{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:10:57Z","timestamp":1772165457770,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Long non-coding RNAs (lncRNAs) can exert functions via forming triplex with DNA. The current methods in predicting the triplex formation mainly rely on mathematic statistic according to the base paring rules. However, these methods have two main limitations: (1) they identify a large number of triplex-forming lncRNAs, but the limited number of experimentally verified triplex-forming lncRNA indicates that maybe not all of them can form triplex in practice, and (2) their predictions only consider the theoretical relationship while lacking the features from the experimentally verified data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In this work, we develop an integrated program named TriplexFPP (Triplex Forming Potential Prediction), which is the first machine learning model in DNA:RNA triplex prediction. TriplexFPP predicts the most likely triplex-forming lncRNAs and DNA sites based on the experimentally verified data, where the high-level features are learned by the convolutional neural networks. In the fivefold cross validation, the average values of Area Under the ROC curves and PRC curves for removed redundancy triplex-forming lncRNA dataset with threshold 0.8 are 0.9649 and 0.9996, and these two values for triplex DNA sites prediction are 0.8705 and 0.9671, respectively. Besides, we also briefly summarize the\n                      <jats:italic>cis<\/jats:italic>\n                      and\n                      <jats:italic>trans<\/jats:italic>\n                      targeting of triplexes lncRNAs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The TriplexFPP is able to predict the most likely triplex-forming lncRNAs from all the lncRNAs with computationally defined triplex forming capacities and the potential of a DNA site to become a triplex. It may provide insights to the exploration of lncRNA functions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-020-03864-0","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T09:03:10Z","timestamp":1605171790000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Deep learning based DNA:RNA triplex forming potential prediction"],"prefix":"10.1186","volume":"21","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahui","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8547-6387","authenticated-orcid":false,"given":"Chee Keong","family":"Kwoh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"issue":"3","key":"3864_CR1","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1038\/nsmb.2480","volume":"20","author":"TR Mercer","year":"2013","unstructured":"Mercer TR, Mattick JS. Structure and function of long noncoding RNAs in epigenetic regulation. Nat Struct Mol Biol. 2013;20(3):300.","journal-title":"Nat Struct Mol Biol"},{"issue":"7385","key":"3864_CR2","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1038\/nature10887","volume":"482","author":"M Guttman","year":"2012","unstructured":"Guttman M, Rinn JL. Modular regulatory principles of large non-coding RNAs. Nature. 2012;482(7385):339\u201346.","journal-title":"Nature"},{"issue":"12","key":"3864_CR3","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1038\/nrm.2016.126","volume":"17","author":"JM Engreitz","year":"2016","unstructured":"Engreitz JM, Ollikainen N, Guttman M. Long non-coding RNAs: spatial amplifiers that control nuclear structure and gene expression. Nat Rev Mol Cell Biol. 2016;17(12):756.","journal-title":"Nat Rev Mol Cell Biol"},{"key":"3864_CR4","first-page":"7","volume":"2018","author":"I Antonov","year":"2018","unstructured":"Antonov I, Medvedeva YA. Purine-rich low complexity regions are potential RNA binding hubs in the human genome. F1000Research. 2018;2018:7.","journal-title":"F1000Research"},{"issue":"20","key":"3864_CR5","doi-asserted-by":"publisher","first-page":"2264","DOI":"10.1101\/gad.590910","volume":"24","author":"KM Schmitz","year":"2010","unstructured":"Schmitz KM, Mayer C, Postepska A, Grummt I. Interaction of noncoding RNA with the rDNA promoter mediates recruitment of DNMT3b and silencing of rRNA genes. Genes Dev. 2010;24(20):2264\u20139.","journal-title":"Genes Dev"},{"issue":"10","key":"3864_CR6","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.4161\/rna.26165","volume":"10","author":"P Grote","year":"2013","unstructured":"Grote P, Herrmann BG. The long non-coding RNA Fendrr links epigenetic control mechanisms to gene regulatory networks in mammalian embryogenesis. RNA Biol. 2013;10(10):1579\u201385.","journal-title":"RNA Biol"},{"issue":"6","key":"3864_CR7","doi-asserted-by":"publisher","first-page":"7743","DOI":"10.1038\/ncomms8743","volume":"24","author":"T Mondal","year":"2015","unstructured":"Mondal T, Subhash S, Vaid R, Enroth S, Uday S, Reinius B, Mitra S, Mohammed A, James AR, Hoberg E, Moustakas A. MEG3 long noncoding RNA regulates the TGF-\u03b2 pathway genes through formation of RNA\u2013DNA triplex structures. Nat Commun. 2015;24(6):7743.","journal-title":"Nat Commun"},{"issue":"3","key":"3864_CR8","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.celrep.2015.03.043","volume":"11","author":"VB O\u2019Leary","year":"2015","unstructured":"O\u2019Leary VB, Ovsepian SV, Carrascosa LG, Buske FA, Radulovic V, Niyazi M, Moertl S, Trau M, Atkinson MJ, Anastasov N. PARTICLE, a triplex-forming long ncRNA, regulates locus-specific methylation in response to low-dose irradiation. Cell Rep. 2015;11(3):474\u201385.","journal-title":"Cell Rep"},{"issue":"4","key":"3864_CR9","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1016\/j.molcel.2015.10.001","volume":"60","author":"A Postepska-Igielska","year":"2015","unstructured":"Postepska-Igielska A, Giwojna A, Gasri-Plotnitsky L, Schmitt N, Dold A, Ginsberg D, Grummt I. LncRNA Khps1 regulates expression of the proto-oncogene SPHK1 via triplex-mediated changes in chromatin structure. Mol Cell. 2015;60(4):626\u201336.","journal-title":"Mol Cell"},{"issue":"22","key":"3864_CR10","doi-asserted-by":"publisher","first-page":"10631","DOI":"10.1093\/nar\/gkw802","volume":"44","author":"M Kalwa","year":"2016","unstructured":"Kalwa M, H\u00e4nzelmann S, Otto S, Kuo CC, Franzen J, Joussen S, Fernandez-Rebollo E, Rath B, Koch C, Hofmann A, Lee SH. The lncRNA HOTAIR impacts on mesenchymal stem cells via triple helix formation. Nucl Acids Res. 2016;44(22):10631\u201343.","journal-title":"Nucl Acids Res"},{"issue":"8","key":"3864_CR11","first-page":"1","volume":"9","author":"S Wang","year":"2018","unstructured":"Wang S, Ke H, Zhang H, Ma Y, Ao L, Zou L, Yang Q, Zhu H, Nie J, Wu C, Jiao B. LncRNA MIR100HG promotes cell proliferation in triple-negative breast cancer through triplex formation with p27 loci. Cell Death Dis. 2018;9(8):1\u20131.","journal-title":"Cell Death Dis"},{"issue":"11\u201312","key":"3864_CR12","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1101\/gad.311688.118","volume":"32","author":"Z Zhao","year":"2018","unstructured":"Zhao Z, Sent\u00fcrk N, Song C, Grummt I. lncRNA PAPAS tethered to the rDNA enhancer recruits hypophosphorylated CHD4\/NuRD to repress rRNA synthesis at elevated temperatures. Genes Dev. 2018;32(11\u201312):836\u201348.","journal-title":"Genes Dev"},{"issue":"4","key":"3864_CR13","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1016\/j.molcel.2011.08.027","volume":"44","author":"C Chu","year":"2011","unstructured":"Chu C, Qu K, Zhong FL, Artandi SE, Chang HY. Genomic maps of long noncoding RNA occupancy reveal principles of RNA-chromatin interactions. Mol Cell. 2011;44(4):667\u201378.","journal-title":"Mol Cell"},{"issue":"51","key":"3864_CR14","doi-asserted-by":"publisher","first-page":"20497","DOI":"10.1073\/pnas.1113536108","volume":"108","author":"MD Simon","year":"2011","unstructured":"Simon MD, Wang CI, Kharchenko PV, West JA, Chapman BA, Alekseyenko AA, Borowsky ML, Kuroda MI, Kingston RE. The genomic binding sites of a noncoding RNA. Proc Natl Acad Sci. 2011;108(51):20497\u2013502.","journal-title":"Proc Natl Acad Sci"},{"issue":"6147","key":"3864_CR15","doi-asserted-by":"publisher","first-page":"1237973","DOI":"10.1126\/science.1237973","volume":"341","author":"JM Engreitz","year":"2013","unstructured":"Engreitz JM, Pandya-Jones A, McDonel P, Shishkin A, Sirokman K, Surka C, Kadri S, Xing J, Goren A, Lander ES, Plath K. The Xist lncRNA exploits three-dimensional genome architecture to spread across the X chromosome. Science. 2013;341(6147):1237973.","journal-title":"Science"},{"issue":"5","key":"3864_CR16","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1093\/nar\/gky1305","volume":"47","author":"N Sent\u00fcrk Cetin","year":"2019","unstructured":"Sent\u00fcrk Cetin N, Kuo CC, Ribarska T, Li R, Costa IG, Grummt I. Isolation and genome-wide characterization of cellular DNA: RNA triplex structures. Nucl Acids Res. 2019;47(5):2306\u201321.","journal-title":"Nucl Acids Res"},{"issue":"7","key":"3864_CR17","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1101\/gr.130237.111","volume":"22","author":"FA Buske","year":"2012","unstructured":"Buske FA, Bauer DC, Mattick JS, Bailey TL. Triplexator: detecting nucleic acid triple helices in genomic and transcriptomic data. Genome Res. 2012;22(7):1372\u201381.","journal-title":"Genome Res"},{"issue":"15","key":"3864_CR18","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1093\/bioinformatics\/btt315","volume":"29","author":"FA Buske","year":"2013","unstructured":"Buske FA, Bauer DC, Mattick JS, Bailey TL. Triplex-inspector: an analysis tool for triplex-mediated targeting of genomic loci. Bioinformatics. 2013;29(15):1895\u20137.","journal-title":"Bioinformatics"},{"issue":"2","key":"3864_CR19","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1093\/bioinformatics\/btu643","volume":"31","author":"S He","year":"2015","unstructured":"He S, Zhang H, Liu H, Zhu H. LongTarget: a tool to predict lncRNA DNA-binding motifs and binding sites via Hoogsteen base-pairing analysis. Bioinformatics. 2015;31(2):178\u201386.","journal-title":"Bioinformatics"},{"issue":"6","key":"3864_CR20","doi-asserted-by":"publisher","first-page":"e32","DOI":"10.1093\/nar\/gkz037","volume":"47","author":"CC Kuo","year":"2019","unstructured":"Kuo CC, H\u00e4nzelmann S, Sent\u00fcrk Cetin N, Frank S, Zajzon B, Derks JP, Akhade VS, Ahuja G, Kanduri C, Grummt I, Kurian L. Detection of RNA\u2013DNA binding sites in long noncoding RNAs. Nucl Acids Res. 2019;47(6):e32.","journal-title":"Nucl Acids Res"},{"issue":"D1","key":"3864_CR21","doi-asserted-by":"publisher","first-page":"D766","DOI":"10.1093\/nar\/gky955","volume":"47","author":"A Frankish","year":"2019","unstructured":"Frankish A, Diekhans M, Ferreira AM, Johnson R, Jungreis I, Loveland J, Mudge JM, Sisu C, Wright J, Armstrong J, Barnes I. GENCODE reference annotation for the human and mouse genomes. Nucl Acids Res. 2019;47(D1):D766\u201373.","journal-title":"Nucl Acids Res"},{"key":"3864_CR22","doi-asserted-by":"crossref","unstructured":"Navarro C, Cano C, Cuadros M, Herrera-Merchan A, Molina M, Blanco A. A mechanistic study of lncRNA Fendrr regulation of FoxF1 lung cancer tumor supressor. In: International conference on bioinformatics and biomedical engineering 2016 Apr 20 (pp. 781\u2013789). Springer, Cham.","DOI":"10.1007\/978-3-319-31744-1_67"},{"issue":"3","key":"3864_CR23","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1093\/nar\/gky1171","volume":"47","author":"AA Ageeli","year":"2019","unstructured":"Ageeli AA, McGovern-Gooch KR, Kaminska MM, Baird NJ. Finely tuned conformational dynamics regulate the protective function of the lncRNA MALAT1 triple helix. Nucl Acids Res. 2019;47(3):1468\u201381.","journal-title":"Nucl Acids Res"},{"issue":"23","key":"3864_CR24","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","volume":"28","author":"L Fu","year":"2012","unstructured":"Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150\u20132.","journal-title":"Bioinformatics"},{"key":"3864_CR25","first-page":"2018","volume":"1","author":"SE Hunt","year":"2018","unstructured":"Hunt SE, McLaren W, Gil L, Thormann A, Schuilenburg H, Sheppard D, Parton A, Armean IM, Trevanion SJ, Flicek P, Cunningham F. Ensembl variation resources. Database. 2018;1:2018.","journal-title":"Database"},{"key":"3864_CR26","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa039","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Jia C, Fullwood MJ, Kwoh CK. DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction. Brief Bioinform. 2020. https:\/\/doi.org\/10.1093\/bib\/bbaa039.","journal-title":"Brief Bioinform"},{"key":"3864_CR27","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa228","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Jia C, Kwoh CK. Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach. Brief Bioinform. 2020. https:\/\/doi.org\/10.1093\/bib\/bbaa228.","journal-title":"Brief Bioinform"},{"issue":"4","key":"3864_CR28","doi-asserted-by":"crossref","first-page":"54","DOI":"10.3390\/ncrna5040054","volume":"5","author":"K Mishra","year":"2019","unstructured":"Mishra K, Kanduri C. Understanding long noncoding RNA and chromatin interactions: what we know so far. Noncoding RNA. 2019;5(4):54.","journal-title":"Noncoding RNA"},{"issue":"11","key":"3864_CR29","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1016\/j.chembiol.2016.09.011","volume":"23","author":"Y Li","year":"2016","unstructured":"Li Y, Syed J, Sugiyama H. RNA\u2013DNA triplex formation by long noncoding RNAs. Cell Chem Biol. 2016;23(11):1325\u201333.","journal-title":"Cell Chem Biol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03864-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-03864-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03864-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T16:01:44Z","timestamp":1669564904000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03864-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,12]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3864"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03864-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-41662\/v2","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-41662\/v3","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.3.rs-41662\/v1","asserted-by":"object"}]},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,12]]},"assertion":[{"value":"10 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","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":"522"}}