{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:57:28Z","timestamp":1773856648406,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2008,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this paper, a novel approach is presented in which 128 4-mer motifs in conjunction with a non-linear machine-learning algorithm utilising a Support Vector Machine (SVM) are used to distinguish between promoter and non-promoter DNA sequences. By applying this approach to plant, Drosophila, human, mouse and rat sequences, the classification model has showed 7-fold cross-validation percentage accuracies of 83.81%, 94.82%, 91.25%, 90.77% and 82.35% respectively. The high sensitivity and specificity value of 0.86 and 0.90 for plant; 0.96 and 0.92 for Drosophila; 0.88 and 0.92 for human; 0.78 and 0.84 for mouse and 0.82 and 0.80 for rat demonstrate that this technique is less prone to false positive results and exhibits better performance than many other tools. Moreover, this model successfully identifies location of promoter using TATA weight matrix.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The high sensitivity and specificity indicate that 4-mer frequencies in conjunction with supervised machine-learning methods can be beneficial in the identification of RNA pol II promoters comparative to other methods. This approach can be extended to identify promoters in sequences for other eukaryotic genomes.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-9-414","type":"journal-article","created":{"date-parts":[[2008,10,4]],"date-time":"2008-10-04T18:13:18Z","timestamp":1223143998000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach"],"prefix":"10.1186","volume":"9","author":[{"given":"Firoz","family":"Anwar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Murtuza","family":"Baker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taskeed","family":"Jabid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md","family":"Mehedi Hasan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Shoyaib","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haseena","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ray","family":"Walshe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2008,10,4]]},"reference":[{"key":"2399_CR1","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/0022-2836(90)90223-9","volume":"212","author":"P Bucher","year":"1990","unstructured":"Bucher P: Weight rr matrix description of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. J Mol Biol 1990, 212: 563\u2013578.","journal-title":"J Mol Biol"},{"key":"2399_CR2","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1101\/gr.7.9.861","volume":"7","author":"JW Fickett","year":"1997","unstructured":"Fickett JW, Hatzigeorgiou AC: Eukaryotic promoter recognition. Genome Res 1997, 7: 861\u2013878.","journal-title":"Genome Res"},{"key":"2399_CR3","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1101\/gr.8.3.319","volume":"8","author":"MQ Zhang","year":"1998","unstructured":"Zhang MQ: Identification of Human Gene Core Promoters in Silico. Genome Research 1998, 8: 319\u2013326.","journal-title":"Genome Research"},{"key":"2399_CR4","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1093\/bioinformatics\/15.5.362","volume":"15","author":"U Ohler","year":"1999","unstructured":"Ohler U, Harbeck S, Niemann H, Noth E, Reese M: Interpolated Markov chains for eukaryotic promoter recognition. Bioinformatics 1999, 15: 362\u2013369.","journal-title":"Bioinformatics"},{"key":"2399_CR5","doi-asserted-by":"publisher","first-page":"5943","DOI":"10.1093\/nar\/gkl608","volume":"34","author":"O Uwe","year":"2006","unstructured":"Uwe O: Identification of core promoter modules in Drosophila and their application in accurate transcription start site prediction. Nucleic Acids Res 2006, 34: 5943\u20135950.","journal-title":"Nucleic Acids Res"},{"key":"2399_CR6","doi-asserted-by":"publisher","first-page":"1606","DOI":"10.1101\/gad.1193404","volume":"18","author":"YL Chin","year":"2004","unstructured":"Chin YL, Santoso B, Boulay T, Dong E, Ohler U, Kadonaga JT: The MTE, a new core promoter element for transcription by RNA polymerase II. Genes and Dev 2004, 18: 1606\u20131617.","journal-title":"Genes and Dev"},{"key":"2399_CR7","first-page":"356","volume":"15","author":"S Knudsen","year":"1999","unstructured":"Knudsen S: Promoter 2.0: for recognition of Pol II promoter sequences. Biotechnologies 1999, 15: 356\u2013361.","journal-title":"Biotechnologies"},{"key":"2399_CR8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.biosystems.2005.09.001","volume":"83","author":"SP Pandey","year":"2006","unstructured":"Pandey SP, Krishnamachari A: Computational analysis of plant RNA Pol-II promoters. Bio Systems 2006, 83: 38\u201350.","journal-title":"Bio Systems"},{"key":"2399_CR9","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1101\/gr.154601","volume":"11","author":"M Scherf","year":"2001","unstructured":"Scherf M, Klingenhoff A, Frech K, Qu TK, Schneider R, Grote K, Frisch M, Gailus-Durner V, Seidel A, Brack-Werner R, Werner T: First pass annotation of promoters of human chromosome 22. Genome Res 2001, 11: 333\u2013340.","journal-title":"Genome Res"},{"key":"2399_CR10","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1038\/ng780","volume":"29","author":"VR Davuluri","year":"2001","unstructured":"Davuluri VR, Grosse I, Zhang MQ: Computational identification of promoters and first exons in the human genome. Nature Genetics 2001, 29: 412\u2013417.","journal-title":"Nature Genetics"},{"key":"2399_CR11","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1093\/bioinformatics\/18.1.198","volume":"18","author":"VB Bajic","year":"2002","unstructured":"Bajic VB, Seah SH, Chong A, Zhang G, Koh JLY, Brusic V: Dragon promoter finder: recognition of vertebrate RNA polymerase II promoters. Bioinformatics 2002, 18: 198\u2013199.","journal-title":"Bioinformatics"},{"key":"2399_CR12","unstructured":"Dragon Promoter Finder 1.5"},{"key":"2399_CR13","volume-title":"Biocomputing Proceedings of the 1996 Pacific Symposium","author":"M Reese","year":"1996","unstructured":"Reese M, Harris NL, Eeckman FH: Large scale sequencing specific neural networks for promoter and splice site recognition. In Biocomputing Proceedings of the 1996 Pacific Symposium. Edited by: Hunter L, Klein T. World Scientific Co; 1996."},{"key":"2399_CR14","unstructured":"NNP 2.2[http:\/\/www.fruitfly.org\/seq_tools\/promoter.html]"},{"key":"2399_CR15","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/S0097-8485(01)00099-7","volume":"26","author":"MG Reese","year":"2001","unstructured":"Reese MG: Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome. Computers and Chemistry 2001, 26: 51\u201356.","journal-title":"Computers and Chemistry"},{"key":"2399_CR16","unstructured":"Prom 2[http:\/\/www.cbs.dtu.dk\/services\/Promoter\/]"},{"key":"2399_CR17","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1006\/jmbi.2000.3589","volume":"297","author":"M Scherf","year":"2000","unstructured":"Scherf M, Klingenhoff A, Werner T: Highly specific localization of promoter regions in large genomic sequences by PromoterInspector: a novel context analysis approach. J Mol Biol 2000, 297: 599\u2013606.","journal-title":"J Mol Biol"},{"key":"2399_CR18","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1101\/gr.216102","volume":"12","author":"TA Down","year":"2002","unstructured":"Down TA, Hubbard TJ: Computational detection and location of transcription start sites in mammalian genomic DNA. Genome Res 2002, 12: 458\u2013461.","journal-title":"Genome Res"},{"key":"2399_CR19","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1093\/abbs\/36.4.250","volume":"36","author":"M Xiao-Tu","year":"2004","unstructured":"Xiao-Tu M, Min-Ping Q, Hai-Xu T: Predicting Polymerase II Core Promoters by Cooperating Transcription Factor Binding Sites in Eukaryotic Genes. Acta Biochimica et Biophysica Sinica 2004, 36: 250\u2013258.","journal-title":"Acta Biochimica et Biophysica Sinica"},{"key":"2399_CR20","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1093\/nar\/gki271","volume":"33","author":"G Rajeev","year":"2005","unstructured":"Rajeev G, Pankaj S: Human pol II promoter prediction: time series descriptors and machine learning. Nucleic Acids Res 2005, 33: 1332\u20131336.","journal-title":"Nucleic Acids Res"},{"key":"2399_CR21","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1093\/nar\/gki247","volume":"33","author":"IA Shahmuradov","year":"2005","unstructured":"Shahmuradov IA, Solovyev VV, Gammerman1 AJ: Plant promoter prediction with confidence estimation. Nucleic Acids Research 2005, 33: 1069\u20131076.","journal-title":"Nucleic Acids Research"},{"key":"2399_CR22","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1093\/bioinformatics\/bti172","volume":"21","author":"N Gershenzon","year":"2005","unstructured":"Gershenzon N, Ioshikhes I: Synergy of human Pol II core promoter elements revealed by statistical sequence analysis. Bioinformatics 2005, 21: 1295\u20131300.","journal-title":"Bioinformatics"},{"key":"2399_CR23","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1186\/1471-2105-7-114","volume":"7","author":"V Jin","year":"2006","unstructured":"Jin V, Singer G, Davuluri R: Genome-wide Analysis of Core Promoters from Conserved Human and Mouse Orthologous Pairs. BMC Bioinformatics 2006, 7: 114.","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"2399_CR24","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1093\/bioinformatics\/12.5.391","volume":"12","author":"G Hutchinson","year":"1996","unstructured":"Hutchinson G: The prediction of vertebrate promoter regions using differential hexamer frequency analysis. Bioinformatics 1996, 12(5):391\u2013398.","journal-title":"Bioinformatics"},{"key":"2399_CR25","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1186\/1471-2105-6-262","volume":"6","author":"B Chan","year":"2005","unstructured":"Chan B, Kibler D: Using hexamers to predict cis-regulatory motifs in Drosophila. BMC Bioinformatics 2005, 6: 262.","journal-title":"BMC Bioinformatics"},{"key":"2399_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2002-3-12-research0087","volume":"3","author":"U Ohler","year":"2002","unstructured":"Ohler U, Liao G, Niemann H, Rubin G: Computational analysis of core promoters in the Drosophila genome. Genome Biol 2002, 3: 1\u201312.","journal-title":"Genome Biol"},{"key":"2399_CR27","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1093\/nar\/gkg041","volume":"31","author":"I Shahmuradov","year":"2003","unstructured":"Shahmuradov I, Gammerman A, Hancock JM, Bramley PM, Solovyev VV: PlantProm: a database of plant promoter sequences. Nucleic Acids Res 2003, 31: 114\u2013117.","journal-title":"Nucleic Acids Res"},{"key":"2399_CR28","first-page":"307","volume-title":"Pac Symp Biocomput","author":"M Zhang","year":"1998","unstructured":"Zhang M: A discrimination study of human core-promoters. Pac Symp Biocomput 1998, 307\u2013309."},{"key":"2399_CR29","doi-asserted-by":"publisher","first-page":"E58","DOI":"10.1371\/journal.pbio.0000058","volume":"1","author":"R Hardison","year":"2003","unstructured":"Hardison R: Comparative Genomics. PLoS Biol 2003, 1: E58.","journal-title":"PLoS Biol"},{"key":"2399_CR30","unstructured":"EPD[http:\/\/www.epd.isb-sib.ch\/]"},{"key":"2399_CR31","doi-asserted-by":"publisher","first-page":"D82","DOI":"10.1093\/nar\/gkj146","volume":"34","author":"C Schmid","year":"2006","unstructured":"Schmid C, Perier R, Praz V, Bucher P: EPD in its twentieth year: towards complete promoter coverage of selected model organisms. Nucleic Acids Res 2006, 34: D82-D85.","journal-title":"Nucleic Acids Res"},{"key":"2399_CR32","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1145\/130385.130401","volume-title":"Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory","author":"BE Boser","year":"1992","unstructured":"Boser BE, Guyon IM, Vapnik VN: A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. ACM Press; 1992:144\u2013152."},{"key":"2399_CR33","volume-title":"A Practical Guide to Support Vector Classification","author":"H Chih-Wei","year":"2004","unstructured":"Chih-Wei H, Chih-Chung C, Chih-Jen L: A Practical Guide to Support Vector Classification. National Taiwan University; 2004."},{"issue":"1","key":"2399_CR34","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1080\/00031305.1983.10483087","volume":"37","author":"E Bradley","year":"1983","unstructured":"Bradley E, Gail G: A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. The American Statistician 1983, 37(1):36\u201348.","journal-title":"The American Statistician"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-9-414.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T11:02:30Z","timestamp":1738407750000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-9-414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,10,4]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2008,12]]}},"alternative-id":["2399"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-9-414","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,10,4]]},"assertion":[{"value":"7 January 2008","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2008","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2008","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"414"}}