{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T00:20:43Z","timestamp":1722126043958},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"S16","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p><jats:bold>HTRgene<\/jats:bold>is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify \u201cresponse order preserving DEGs\u201d that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-019-3072-2","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T12:00:35Z","timestamp":1575288035000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis"],"prefix":"10.1186","volume":"20","author":[{"given":"Hongryul","family":"Ahn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inuk","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heejoon","family":"Chae","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongwon","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Woosuk","family":"Jung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,2]]},"reference":[{"key":"3072_CR1","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/S0076-6879(99)03014-1","volume":"303","author":"M Eisen","year":"1999","unstructured":"Eisen M, Brown P. DNA arrays for analysis of gene expression. Methods Enzymol. 1999; 303:179.","journal-title":"Methods Enzymol"},{"issue":"1","key":"3072_CR2","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/nrg2484","volume":"10","author":"Z Wang","year":"2009","unstructured":"Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Rev Genet. 2009; 10(1):57\u201363.","journal-title":"Nature Rev Genet"},{"issue":"4","key":"3072_CR3","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1104\/pp.008532","volume":"130","author":"JA Kreps","year":"2002","unstructured":"Kreps JA, Wu Y, Chang H-S, Zhu T, Wang X, Harper JF. Transcriptome changes for Arabidopsis in response to salt, osmotic, and cold stress. Plant Physiol. 2002; 130(4):2129\u201341.","journal-title":"Plant Physiol"},{"issue":"8","key":"3072_CR4","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1093\/pcp\/pcn101","volume":"49","author":"A Matsui","year":"2008","unstructured":"Matsui A, Ishida J, Morosawa T, Mochizuki Y, Kaminuma E, Endo TA, Okamoto M, Nambara E, Nakajima M, Kawashima M, et al.Arabidopsis transcriptome analysis under drought, cold, high-salinity and ABA treatment conditions using a tiling array. Plant Cell Physiol. 2008; 49(8):1135\u201349.","journal-title":"Plant Cell Physiol"},{"issue":"2","key":"3072_CR5","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1093\/pcp\/pcq195","volume":"52","author":"K Hamada","year":"2010","unstructured":"Hamada K, Hongo K, Suwabe K, Shimizu A, Nagayama T, Abe R, Kikuchi S, Yamamoto N, Fujii T, Yokoyama K, et al.OryzaExpress: an integrated database of gene expression networks and omics annotations in rice. Plant Cell Physiol. 2010; 52(2):220\u201329.","journal-title":"Plant Cell Physiol"},{"issue":"1","key":"3072_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/pcp\/pcw208","volume":"58","author":"T Kudo","year":"2017","unstructured":"Kudo T, Terashima S, Takaki Y, Tomita K, Saito M, Kanno M, Yokoyama K, Yano K. PlantExpress: a database integrating OryzaExpress and ArthaExpress for single-species and cross-species gene expression network analyses with microarray-based transcriptome data. Plant Cell Physiol. 2017; 58(1):1.","journal-title":"Plant Cell Physiol"},{"issue":"5","key":"3072_CR7","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.jgg.2017.05.003","volume":"44","author":"L Xia","year":"2017","unstructured":"Xia L, Zou D, Sang J, Xu X, Yin H, Li M, Wu S, Hu S, Hao L, Zhang Z. Rice Expression Database (RED): an integrated RNA-Seq-derived gene expression database for rice. J Genet Genomics. 2017; 44(5):235\u201341.","journal-title":"J Genet Genomics"},{"issue":"10","key":"3072_CR8","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/gb-2010-11-10-r106","volume":"11","author":"S Anders","year":"2010","unstructured":"Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010; 11(10):106.","journal-title":"Genome Biol"},{"issue":"1","key":"3072_CR9","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(1):139\u201340.","journal-title":"Bioinformatics"},{"issue":"7","key":"3072_CR10","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43(7):47.","journal-title":"Nucleic Acids Res"},{"issue":"18","key":"3072_CR11","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1093\/bioinformatics\/btu333","volume":"30","author":"MJ Nueda","year":"2014","unstructured":"Nueda MJ, Tarazona S, Conesa A. Next maSigPro: updating maSigPro bioconductor package for RNA-Seq time series. Bioinformatics. 2014; 30(18):2598\u2013602.","journal-title":"Bioinformatics"},{"issue":"8","key":"3072_CR12","doi-asserted-by":"publisher","first-page":"0134540","DOI":"10.1371\/journal.pone.0134540","volume":"10","author":"J Straube","year":"2015","unstructured":"Straube J, Gorse A-D, Huang BE, L\u00ea Cao K-A, et al.A linear mixed model spline framework for analysing time course \u2018omics\u2019 data. PLoS ONE. 2015; 10(8):0134540.","journal-title":"PLoS ONE"},{"issue":"8","key":"3072_CR13","doi-asserted-by":"publisher","first-page":"0160791","DOI":"10.1371\/journal.pone.0160791","volume":"11","author":"A Michna","year":"2016","unstructured":"Michna A, Braselmann H, Selmansberger M, Dietz A, Hess J, Gomolka M, Hornhardt S, Bl\u00fcthgen N, Zitzelsberger H, Unger K. Natural cubic spline regression modeling followed by dynamic network reconstruction for the identification of radiation-sensitivity gene association networks from time-course transcriptome data. PLoS ONE. 2016; 11(8):0160791.","journal-title":"PLoS ONE"},{"issue":"5","key":"3072_CR14","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1093\/bioinformatics\/btw665","volume":"33","author":"J Sander","year":"2017","unstructured":"Sander J, Schultze JL, Yosef N. ImpulseDE: detection of differentially expressed genes in time series data using impulse models. Bioinformatics. 2017; 33(5):757\u20139.","journal-title":"Bioinformatics"},{"key":"3072_CR15","unstructured":"Kang D, Ahn H, Jung W, Kim S. Formulation of a problem for the integrated analysis of heterogeneous time-series gene expression data and cold stress response gene set analysis in Arabidopsis. Proc Korean Inf Sci Soc Confer. 2015:648\u201350."},{"key":"3072_CR16","doi-asserted-by":"crossref","unstructured":"Ahn H, Chae H, Jung W, Kim S. Integration of heterogeneous time series gene expression data by clustering on time dimension. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE: 2017. p. 332\u20135. https:\/\/doi.org\/10.1109\/bigcomp.2017.7881688.","DOI":"10.1109\/BIGCOMP.2017.7881688"},{"issue":"2","key":"3072_CR17","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1089\/cmb.2008.13TT","volume":"16","author":"G Chechik","year":"2009","unstructured":"Chechik G, Koller D. Timing of gene expression responses to environmental changes. J Comput Biol. 2009; 16(2):279\u201390.","journal-title":"J Comput Biol"},{"issue":"6","key":"3072_CR18","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1016\/j.cell.2011.02.015","volume":"144","author":"N Yosef","year":"2011","unstructured":"Yosef N, Regev A. Impulse control: temporal dynamics in gene transcription. Cell. 2011; 144(6):886\u201396.","journal-title":"Cell"},{"issue":"D1","key":"3072_CR19","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1093\/nar\/gkr1090","volume":"40","author":"P Lamesch","year":"2011","unstructured":"Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M, et al.The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 2011; 40(D1):1202\u201310.","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"3072_CR20","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1093\/bioinformatics\/btg405","volume":"20","author":"L Gautier","year":"2004","unstructured":"Gautier L, Cope L, Bolstad BM, Irizarry RA. affy\u2013analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004; 20(3):307\u201315.","journal-title":"Bioinformatics"},{"issue":"1","key":"3072_CR21","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Yoav Benjamini","year":"1995","unstructured":"Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Ser B (Methodol). 1995:289\u2013300. https:\/\/doi.org\/10.1111\/j.2517-6161.1995.tb02031.x.","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"3072_CR22","doi-asserted-by":"crossref","unstructured":"Jung I, Jo K, Kang H, Ahn H, Yu Y, Kim S. TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes. Bioinformatics. 2017:780. https:\/\/doi.org\/10.1093\/bioinformatics\/btw780.","DOI":"10.1093\/bioinformatics\/btw780"},{"issue":"10","key":"3072_CR23","first-page":"1","volume":"50","author":"C Buchta","year":"2012","unstructured":"Buchta C, Kober M, Feinerer I, Hornik K. Spherical k-means clustering. J Stat Softw. 2012; 50(10):1\u201322.","journal-title":"J Stat Softw"},{"issue":"3","key":"3072_CR24","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/1471-2105-13-S3-S15","volume":"13","author":"Y Yang","year":"2012","unstructured":"Yang Y, Nephew K, Kim S. A novel k-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters. BMC Bioinformatics. 2012; 13(3):15.","journal-title":"BMC Bioinformatics"},{"issue":"D1","key":"3072_CR25","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1093\/nar\/gks1193","volume":"41","author":"T Barrett","year":"2013","unstructured":"Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al.NCBI GEO: archive for functional genomics data sets\u2013update. Nucleic Acids Res. 2013; 41(D1):991\u20135.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"3072_CR26","doi-asserted-by":"publisher","first-page":"D1113","DOI":"10.1093\/nar\/gku1057","volume":"43","author":"Nikolay Kolesnikov","year":"2014","unstructured":"Kolesnikov N, Hastings E, Keays M, Melnichuk O, Tang YA, Williams E, Dylag M, Kurbatova N, Brandizi M, Burdett T, et al.ArrayExpress update\u2013simplifying data submissions. Nucleic Acids Res. 2014:1057. https:\/\/doi.org\/10.1093\/nar\/gku1057.","journal-title":"Nucleic Acids Research"},{"issue":"2","key":"3072_CR27","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.cell.2016.08.029","volume":"167","author":"J-K Zhu","year":"2016","unstructured":"Zhu J-K. Abiotic stress signaling and responses in plants. Cell. 2016; 167(2):313\u201324.","journal-title":"Cell"},{"issue":"2","key":"3072_CR28","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s12374-013-0903-y","volume":"56","author":"J Jeon","year":"2013","unstructured":"Jeon J, Kim J. Cold stress signaling networks in Arabidopsis. J Plant Biol. 2013; 56(2):69\u201376.","journal-title":"J Plant Biol"},{"issue":"3","key":"3072_CR29","doi-asserted-by":"publisher","first-page":"5312","DOI":"10.3390\/ijms14035312","volume":"14","author":"K Miura","year":"2013","unstructured":"Miura K, Furumoto T. Cold signaling and cold response in plants. Int J Mole Sci. 2013; 14(3):5312\u201337.","journal-title":"Int J Mole Sci"},{"key":"3072_CR30","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.plantsci.2013.01.008","volume":"205","author":"J Barrero-Gil","year":"2013","unstructured":"Barrero-Gil J, Salinas J. Post-translational regulation of cold acclimation response. Plant Sci. 2013; 205:48\u201354.","journal-title":"Plant Sci"},{"issue":"6","key":"3072_CR31","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/s10265-013-0576-0","volume":"126","author":"T Furuya","year":"2013","unstructured":"Furuya T, Matsuoka D, Nanmori T. Phosphorylation of Arabidopsis thaliana MEKK1 via Ca 2+ signaling as a part of the cold stress response. J Plant Res. 2013; 126(6):833\u201340.","journal-title":"J Plant Res"},{"issue":"1","key":"3072_CR32","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.molcel.2004.06.023","volume":"15","author":"M Teige","year":"2004","unstructured":"Teige M, Scheikl E, Eulgem T, D\u00f3czi R, Ichimura K, Shinozaki K, Dangl JL, Hirt H. The MKK2 pathway mediates cold and salt stress signaling in Arabidopsis. Mole Cell. 2004; 15(1):141\u201352.","journal-title":"Mole Cell"},{"key":"3072_CR33","volume-title":"Abiotic Stress in Plants - Mechanisms and Adaptations","author":"Loredana F.","year":"2011","unstructured":"Ciarmiello LF, Woodrow P, Fuggi A, Pontecorvo G, Carillo P. Plant genes for abiotic stress. In: Abiotic Stress in Plants-Mechanisms and Adaptations. InTech: 2011. https:\/\/doi.org\/10.5772\/22465."},{"key":"3072_CR34","unstructured":"Hemantaranjan A, Vol. 8. Advances in Plant Physiology: Scientific Publishers; 2006. URL https:\/\/books.google.co.kr\/books?id=qFp2DwAAQBAJ."},{"key":"3072_CR35","first-page":"39","volume-title":"Methods in Molecular Biology","author":"Viswanathan Chinnusamy","year":"2010","unstructured":"Chinnusamy V, Zhu J-K, Sunkar R. Gene regulation during cold stress acclimation in plants. In: Plant Stress Tolerance. Springer: 2010. p. 39\u201355. https:\/\/doi.org\/10.1007\/978-1-60761-702-0_3."},{"issue":"5","key":"3072_CR36","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1105\/tpc.105.035881","volume":"18","author":"Y Sakuma","year":"2006","unstructured":"Sakuma Y, Maruyama K, Osakabe Y, Qin F, Seki M, Shinozaki K, Yamaguchi-Shinozaki K. Functional analysis of an Arabidopsis transcription factor, DREB2A, involved in drought-responsive gene expression. Plant Cell. 2006; 18(5):1292\u2013309.","journal-title":"Plant Cell"},{"issue":"6","key":"3072_CR37","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1111\/j.1365-313X.2004.02100.x","volume":"38","author":"K Maruyama","year":"2004","unstructured":"Maruyama K, Sakuma Y, Kasuga M, Ito Y, Seki M, Goda H, Shimada Y, Yoshida S, Shinozaki K, Yamaguchi-Shinozaki K. Identification of cold-inducible downstream genes of the Arabidopsis DREB1A\/CBF3 transcriptional factor using two microarray systems. Plant J. 2004; 38(6):982\u201393.","journal-title":"Plant J"},{"issue":"1","key":"3072_CR38","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.tplants.2016.08.015","volume":"22","author":"N Ohama","year":"2017","unstructured":"Ohama N, Sato H, Shinozaki K, Yamaguchi-Shinozaki K. Transcriptional regulatory network of plant heat stress response. Trends Plant Sci. 2017; 22(1):53\u201365.","journal-title":"Trends Plant Sci"},{"issue":"5","key":"3072_CR39","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1111\/j.1365-313X.2008.03544.x","volume":"55","author":"H-T Liu","year":"2008","unstructured":"Liu H-T, Gao F, Li G-L, Han J-L, Liu D-L, Sun D-Y, Zhou R-G. The calmodulin-binding protein kinase 3 is part of heat-shock signal transduction in Arabidopsis thaliana. Plant J. 2008; 55(5):760\u201373.","journal-title":"Plant J"},{"issue":"2","key":"3072_CR40","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1111\/j.1365-3040.2006.01613.x","volume":"30","author":"H-T LIU","year":"2007","unstructured":"LIU H-T, LI G-L, Chang H, SUN D-Y, ZHOU R-G, Li B. Calmodulin-binding protein phosphatase PP7 is involved in thermotolerance in Arabidopsis. Plant, Cell & Environ. 2007; 30(2):156\u201364.","journal-title":"Plant, Cell & Environ"},{"issue":"5","key":"3072_CR41","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1111\/j.1365-3040.2011.02278.x","volume":"34","author":"H-C LIU","year":"2011","unstructured":"LIU H-C, LIAO H-T, CHARNG Y-Y. The role of class A1 heat shock factors (HSFA1s) in response to heat and other stresses in Arabidopsis. Plant, Cell Environ. 2011; 34(5):738\u201351.","journal-title":"Plant, Cell Environ"},{"issue":"21","key":"3072_CR42","doi-asserted-by":"publisher","first-page":"6139","DOI":"10.1093\/jxb\/erw376","volume":"67","author":"M Muench","year":"2016","unstructured":"Muench M, Hsin C-H, Ferber E, Berger S, Mueller MJ. Reactive electrophilic oxylipins trigger a heat stress-like response through HSFA1 transcription factors. J Experiment Botany. 2016; 67(21):6139\u201348.","journal-title":"J Experiment Botany"},{"issue":"1","key":"3072_CR43","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1186\/1471-2164-15-211","volume":"15","author":"H Zhang","year":"2014","unstructured":"Zhang H, Liu W-Z, Zhang Y, Deng M, Niu F, Yang B, Wang X, Wang B, Liang W, Deyholos MK, et al.Identification, expression and interaction analyses of calcium-dependent protein kinase (CPK) genes in canola (Brassica napus L.)BMC Genomics. 2014; 15(1):211.","journal-title":"BMC Genomics"},{"issue":"1","key":"3072_CR44","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1104\/pp.103.033068","volume":"134","author":"\u00dc. Kolukisaoglu","year":"2004","unstructured":"Kolukisaoglu \u00dc., Weinl S, Blazevic D, Batistic O, Kudla J. Calcium sensors and their interacting protein kinases: genomics of the Arabidopsis and rice CBL-CIPK signaling networks. Plant Physiol. 2004; 134(1):43\u201358.","journal-title":"Plant Physiol"},{"issue":"5","key":"3072_CR45","doi-asserted-by":"publisher","first-page":"547","DOI":"10.4161\/psb.19803","volume":"7","author":"H-C Liu","year":"2012","unstructured":"Liu H-C, Charng Y-y. Acquired thermotolerance independent of heat shock factor A1 (HsfA1), the master regulator of the heat stress response. Plant Signal Behav. 2012; 7(5):547\u201350.","journal-title":"Plant Signal Behav"},{"issue":"4","key":"3072_CR46","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1111\/pbi.12659","volume":"15","author":"Pierre Jacob","year":"2017","unstructured":"Jacob P, Hirt H, Bendahmane A. The heat shock protein\/chaperone network and multiple stress resistance. Plant Biotechnol J. 2016. https:\/\/doi.org\/10.1111\/pbi.12659.","journal-title":"Plant Biotechnology Journal"},{"issue":"3","key":"3072_CR47","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1016\/j.bbrc.2008.01.134","volume":"368","author":"T Yoshida","year":"2008","unstructured":"Yoshida T, Sakuma Y, Todaka D, Maruyama K, Qin F, Mizoi J, Kidokoro S, Fujita Y, Shinozaki K, Yamaguchi-Shinozaki K. Functional analysis of an Arabidopsis heat-shock transcription factor HsfA3 in the transcriptional cascade downstream of the DREB2A stress-regulatory system. Biochem Biophys Res Commun. 2008; 368(3):515\u201321.","journal-title":"Biochem Biophys Res Commun"},{"issue":"3","key":"3072_CR48","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1104\/pp.111.179036","volume":"157","author":"M Ikeda","year":"2011","unstructured":"Ikeda M, Mitsuda N, Ohme-Takagi M. Arabidopsis HsfB1 and HsfB2b act as repressors of the expression of heat-inducible Hsfs but positively regulate the acquired thermotolerance. Plant Physiol. 2011; 157(3):1243\u201354.","journal-title":"Plant Physiol"},{"issue":"5","key":"3072_CR49","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/s11103-005-5750-x","volume":"60","author":"F Schramm","year":"2006","unstructured":"Schramm F, Ganguli A, Kiehlmann E, Englich G, Walch D, von Koskull-D\u00f6ring P. The heat stress transcription factor HsfA2 serves as a regulatory amplifier of a subset of genes in the heat stress response in Arabidopsis. Plant Mole Biol. 2006; 60(5):759\u201372.","journal-title":"Plant Mole Biol"},{"issue":"24","key":"3072_CR50","doi-asserted-by":"publisher","first-page":"3135","DOI":"10.1093\/bioinformatics\/btq596","volume":"26","author":"G Su","year":"2010","unstructured":"Su G, Kuchinsky A, Morris JH, States DJ, Meng F. GLay: community structure analysis of biological networks. Bioinformatics. 2010; 26(24):3135\u20137.","journal-title":"Bioinformatics"},{"issue":"1","key":"3072_CR51","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1186\/1471-2105-12-436","volume":"12","author":"JH Morris","year":"2011","unstructured":"Morris JH, Apeltsin L, Newman AM, Baumbach J, Wittkop T, Su G, Bader GD, Ferrin TE. clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics. 2011; 12(1):436.","journal-title":"BMC Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3072-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-019-3072-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-019-3072-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T23:32:37Z","timestamp":1722123157000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-019-3072-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":51,"journal-issue":{"issue":"S16","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3072"],"URL":"https:\/\/doi.org\/10.1186\/s12859-019-3072-2","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"2 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"No ethics approval was needed for this study.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"588"}}