104, 101\u2013111 (2010).","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"86_CR30","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/1471-2105-15-162","volume":"15","author":"C Meng","year":"2014","unstructured":"Meng, C., Kuster, B., Culhane, A. C. & Gholami, A. M. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics 15, 162 (2014).","journal-title":"BMC Bioinformatics"},{"key":"86_CR31","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12859-016-1037-2","volume":"17","author":"FM van der Kloet","year":"2016","unstructured":"van der Kloet, F. M., Sebasti\u00e1n-Le\u00f3n, P., Conesa, A., Smilde, A. K. & Westerhuis, J. A. Separating common from distinctive variation. BMC Bioinformatics 17, 195 (2016).","journal-title":"BMC Bioinformatics"},{"key":"86_CR32","doi-asserted-by":"publisher","first-page":"2877","DOI":"10.1093\/bioinformatics\/btw324","volume":"32","author":"MJ O\u2019Connell","year":"2016","unstructured":"O\u2019Connell, M. J. & Lock, E. F. R.JIVE for exploration of multi-source molecular data. Bioinformatics 32, 2877\u20132879 (2016).","journal-title":"Bioinformatics"},{"key":"86_CR33","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1186\/s12859-018-2371-3","volume":"19","author":"SE Bouhaddani","year":"2018","unstructured":"Bouhaddani, S. E. et al. Integrating omics datasets with the OmicsPLS package. BMC Bioinformatics 19, 371 (2018).","journal-title":"BMC Bioinformatics"},{"key":"86_CR34","doi-asserted-by":"publisher","first-page":"143","DOI":"10.3389\/fgene.2021.620453","volume":"12","author":"N Planell","year":"2021","unstructured":"Planell, N. et al. STATegra: multi-omics data integration\u2014a conceptual scheme with a bioinformatics pipeline. Front. Genet. 12, 143 (2021).","journal-title":"Front. Genet."},{"key":"86_CR35","doi-asserted-by":"publisher","first-page":"7691937","DOI":"10.1155\/2017\/7691937","volume":"2017","author":"A-L Boulesteix","year":"2017","unstructured":"Boulesteix, A.-L., De Bin, R., Jiang, X. & Fuchs, M. IPF-LASSO: integrative L(1)-penalized regression with penalty factors for prediction based on multi-omics data. Comput. Math. Methods Med. 2017, 7691937 (2017).","journal-title":"Comput. Math. Methods Med."},{"key":"86_CR36","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-018-4842-3","volume":"19","author":"EM Kennedy","year":"2018","unstructured":"Kennedy, E. M. et al. An integrated -omics analysis of the epigenetic landscape of gene expression in human blood cells. BMC Genomics 19, 476 (2018).","journal-title":"BMC Genomics"},{"key":"86_CR37","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/s12859-016-0951-7","volume":"17","author":"M-Y Wu","year":"2016","unstructured":"Wu, M.-Y. et al. Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer. BMC Bioinformatics 17, 108 (2016).","journal-title":"BMC Bioinformatics"},{"key":"86_CR38","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/ht8010004","volume":"8","author":"C Wu","year":"2019","unstructured":"Wu, C. et al. A selective review of multi-level omics data integration using variable selection. High Throughput 8, 4 (2019).","journal-title":"High Throughput"},{"key":"86_CR39","doi-asserted-by":"publisher","first-page":"e201303004","DOI":"10.5936\/csbj.201303004","volume":"6","author":"V Lagani","year":"2013","unstructured":"Lagani, V., Kortas, G. & Tsamardinos, I. Biomarker signature identification in \u2018omics\u2019 data with multi-class outcome. Comput. Struct. Biotechnol. J. 6, e201303004 (2013).","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"86_CR40","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1093\/bfgp\/elaa013","volume":"19","author":"D-H Le","year":"2020","unstructured":"Le, D.-H. Machine learning-based approaches for disease gene prediction. Brief. Funct. Genomics 19, 350\u2013363 (2020).","journal-title":"Brief. Funct. Genomics"},{"key":"86_CR41","doi-asserted-by":"publisher","first-page":"3172","DOI":"10.1093\/bioinformatics\/btv349","volume":"31","author":"H Fang","year":"2015","unstructured":"Fang, H., Huang, C., Zhao, H. & Deng, M. CCLasso: correlation inference for compositional data through Lasso. Bioinformatics 31, 3172\u20133180 (2015).","journal-title":"Bioinformatics"},{"key":"86_CR42","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1186\/s12859-018-2344-6","volume":"19","author":"S Klau","year":"2018","unstructured":"Klau, S., Jurinovic, V., Hornung, R., Herold, T. & Boulesteix, A.-L. Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data. BMC Bioinformatics 19, 322 (2018).","journal-title":"BMC Bioinformatics"},{"key":"86_CR43","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1093\/bioinformatics\/btz822","volume":"36","author":"J Li","year":"2020","unstructured":"Li, J., Lu, Q. & Wen, Y. Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data. Bioinformatics 36, 1785\u20131794 (2020).","journal-title":"Bioinformatics"},{"key":"86_CR44","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1089\/cmb.2014.0197","volume":"22","author":"H Park","year":"2015","unstructured":"Park, H., Niida, A., Miyano, S. & Imoto, S. Sparse overlapping group lasso for integrative multi-omics analysis. J. Comput. Biol. 22, 73\u201384 (2015).","journal-title":"J. Comput. Biol."},{"key":"86_CR45","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1093\/bioinformatics\/bty1054","volume":"35","author":"A Singh","year":"2019","unstructured":"Singh, A. et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35, 3055\u20133062 (2019).","journal-title":"Bioinformatics"},{"key":"86_CR46","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41598-020-57691-7","volume":"10","author":"NL Patel-Murray","year":"2020","unstructured":"Patel-Murray, N. L. et al. A multi-omics interpretable machine learning model reveals modes of action of small molecules. Sci. Rep. 10, 954 (2020).","journal-title":"Sci. Rep."},{"key":"86_CR47","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1038\/s41588-018-0092-1","volume":"50","author":"A Gusev","year":"2018","unstructured":"Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538\u2013548 (2018).","journal-title":"Nat. Genet."},{"key":"86_CR48","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1038\/s41598-020-80941-7","volume":"11","author":"T Rubio","year":"2021","unstructured":"Rubio, T. et al. Multi-omic analysis unveils biological pathways in peripheral immune system associated to minimal hepatic encephalopathy appearance in cirrhotic patients. Sci. Rep. 11, 1907 (2021).","journal-title":"Sci. Rep."},{"key":"86_CR49","doi-asserted-by":"publisher","first-page":"e1003068","DOI":"10.1371\/journal.pcbi.1003068","volume":"9","author":"X Cai","year":"2013","unstructured":"Cai, X., Bazerque, J. A. & Giannakis, G. B. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations. PLoS Comput. Biol. 9, e1003068 (2013).","journal-title":"PLoS Comput. Biol."},{"key":"86_CR50","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-1-59745-525-1_3","volume":"500","author":"MA Oberhardt","year":"2009","unstructured":"Oberhardt, M. A., Chavali, A. K. & Papin, J. A. Flux balance analysis: interrogating genome-scale metabolic networks. Methods Mol. Biol. 500, 61\u201380 (2009).","journal-title":"Methods Mol. Biol."},{"key":"86_CR51","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","volume":"28","author":"JD Orth","year":"2010","unstructured":"Orth, J. D., Thiele, I. & Palsson, B. \u00d8. What is flux balance analysis? Nat. Biotechnol. 28, 245\u2013248 (2010).","journal-title":"Nat. Biotechnol."},{"key":"86_CR52","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1006\/jtbi.2001.2405","volume":"213","author":"MW Covert","year":"2001","unstructured":"Covert, M. W., Schilling, C. H. & Palsson, B. Regulation of gene expression in flux balance models of metabolism. J. Theor. Biol. 213, 73\u201388 (2001).","journal-title":"J. Theor. Biol."},{"key":"86_CR53","doi-asserted-by":"publisher","first-page":"361","DOI":"10.3389\/fgene.2018.00361","volume":"9","author":"E Tzika","year":"2018","unstructured":"Tzika, E., Dreker, T. & Imhof, A. Epigenetics and metabolism in health and disease. Front. Genet. 9, 361 (2018).","journal-title":"Front. Genet."},{"key":"86_CR54","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s12859-021-04016-8","volume":"22","author":"JC Siebert","year":"2021","unstructured":"Siebert, J. C. et al. CANTARE: finding and visualizing network-based multi-omic predictive models. BMC Bioinformatics 22, 80 (2021).","journal-title":"BMC Bioinformatics"},{"key":"86_CR55","doi-asserted-by":"publisher","first-page":"3092","DOI":"10.1038\/s41467-020-16937-8","volume":"11","author":"S Tarazona","year":"2020","unstructured":"Tarazona, S. et al. Harmonization of quality metrics and power calculation in multi-omic studies. Nat. Commun. 11, 3092 (2020).","journal-title":"Nat. Commun."},{"key":"86_CR56","doi-asserted-by":"publisher","unstructured":"Soerensen, M. et al. A genome-wide integrative association study of DNA methylation and gene expression data and later life cognitive functioning in monozygotic twins. Front. Neurosci. 14, https:\/\/doi.org\/10.3389\/fnins.2020.00233 (2020).","DOI":"10.3389\/fnins.2020.00233"},{"key":"86_CR57","doi-asserted-by":"publisher","unstructured":"Dai, Y., Pei, G., Zhao, Z. & Jia, P. A convergent study of genetic variants associated with Crohn\u2019s disease: evidence from GWAS, gene expression, methylation, eQTL and TWAS. Front. Genet. 10, https:\/\/doi.org\/10.3389\/fgene.2019.00318 (2019).","DOI":"10.3389\/fgene.2019.00318"},{"key":"86_CR58","doi-asserted-by":"publisher","first-page":"e0165545","DOI":"10.1371\/journal.pone.0165545","volume":"11","author":"N Karathanasis","year":"2016","unstructured":"Karathanasis, N., Tsamardinos, I. & Lagani, V. omicsNPC: applying the non-parametric combination methodology to the integrative analysis of heterogeneous omics data. PLoS ONE 11, e0165545 (2016).","journal-title":"PLoS ONE"},{"key":"86_CR59","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1093\/bioinformatics\/btq594","volume":"27","author":"F Garcia-Alcalde","year":"2011","unstructured":"Garcia-Alcalde, F., Garcia-Lopez, F., Dopazo, J. & Conesa, A. Paintomics: a web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics 27, 137\u2013139 (2011).","journal-title":"Bioinformatics"},{"key":"86_CR60","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1186\/s12859-016-1273-5","volume":"17","author":"V Voillet","year":"2016","unstructured":"Voillet, V., Besse, P., Liaubet, L., San Cristobal, M. & Gonz\u00e1lez, I. Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework. BMC Bioinformatics 17, 402 (2016).","journal-title":"BMC Bioinformatics"},{"key":"86_CR61","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-017-3691-9","volume":"18","author":"RI Kuo","year":"2017","unstructured":"Kuo, R. I. et al. Normalized long read RNA sequencing in chicken reveals transcriptome complexity similar to human. BMC Genomics 18, 323 (2017).","journal-title":"BMC Genomics"},{"key":"86_CR62","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1038\/s41597-019-0258-4","volume":"6","author":"A Conesa","year":"2019","unstructured":"Conesa, A. & Beck, S. Making multi-omics data accessible to researchers. Sci. Data 6, 251 (2019).","journal-title":"Sci. Data"},{"key":"86_CR63","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1093\/bioinformatics\/bty796","volume":"35","author":"X Dong","year":"2019","unstructured":"Dong, X. et al. TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach. Bioinformatics 35, 1278\u20131283 (2019).","journal-title":"Bioinformatics"},{"key":"86_CR64","doi-asserted-by":"publisher","unstructured":"Zhou, X., Chai, H., Zhao, H., Luo, C.-H. & Yang, Y. Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning\u2013based neural network. GigaScience 9, https:\/\/doi.org\/10.1093\/gigascience\/giaa076 (2020).","DOI":"10.1093\/gigascience\/giaa076"},{"key":"86_CR65","doi-asserted-by":"publisher","first-page":"2851","DOI":"10.1177\/0962280220907365","volume":"29","author":"M Ugidos","year":"2020","unstructured":"Ugidos, M., Tarazona, S., Prats-Montalb\u00e1n, J. M., Ferrer, A. & Conesa, A. MultiBaC: a strategy to remove batch effects between different omic data types. Stat. Methods Med. Res. 29, 2851\u20132864 (2020).","journal-title":"Stat. Methods Med. Res."},{"key":"86_CR66","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.cct.2006.07.003","volume":"27","author":"K Messer","year":"2006","unstructured":"Messer, K., Vaida, F. & Hogan, C. Robust analysis of biomarker data with informative missingness using a two-stage hypothesis test in an HIV treatment interruption trial: AIEDRP AIN503\/ACTG A5217. Contemp. Clin. Trials 27, 506\u2013517 (2006).","journal-title":"Contemp. Clin. Trials"},{"key":"86_CR67","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s00439-009-0676-z","volume":"126","author":"M-G Hong","year":"2009","unstructured":"Hong, M.-G., Pawitan, Y., Magnusson, P. K. E. & Prince, J. A. Strategies and issues in the detection of pathway enrichment in genome-wide association studies. Hum. Genet. 126, 289\u2013301 (2009).","journal-title":"Hum. Genet."},{"key":"86_CR68","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545\u201315550 (2005).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"86_CR69","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-016-3057-8","volume":"17","author":"D Arneson","year":"2016","unstructured":"Arneson, D., Bhattacharya, A., Shu, L., M\u00e4kinen, V.-P. & Yang, X. Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration. BMC Genomics 17, 722 (2016).","journal-title":"BMC Genomics"},{"key":"86_CR70","doi-asserted-by":"publisher","first-page":"e105","DOI":"10.1093\/nar\/gku463","volume":"42","author":"RP Welch","year":"2014","unstructured":"Welch, R. P. et al. ChIP-enrich: gene set enrichment testing for ChIP-seq data. Nucleic Acids Res. 42, e105 (2014).","journal-title":"Nucleic Acids Res."},{"key":"86_CR71","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1186\/s12859-020-03910-x","volume":"21","author":"S Canzler","year":"2020","unstructured":"Canzler, S. & Hackerm\u00fcller, J. multiGSEA: a GSEA-based pathway enrichment analysis for multi-omics data. BMC Bioinformatics 21, 561 (2020).","journal-title":"BMC Bioinformatics"},{"key":"86_CR72","doi-asserted-by":"publisher","unstructured":"Long, Y., Lu, M., Cheng, T., Zhan, X. & Zhan, X. Multiomics-based signaling pathway network alterations in human non-functional pituitary adenomas. Front. Endocrinol. 10, https:\/\/doi.org\/10.3389\/fendo.2019.00835 (2019).","DOI":"10.3389\/fendo.2019.00835"},{"key":"86_CR73","doi-asserted-by":"publisher","first-page":"W503","DOI":"10.1093\/nar\/gky466","volume":"46","author":"R Hern\u00e1ndez-de-Diego","year":"2018","unstructured":"Hern\u00e1ndez-de-Diego, R. et al. PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data. Nucleic Acids Res. 46, W503\u2013W509 (2018).","journal-title":"Nucleic Acids Res."},{"key":"86_CR74","doi-asserted-by":"publisher","first-page":"D677","DOI":"10.1093\/nar\/gkq989","volume":"39","author":"N Sakurai","year":"2011","unstructured":"Sakurai, N. et al. KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data. Nucleic Acids Res. 39, D677\u2013D684 (2011).","journal-title":"Nucleic Acids Res."},{"key":"86_CR75","doi-asserted-by":"publisher","first-page":"8.13.11","DOI":"10.1002\/0471250953.bi0813s47","volume":"47","author":"G Su","year":"2014","unstructured":"Su, G., Morris, J. H., Demchak, B. & Bader, G. D. Biological network exploration with Cytoscape 3. Curr. Protoc. Bioinformatics 47, 8.13.11\u201318.13.24 (2014).","journal-title":"Curr. Protoc. Bioinformatics"},{"key":"86_CR76","doi-asserted-by":"publisher","unstructured":"Kuo, T. C., Tian, T. F. & Tseng, Y. J. 3Omics: a web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Syst. Biol. 7, https:\/\/doi.org\/10.1186\/1752-0509-7-64 (2013).","DOI":"10.1186\/1752-0509-7-64"},{"key":"86_CR77","unstructured":"Miller, J. J. Graph database applications and concepts with Neo4j. In Proc. Southern Association for Information Systems Conference (AIS, 2013)."},{"key":"86_CR78","doi-asserted-by":"publisher","first-page":"19","DOI":"10.5808\/GI.2017.15.1.19","volume":"15","author":"B-H Yoon","year":"2017","unstructured":"Yoon, B.-H., Kim, S.-K. & Kim, S.-Y. Use of graph database for the integration of heterogeneous biological data. Genomics Inform. 15, 19\u201327 (2017).","journal-title":"Genomics Inform."},{"key":"86_CR79","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.chom.2014.08.014","volume":"16","author":"TIHiRN Consortium","year":"2014","unstructured":"Consortium, T. I. Hi. R. N. The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276\u2013289 (2014).","journal-title":"Cell Host Microbe"},{"key":"86_CR80","unstructured":"ICGC Data Portal (The International Cancer Genome Consortium, 2021); https:\/\/dcc.icgc.org\/"},{"key":"86_CR81","unstructured":"Human Microbiome Project Data Portal (Human Microbiome Project, 2021); https:\/\/portal.hmpdacc.org\/"},{"key":"86_CR82","doi-asserted-by":"publisher","first-page":"160018","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).","journal-title":"Sci. Data"},{"key":"86_CR83","doi-asserted-by":"publisher","first-page":"D54","DOI":"10.1093\/nar\/gkr854","volume":"40","author":"Y Kodama","year":"2012","unstructured":"Kodama, Y., Shumway, M. & Leinonen, R. The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res. 40, D54\u2013D56 (2012).","journal-title":"Nucleic Acids Res."},{"key":"86_CR84","doi-asserted-by":"publisher","first-page":"D975","DOI":"10.1093\/nar\/gkt1211","volume":"42","author":"KA Tryka","year":"2014","unstructured":"Tryka, K. A. et al. NCBI\u2019s Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Res. 42, D975\u2013D979 (2014).","journal-title":"Nucleic Acids Res."},{"key":"86_CR85","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1038\/ng.3312","volume":"47","author":"I Lappalainen","year":"2015","unstructured":"Lappalainen, I. et al. The European Genome-phenome Archive of human data consented for biomedical research. Nat. Genet. 47, 692\u2013695 (2015).","journal-title":"Nat. Genet."},{"key":"86_CR86","first-page":"D440","volume":"48","author":"K Haug","year":"2020","unstructured":"Haug, K. et al. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 48, D440\u2013D444 (2020).","journal-title":"Nucleic Acids Res."},{"key":"86_CR87","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkw936","volume":"45","author":"EW Deutsch","year":"2017","unstructured":"Deutsch, E. W. et al. The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition. Nucleic Acids Res. 45, D1100\u2013D1106 (2017).","journal-title":"Nucleic Acids Res."},{"key":"86_CR88","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1038\/s41576-020-0257-5","volume":"21","author":"JB Byrd","year":"2020","unstructured":"Byrd, J. B., Greene, A. C., Prasad, D. V., Jiang, X. & Greene, C. S. Responsible, practical genomic data sharing that accelerates research. Nat. Rev. Genet. 21, 615\u2013629 (2020).","journal-title":"Nat. Rev. Genet."},{"key":"86_CR89","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-8-S2-S9","volume":"8","author":"R Hernandez-de-Diego","year":"2014","unstructured":"Hernandez-de-Diego, R. et al. STATegra EMS: an experiment management system for complex next-generation omics experiments. BMC Syst. Biol. 8, S9 (2014).","journal-title":"BMC Syst. Biol."},{"key":"86_CR90","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1515\/jib-2011-160","volume":"8","author":"K Lin","year":"2011","unstructured":"Lin, K. et al. MADMAX\u2014management and analysis database for multiple ~omics experiments. J. Integr. Bioinform. 8, 59\u201374 (2011).","journal-title":"J. Integr. Bioinform."},{"key":"86_CR91","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1186\/1471-2105-15-S14-S3","volume":"15","author":"F Venco","year":"2014","unstructured":"Venco, F., Vaskin, Y., Ceol, A. & Muller, H. SMITH: a LIMS for handling next-generation sequencing workflows. BMC Bioinformatics 15, S3 (2014).","journal-title":"BMC Bioinformatics"},{"key":"86_CR92","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1038\/nbt.3790","volume":"35","author":"Y Perez-Riverol","year":"2017","unstructured":"Perez-Riverol, Y. et al. Discovering and linking public omics data sets using the Omics Discovery index. Nat. Biotechnol. 35, 406\u2013409 (2017).","journal-title":"Nat. Biotechnol."},{"key":"86_CR93","doi-asserted-by":"crossref","unstructured":"Chervitz, S. A. et al. in Bioinformatics for Omics Data: Methods and Protocols (ed. Mayer, B.) 31\u201369 (Humana Press, 2011).","DOI":"10.1007\/978-1-61779-027-0_2"},{"key":"86_CR94","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1038\/nrg.2018.4","volume":"19","author":"KJ Karczewski","year":"2018","unstructured":"Karczewski, K. J. & Snyder, M. P. Integrative omics for health and disease. Nat. Rev. Genet. 19, 299\u2013310 (2018).","journal-title":"Nat. Rev. Genet."},{"key":"86_CR95","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/s10545-017-0128-1","volume":"41","author":"CDM van Karnebeek","year":"2018","unstructured":"van Karnebeek, C. D. M. et al. The role of the clinician in the multi-omics era: are you ready? J. Inherit. Metab. Dis. 41, 571\u2013582 (2018).","journal-title":"J. Inherit. Metab. Dis."},{"key":"86_CR96","doi-asserted-by":"publisher","first-page":"8304260","DOI":"10.1155\/2019\/8304260","volume":"2019","author":"C Angione","year":"2019","unstructured":"Angione, C. Human systems biology and metabolic modelling: a review\u2014from disease metabolism to precision medicine. Biomed. Res. Int. 2019, 8304260 (2019).","journal-title":"Biomed. Res. Int."},{"key":"86_CR97","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1146\/annurev-biodatasci-080917-013328","volume":"2","author":"J-K H\u00e9rich\u00e9","year":"2019","unstructured":"H\u00e9rich\u00e9, J.-K., Alexander, S. & Ellenberg, J. Integrating imaging and omics: computational methods and challenges. Annu. Rev. Biomed. Data Sci. 2, 175\u2013197 (2019).","journal-title":"Annu. Rev. Biomed. Data Sci."},{"key":"86_CR98","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1038\/s12276-020-0420-2","volume":"52","author":"J Lee","year":"2020","unstructured":"Lee, J., Hyeon, D. Y. & Hwang, D. Single-cell multiomics: technologies and data analysis methods. Exp. Mol. Med. 52, 1428\u20131442 (2020).","journal-title":"Exp. Mol. Med."},{"key":"86_CR99","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1186\/gb-2010-11-5-207","volume":"11","author":"LD Stein","year":"2010","unstructured":"Stein, L. D. The case for cloud computing in genome informatics. Genome Biol. 11, 207 (2010).","journal-title":"Genome Biol."},{"key":"86_CR100","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1093\/bib\/bbaa032","volume":"22","author":"M Oh","year":"2020","unstructured":"Oh, M., Park, S., Kim, S. & Chae, H. Machine learning-based analysis of multi-omics data on the cloud for investigating gene regulations. Brief. Bioinform. 22, 66\u201376 (2020).","journal-title":"Brief. Bioinform."},{"key":"86_CR101","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2897188","volume":"3","author":"E Solomonik","year":"2016","unstructured":"Solomonik, E., Carson, E., Knight, N. & Demmel, J. Trade-offs between synchronization, communication, and computation in parallel linear algebra computations. ACM Trans. Parallel Comput. 3, 1\u201347 (2016).","journal-title":"ACM Trans. Parallel Comput."},{"key":"86_CR102","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1038\/nrg3433","volume":"14","author":"B Berger","year":"2013","unstructured":"Berger, B., Peng, J. & Singh, M. Computational solutions for omics data. Nat. Rev. Genet. 14, 333\u2013346 (2013).","journal-title":"Nat. Rev. Genet."},{"key":"86_CR103","first-page":"33","volume":"8","author":"A Alyass","year":"2015","unstructured":"Alyass, A., Turcotte, M. & Meyre, D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med. Genet. 8, 33 (2015).","journal-title":"BMC Med. Genet."},{"key":"86_CR104","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","volume":"2","author":"X-W Chen","year":"2014","unstructured":"Chen, X.-W. & Lin, X. Big data deep learning: challenges and perspectives. IEEE Access 2, 514\u2013525 (2014).","journal-title":"IEEE Access"},{"key":"86_CR105","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1038\/s12276-020-0422-0","volume":"52","author":"J Fan","year":"2020","unstructured":"Fan, J., Slowikowski, K. & Zhang, F. Single-cell transcriptomics in cancer: computational challenges and opportunities. Exp. Mol. Med. 52, 1452\u20131465 (2020).","journal-title":"Exp. Mol. Med."},{"key":"86_CR106","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neuron.2020.12.010","volume":"109","author":"EJ Armand","year":"2021","unstructured":"Armand, E. J., Li, J., Xie, F., Luo, C. & Mukamel, E. A. Single-cell sequencing of brain cell transcriptomes and epigenomes. Neuron 109, 11\u201326 (2021).","journal-title":"Neuron"},{"key":"86_CR107","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1038\/s41592-019-0691-5","volume":"17","author":"C Zhu","year":"2020","unstructured":"Zhu, C., Preissl, S. & Ren, B. Single-cell multimodal omics: the power of many. Nat. Methods 17, 11\u201314 (2020).","journal-title":"Nat. Methods"},{"key":"86_CR108","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1093\/bib\/bbaa042","volume":"22","author":"M Forcato","year":"2021","unstructured":"Forcato, M., Romano, O. & Bicciato, S. Computational methods for the integrative analysis of single-cell data. Brief. Bioinform. 22, 20\u201329 (2021).","journal-title":"Brief. Bioinform."},{"key":"86_CR109","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1016\/j.cell.2016.11.048","volume":"167","author":"B Adamson","year":"2016","unstructured":"Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867\u20131882.e1821 (2016).","journal-title":"Cell"},{"key":"86_CR110","doi-asserted-by":"publisher","first-page":"eaat5691","DOI":"10.1126\/science.aat5691","volume":"361","author":"X Wang","year":"2018","unstructured":"Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).","journal-title":"Science"},{"key":"86_CR111","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1038\/nbt.4103","volume":"36","author":"B Raj","year":"2018","unstructured":"Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442\u2013450 (2018).","journal-title":"Nat. Biotechnol."},{"key":"86_CR112","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1038\/nbt.2859","volume":"32","author":"C Trapnell","year":"2014","unstructured":"Trapnell, C. & Cacchiarelli, D. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381\u2013386 (2014).","journal-title":"Nat. Biotechnol."},{"key":"86_CR113","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1038\/nmeth.4380","volume":"14","author":"M Stoeckius","year":"2017","unstructured":"Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865\u2013868 (2017).","journal-title":"Nat. Methods"},{"key":"86_CR114","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.celrep.2015.12.021","volume":"14","author":"S Darmanis","year":"2016","unstructured":"Darmanis, S. et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 14, 380\u2013389 (2016).","journal-title":"Cell Rep."},{"key":"86_CR115","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1038\/nbt.3973","volume":"35","author":"VM Peterson","year":"2017","unstructured":"Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936\u2013939 (2017).","journal-title":"Nat. Biotechnol."},{"key":"86_CR116","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1186\/s13059-019-1854-5","volume":"20","author":"H Chen","year":"2019","unstructured":"Chen, H. et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data. Genome Biol. 20, 241 (2019).","journal-title":"Genome Biol."},{"key":"86_CR117","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","volume":"177","author":"T Stuart","year":"2019","unstructured":"Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888\u20131902 (2019).","journal-title":"Cell"},{"key":"86_CR118","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1186\/s13059-017-1269-0","volume":"18","author":"JD Welch","year":"2017","unstructured":"Welch, J. D., Hartemink, A. J. & Prins, J. F. MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics. Genome Biol. 18, 138 (2017).","journal-title":"Genome Biol."},{"key":"86_CR119","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1186\/s13059-019-1645-z","volume":"20","author":"KR Campbell","year":"2019","unstructured":"Campbell, K. R. et al. clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers. Genome Biol. 20, 54 (2019).","journal-title":"Genome Biol."},{"key":"86_CR120","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1101\/gr.228080.117","volume":"28","author":"J Fan","year":"2018","unstructured":"Fan, J. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217\u20131227 (2018).","journal-title":"Genome Res."},{"key":"86_CR121","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41588-018-0089-9","volume":"50","author":"MGP Van Der Wijst","year":"2018","unstructured":"Van Der Wijst, M. G. P. et al. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. Nat. Genet. 50, 493\u2013497 (2018).","journal-title":"Nat. Genet."},{"key":"86_CR122","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1038\/s41586-018-0414-6","volume":"560","author":"G La Manno","year":"2018","unstructured":"La Manno, G. et al. RNA velocity of single cells. Nature 560, 494\u2013498 (2018).","journal-title":"Nature"},{"key":"86_CR123","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1038\/nprot.2016.105","volume":"11","author":"M-A Bray","year":"2016","unstructured":"Bray, M.-A. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757\u20131774 (2016).","journal-title":"Nat. Protoc."},{"key":"86_CR124","doi-asserted-by":"publisher","first-page":"i62","DOI":"10.1093\/bioinformatics\/btt229","volume":"29","author":"AJ Sedgewick","year":"2013","unstructured":"Sedgewick, A. J., Benz, S. C., Rabizadeh, S., Soon-Shiong, P. & Vaske, C. J. Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM. Bioinformatics 29, i62\u2013i70 (2013).","journal-title":"Bioinformatics"},{"key":"86_CR125","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0202-7","volume":"6","author":"D Gomez-Cabrero","year":"2019","unstructured":"Gomez-Cabrero, D. et al. STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse. Sci. Data 6, 256 (2019).","journal-title":"Sci. Data"},{"key":"86_CR126","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.cell.2018.03.042","volume":"173","author":"C Hutter","year":"2018","unstructured":"Hutter, C. & Zenklusen, J. C. The Cancer Genome Atlas: creating lasting value beyond its data. Cell 173, 283\u2013285 (2018).","journal-title":"Cell"},{"key":"86_CR127","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/s41586-019-1186-3","volume":"569","author":"M Ghandi","year":"2019","unstructured":"Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503\u2013508 (2019).","journal-title":"Nature"},{"key":"86_CR128","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1038\/ng.2653","volume":"45","author":"J Lonsdale","year":"2013","unstructured":"Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580\u2013585 (2013).","journal-title":"Nat. Genet."},{"key":"86_CR129","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1038\/s41586-021-03205-y","volume":"590","author":"D Taliun","year":"2021","unstructured":"Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290\u2013299 (2021).","journal-title":"Nature"},{"key":"86_CR130","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1038\/s41586-020-2493-4","volume":"583","author":"JE Moore","year":"2020","unstructured":"Moore, J. E. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699\u2013710 (2020).","journal-title":"Nature"},{"key":"86_CR131","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1038\/s41586-019-0965-1","volume":"568","author":"A Almeida","year":"2019","unstructured":"Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499\u2013504 (2019).","journal-title":"Nature"},{"key":"86_CR132","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1038\/s41586-020-2094-2","volume":"579","author":"J Mergner","year":"2020","unstructured":"Mergner, J. et al. Mass-spectrometry-based draft of the Arabidopsis proteome. Nature 579, 409\u2013414 (2020).","journal-title":"Nature"},{"key":"86_CR133","doi-asserted-by":"publisher","first-page":"4411","DOI":"10.1093\/bioinformatics\/bti714","volume":"21","author":"TR O\u2019Connor","year":"2005","unstructured":"O\u2019Connor, T. R., Dyreson, C. & Wyrick, J. J. Athena: a resource for rapid visualization and systematic analysis of Arabidopsis promoter sequences. Bioinformatics 21, 4411\u20134413 (2005).","journal-title":"Bioinformatics"}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-021-00086-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-021-00086-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-021-00086-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T02:09:45Z","timestamp":1675476585000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-021-00086-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":133,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["86"],"URL":"https:\/\/doi.org\/10.1038\/s43588-021-00086-z","relation":{},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,21]]},"assertion":[{"value":"18 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}