{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:11:05Z","timestamp":1772165465347,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Ministry of Education, Culture, Sport, Science and Technology of Japan for the RIKEN Center for Integrative Medical Sciences"},{"name":"RIKEN IMS Internship Program"},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2019\/35\/B\/NZ2\/02548"],"award-info":[{"award-number":["2019\/35\/B\/NZ2\/02548"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Elucidating the Transcription Factors (TFs) that drive the gene expression changes in a given experiment is a common question asked by researchers. The existing methods rely on the predicted Transcription Factor Binding Site (TFBS) to model the changes in the motif activity. Such methods only work for TFs that have a motif and assume the TF binding profile is the same in all cell types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Given the wealth of the ChIP-seq data available for a wide range of the TFs in various cell types, we propose that gene expression modeling can be done using ChIP-seq \u201csignatures\u201d directly, effectively skipping the motif finding and TFBS prediction steps. We present\n                      <jats:italic>xcore<\/jats:italic>\n                      , an R package that allows TF activity modeling based on ChIP-seq signatures and the user's gene expression data. We also provide\n                      <jats:italic>xcoredata<\/jats:italic>\n                      a companion data package that provides a collection of preprocessed ChIP-seq signatures. We demonstrate that\n                      <jats:italic>xcore<\/jats:italic>\n                      leads to biologically relevant predictions using transforming growth factor beta induced epithelial-mesenchymal transition time-courses, rinderpest infection time-courses, and embryonic stem cells differentiated to cardiomyocytes time-course profiled with Cap Analysis Gene Expression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      <jats:italic>xcore<\/jats:italic>\n                      provides a simple analytical framework for gene expression modeling using linear models that can be easily incorporated into differential expression analysis pipelines. Taking advantage of public ChIP-seq databases,\n                      <jats:italic>xcore<\/jats:italic>\n                      can identify meaningful molecular signatures and relevant ChIP-seq experiments.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-022-05084-0","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T04:05:55Z","timestamp":1673409955000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["xcore: an R package for inference of gene expression regulators"],"prefix":"10.1186","volume":"24","author":[{"given":"Maciej","family":"Migda\u0142","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takahiro","family":"Arakawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satoshi","family":"Takizawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masaaki","family":"Furuno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harukazu","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik","family":"Arner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7718-5248","authenticated-orcid":false,"given":"Cecilia Lanny","family":"Winata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bogumi\u0142","family":"Kaczkowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"5084_CR1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.169508.113","author":"PJ Balwierz","year":"2014","unstructured":"Balwierz PJ, Pachkov M, Arnold P, Gruber AJ, Zavolan M, van Nimwegen E. ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs. Genome Res. 2014. https:\/\/doi.org\/10.1101\/gr.169508.113.","journal-title":"Genome Res"},{"key":"5084_CR2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1093\/nar\/gkw1061","volume":"45","author":"F Schmidt","year":"2017","unstructured":"Schmidt F, Gasparoni N, Gasparoni G, Gianmoena K, Cadenas C, Polansky JK, et al. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction. Nucleic Acids Res. 2017;45:54\u201366.","journal-title":"Nucleic Acids Res"},{"key":"5084_CR3","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1101\/gr.227231.117","volume":"28","author":"JGS Madsen","year":"2018","unstructured":"Madsen JGS, Rauch A, Van Hauwaert EL, Schmidt SF, Winnefeld M, Mandrup S. Integrated analysis of motif activity and gene expression changes of transcription factors. Genome Res. 2018;28:243\u201355.","journal-title":"Genome Res"},{"key":"5084_CR4","doi-asserted-by":"crossref","unstructured":"FANTOM Consortium, Suzuki H, Forrest ARR, van Nimwegen E, Daub CO, Balwierz PJ, et al. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat Genet. 2009;41:553\u201362.","DOI":"10.1038\/ng.375"},{"key":"5084_CR5","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1101\/gr.135129.111","volume":"22","author":"A Natarajan","year":"2012","unstructured":"Natarajan A, Yard\u0131mc\u0131 GG, Sheffield NC, Crawford GE, Ohler U. Predicting cell-type\u2013specific gene expression from regions of open chromatin. Genome Res. 2012;22:1711\u201322.","journal-title":"Genome Res"},{"key":"5084_CR6","first-page":"D180","volume":"48","author":"J Ch\u00e8neby","year":"2020","unstructured":"Ch\u00e8neby J, M\u00e9n\u00e9trier Z, Mestdagh M, Rosnet T, Douida A, Rhalloussi W, et al. ReMap 2020: a database of regulatory regions from an integrative analysis of Human and Arabidopsis DNA-binding sequencing experiments. Nucleic Acids Res. 2020;48:D180\u20138.","journal-title":"Nucleic Acids Res"},{"key":"5084_CR7","doi-asserted-by":"publisher","first-page":"e46255","DOI":"10.15252\/embr.201846255","volume":"19","author":"S Oki","year":"2018","unstructured":"Oki S, Ohta T, Shioi G, Hatanaka H, Ogasawara O, Okuda Y, et al. ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep. 2018;19:e46255.","journal-title":"EMBO Rep"},{"key":"5084_CR8","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s13059-020-1934-6","volume":"21","author":"Q Qin","year":"2020","unstructured":"Qin Q, Fan J, Zheng R, Wan C, Mei S, Wu Q, et al. Lisa: inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data. Genome Biol. 2020;21:32.","journal-title":"Genome Biol"},{"key":"5084_CR9","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1186\/s13059-022-02690-2","volume":"23","author":"M Karimzadeh","year":"2022","unstructured":"Karimzadeh M, Hoffman MM. Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome. Genome Biol. 2022;23:126.","journal-title":"Genome Biol"},{"key":"5084_CR10","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","volume":"26","author":"MD Robinson","year":"2010","unstructured":"Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139\u201340.","journal-title":"Bioinformatics"},{"key":"5084_CR11","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.","journal-title":"Genome Biol"},{"key":"5084_CR12","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1186\/s13059-014-0560-6","volume":"16","author":"M Lizio","year":"2015","unstructured":"Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, et al. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol. 2015;16:22.","journal-title":"Genome Biol"},{"key":"5084_CR13","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","volume":"12","author":"AE Hoerl","year":"1970","unstructured":"Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12:55\u201367.","journal-title":"Technometrics"},{"key":"5084_CR14","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1186\/1471-2105-12-372","volume":"12","author":"E Cule","year":"2011","unstructured":"Cule E, Vineis P, De Iorio M. Significance testing in ridge regression for genetic data. BMC Bioinform. 2011;12:372.","journal-title":"BMC Bioinform"},{"key":"5084_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"JH Friedman","year":"2010","unstructured":"Friedman JH, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1\u201322.","journal-title":"J Stat Softw"},{"key":"5084_CR16","doi-asserted-by":"publisher","first-page":"1986","DOI":"10.2337\/db11-1508","volume":"61","author":"E Arner","year":"2012","unstructured":"Arner E, Mejhert N, Kulyt\u00e9 A, Balwierz PJ, Pachkov M, Cormont M, et al. Adipose tissue microRNAs as regulators of CCL2 production in human obesity. Diabetes. 2012;61:1986\u201393.","journal-title":"Diabetes"},{"key":"5084_CR17","volume-title":"The American soldier: adjustment during army life. (Studies in social psychology in World War II)","author":"SA Stouffer","year":"1949","unstructured":"Stouffer SA, Suchman EA, Devinney LC, Star SA, Williams RM Jr. The American soldier: adjustment during army life. (Studies in social psychology in World War II), vol. 1. Oxford: Princeton University Press; 1949."},{"key":"5084_CR18","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1093\/bioinformatics\/btp683","volume":"26","author":"MA Sartor","year":"2010","unstructured":"Sartor MA, Mahavisno V, Keshamouni VG, Cavalcoli J, Wright Z, Karnovsky A, et al. ConceptGen: a gene set enrichment and gene set relation mapping tool. Bioinformatics. 2010;26:456\u201363.","journal-title":"Bioinformatics"},{"key":"5084_CR19","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/cr.2009.5","volume":"19","author":"J Xu","year":"2009","unstructured":"Xu J, Lamouille S, Derynck R. TGF-\u03b2-induced epithelial to mesenchymal transition. Cell Res. 2009;19:156\u201372.","journal-title":"Cell Res"},{"key":"5084_CR20","doi-asserted-by":"publisher","first-page":"554","DOI":"10.3389\/fonc.2020.00554","volume":"10","author":"DP Lavin","year":"2020","unstructured":"Lavin DP, Tiwari VK. Unresolved complexity in the gene regulatory network underlying EMT. Front Oncol. 2020;10:554.","journal-title":"Front Oncol"},{"key":"5084_CR21","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1016\/j.celrep.2014.05.010","volume":"7","author":"E Dardenne","year":"2014","unstructured":"Dardenne E, Polay Espinoza M, Fattet L, Germann S, Lambert M-P, Neil H, et al. RNA helicases DDX5 and DDX17 dynamically orchestrate transcription, miRNA, and splicing programs in cell differentiation. Cell Rep. 2014;7:1900\u201313.","journal-title":"Cell Rep"},{"key":"5084_CR22","doi-asserted-by":"publisher","first-page":"16528","DOI":"10.1074\/jbc.RA118.003662","volume":"293","author":"B Tian","year":"2018","unstructured":"Tian B, Widen SG, Yang J, Wood TG, Kudlicki A, Zhao Y, et al. The NF\u03baB subunit RELA is a master transcriptional regulator of the committed epithelial-mesenchymal transition in airway epithelial cells. J Biol Chem. 2018;293:16528\u201345.","journal-title":"J Biol Chem"},{"issue":"Database issue","key":"5084_CR23","doi-asserted-by":"publisher","first-page":"D355","DOI":"10.1093\/nar\/gkp896","volume":"38","author":"M Kanehisa","year":"2010","unstructured":"Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38(Database issue):D355-360.","journal-title":"Nucleic Acids Res"},{"key":"5084_CR24","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1093\/bioinformatics\/btn615","volume":"25","author":"S Carbon","year":"2009","unstructured":"Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S. AmiGO: online access to ontology and annotation data. Bioinformatics. 2009;25:288\u20139.","journal-title":"Bioinformatics"},{"key":"5084_CR25","doi-asserted-by":"publisher","first-page":"D729","DOI":"10.1093\/nar\/gky1094","volume":"47","author":"R Zheng","year":"2019","unstructured":"Zheng R, Wan C, Mei S, Qin Q, Wu Q, Sun H, et al. Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis. Nucleic Acids Res. 2019;47:D729\u201335.","journal-title":"Nucleic Acids Res"},{"key":"5084_CR26","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1111\/j.1349-7006.2006.00357.x","volume":"98","author":"S Ehata","year":"2007","unstructured":"Ehata S, Hanyu A, Fujime M, Katsuno Y, Fukunaga E, Goto K, et al. Ki26894, a novel transforming growth factor-\u03b2 type I receptor kinase inhibitor, inhibits in vitro invasion and in vivo bone metastasis of a human breast cancer cell line. Cancer Sci. 2007;98:127\u201333.","journal-title":"Cancer Sci"},{"key":"5084_CR27","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/978-1-4939-0805-9_7","volume-title":"Transcription factor regulatory networks: methods and protocols","author":"M Murata","year":"2014","unstructured":"Murata M, Nishiyori-Sueki H, Kojima-Ishiyama M, Carninci P, Hayashizaki Y, Itoh M. Detecting expressed genes using CAGE. In: Miyamoto-Sato E, Ohashi H, Sasaki H, Nishikawa J, Yanagawa H, editors. Transcription factor regulatory networks: methods and protocols. New York: Springer; 2014. p. 67\u201385."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-05084-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-05084-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-05084-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T11:42:02Z","timestamp":1679312522000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-05084-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,11]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5084"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-05084-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.02.23.481130","asserted-by":"object"}]},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,11]]},"assertion":[{"value":"3 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests. EA took a position with GSK during the submission of this manuscript. GSK was not involved at any stage of the work presented here and there is no conflict of interest related to GSK for this work.\u00a0BK took a position with AstraZeneca R&D during the submission of this manuscript. AstraZeneca R&D was not involved at any stage of the work presented here and there is no conflict of interest related to AstraZeneca R&D for this work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14"}}