{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:06:54Z","timestamp":1759190814633,"version":"3.38.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Algorithms Mol Biol"],"published-print":{"date-parts":[[2011,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>With an increasing number of plant genome sequences, it has become important to develop a robust computational method for detecting plant promoters. Although a wide variety of programs are currently available, prediction accuracy of these still requires further improvement. The limitations of these methods can be addressed by selecting appropriate features for distinguishing promoters and non-promoters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we proposed two feature selection approaches based on hexamer sequences: the Frequency Distribution Analyzed Feature Selection Algorithm (FDAFSA) and the Random Triplet Pair Feature Selecting Genetic Algorithm (RTPFSGA). In FDAFSA, adjacent triplet-pairs (hexamer sequences) were selected based on the difference in the frequency of hexamers between promoters and non-promoters. In RTPFSGA, random triplet-pairs (RTPs) were selected by exploiting a genetic algorithm that distinguishes frequencies of non-adjacent triplet pairs between promoters and non-promoters. Then, a support vector machine (SVM), a nonlinear machine-learning algorithm, was used to classify promoters and non-promoters by combining these two feature selection approaches. We referred to this novel algorithm as PromoBot.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Promoter sequences were collected from the PlantProm database. Non-promoter sequences were collected from plant mRNA, rRNA, and tRNA of PlantGDB and plant miRNA of miRBase. Then, in order to validate the proposed algorithm, we applied a 5-fold cross validation test. Training data sets were used to select features based on FDAFSA and RTPFSGA, and these features were used to train the SVM. We achieved 89% sensitivity and 86% specificity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We compared our PromoBot algorithm to five other algorithms. It was found that the sensitivity and specificity of PromoBot performed well (or even better) with the algorithms tested. These results show that the two proposed feature selection methods based on hexamer frequencies and random triplet-pair could be successfully incorporated into a supervised machine learning method in promoter classification problem. As such, we expect that PromoBot can be used to help identify new plant promoters. Source codes and analysis results of this work could be provided upon request.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1748-7188-6-19","type":"journal-article","created":{"date-parts":[[2011,6,29]],"date-time":"2011-06-29T18:18:49Z","timestamp":1309371529000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Prediction of plant promoters based on hexamers and random triplet pair analysis"],"prefix":"10.1186","volume":"6","author":[{"given":"AKM","family":"Azad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saima","family":"Shahid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nasimul","family":"Noman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyunju","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2011,6,28]]},"reference":[{"issue":"6","key":"132_CR1","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1023\/A:1006129924683","volume":"39","author":"GJ de Boer","year":"1999","unstructured":"de Boer GJ, Testerink C, Pielage G, Nijkamp HJ, Stuitje AR: Sequences surrounding the transcription initiation site of the Arabidopsis enoyl-acyl carrier protein reductase gene control seed expression in transgenic tobacco. Plant Mol Biol. 1999, 39 (6): 1197-1207. 10.1023\/A:1006129924683","journal-title":"Plant Mol Biol"},{"issue":"9","key":"132_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 AG: Eukaryotic promoter recognition. Genome Res. 1997, 7 (9): 861-878.","journal-title":"Genome Res"},{"issue":"Suppl 1","key":"132_CR3","doi-asserted-by":"publisher","first-page":"S199","DOI":"10.1093\/bioinformatics\/17.suppl_1.S199","volume":"17","author":"U Ohler","year":"2001","unstructured":"Ohler U, Niemann H, Liao G, Rubin GM: Joint modeling of DNA sequence and physical properties to improve eukaryotic promoter recognition. Bioinformatics. 2001, 17 (Suppl 1): S199-206. 10.1093\/bioinformatics\/17.suppl_1.S199","journal-title":"Bioinformatics"},{"issue":"5","key":"132_CR4","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1093\/bioinformatics\/15.5.356","volume":"15","author":"S Knudsen","year":"1999","unstructured":"Knudsen S: Promoter2.0: for the recognition of PolII promoter sequences. Bioinformatics. 1999, 15 (5): 356-361. 10.1093\/bioinformatics\/15.5.356","journal-title":"Bioinformatics"},{"key":"132_CR5","first-page":"294","volume":"5","author":"V Solovyev","year":"1997","unstructured":"Solovyev V, Salamov A: The Gene-Finder computer tools for analysis of human and model organisms genome sequences. Proc Int Conf Intell Syst Mol Biol. 1997, 5: 294-302.","journal-title":"Proc Int Conf Intell Syst Mol Biol"},{"issue":"3","key":"132_CR6","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1093\/nar\/gki247","volume":"33","author":"IA Shahmuradov","year":"2005","unstructured":"Shahmuradov IA, Solovyev VV, Gammerman AJ: Plant promoter prediction with confidence estimation. Nucleic Acids Res. 2005, 33 (3): 1069-1076. 10.1093\/nar\/gki247","journal-title":"Nucleic Acids Res"},{"issue":"18","key":"132_CR7","doi-asserted-by":"publisher","first-page":"6219","DOI":"10.1093\/nar\/gkm685","volume":"35","author":"YY Yamamoto","year":"2007","unstructured":"Yamamoto YY, Ichida H, Abe T, Suzuki Y, Sugano S, Obokata J: Differentiation of core promoter architecture between plants and mammals revealed by LDSS analysis. Nucleic Acids Res. 2007, 35 (18): 6219-6226. 10.1093\/nar\/gkm685","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"132_CR8","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1139\/G09-001","volume":"52","author":"P Civan","year":"2009","unstructured":"Civan P, Svec M: Genome-wide analysis of rice (Oryza sativa L. subsp. japonica) TATA box and Y Patch promoter elements. Genome. 2009, 52 (3): 294-297. 10.1139\/G09-001","journal-title":"Genome"},{"issue":"1","key":"132_CR9","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. Biosystems. 2006, 83 (1): 38-50. 10.1016\/j.biosystems.2005.09.001","journal-title":"Biosystems"},{"issue":"2","key":"132_CR10","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1101\/gr.6991408","volume":"18","author":"T Abeel","year":"2008","unstructured":"Abeel T, Saeys Y, Bonnet E, Rouze P, Van de Peer Y: Generic eukaryotic core promoter prediction using structural features of DNA. Genome Res. 2008, 18 (2): 310-323. 10.1101\/gr.6991408","journal-title":"Genome Res"},{"issue":"16","key":"132_CR11","doi-asserted-by":"publisher","first-page":"2006","DOI":"10.1093\/bioinformatics\/btp359","volume":"25","author":"Y Gan","year":"2009","unstructured":"Gan Y, Guan J, Zhou S: A pattern-based nearest neighbor search approach for promoter prediction using DNA structural profiles. Bioinformatics. 2009, 25 (16): 2006-2012. 10.1093\/bioinformatics\/btp359","journal-title":"Bioinformatics"},{"key":"132_CR12","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1186\/1471-2105-9-414","volume":"9","author":"F Anwar","year":"2008","unstructured":"Anwar F, Baker SM, Jabid T, Mehedi Hasan M, Shoyaib M, Khan H, Walshe R: Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach. BMC Bioinformatics. 2008, 9: 414- 10.1186\/1471-2105-9-414","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"132_CR13","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/1471-2164-6-25","volume":"6","author":"C Molina","year":"2005","unstructured":"Molina C, Grotewold E: Genome wide analysis of Arabidopsis core promoters. BMC Genomics. 2005, 6 (1): 25- 10.1186\/1471-2164-6-25","journal-title":"BMC Genomics"},{"issue":"12","key":"132_CR14","doi-asserted-by":"publisher","first-page":"i313","DOI":"10.1093\/bioinformatics\/btp191","volume":"25","author":"T Abeel","year":"2009","unstructured":"Abeel T, Van de Peer Y, Saeys Y: Toward a gold standard for promoter prediction evaluation. Bioinformatics. 2009, 25 (12): i313-i320. 10.1093\/bioinformatics\/btp191","journal-title":"Bioinformatics"},{"issue":"38","key":"132_CR15","doi-asserted-by":"publisher","first-page":"14377","DOI":"10.1073\/pnas.0807988105","volume":"105","author":"AP Kornev","year":"2008","unstructured":"Kornev AP, Taylor SS, Ten Eyck LF: A helix scaffold for the assembly of active protein kinases. Proc Natl Acad Sci USA. 2008, 105 (38): 14377-14382. 10.1073\/pnas.0807988105","journal-title":"Proc Natl Acad Sci USA"},{"issue":"1","key":"132_CR16","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.bbapap.2007.11.002","volume":"1784","author":"LF Ten Eyck","year":"2008","unstructured":"Ten Eyck LF, Taylor SS, Kornev AP: Conserved spatial patterns across the protein kinase family. Biochim Biophys Acta. 2008, 1784 (1): 238-243.","journal-title":"Biochim Biophys Acta"},{"issue":"4","key":"132_CR17","doi-asserted-by":"crossref","first-page":"471","DOI":"10.3233\/ISB-00110","volume":"3","author":"AN Gorban","year":"2003","unstructured":"Gorban AN, Zinovyev AY, Popova TG: Seven clusters in genomic triplet distributions. In Silico Biol. 2003, 3 (4): 471-482.","journal-title":"In Silico Biol"},{"issue":"12","key":"132_CR18","doi-asserted-by":"publisher","first-page":"1827","DOI":"10.1101\/gr.606402","volume":"12","author":"J Majewski","year":"2002","unstructured":"Majewski J, Ott J: Distribution and characterization of regulatory elements in the human genome. Genome Res. 2002, 12 (12): 1827-1836. 10.1101\/gr.606402","journal-title":"Genome Res"},{"issue":"5","key":"132_CR19","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.ygeno.2006.12.009","volume":"89","author":"G Albrecht-Buehler","year":"2007","unstructured":"Albrecht-Buehler G: The three classes of triplet profiles of natural genomes. Genomics. 2007, 89 (5): 596-601. 10.1016\/j.ygeno.2006.12.009","journal-title":"Genomics"},{"issue":"1","key":"132_CR20","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1093\/nar\/gkg041","volume":"31","author":"IA Shahmuradov","year":"2003","unstructured":"Shahmuradov IA, Gammerman AJ, Hancock JM, Bramley PM, Solovyev VV: PlantProm: a database of plant promoter sequences. Nucleic Acids Res. 2003, 31 (1): 114-117. 10.1093\/nar\/gkg041","journal-title":"Nucleic Acids Res"},{"issue":"Database","key":"132_CR21","doi-asserted-by":"publisher","first-page":"D354","DOI":"10.1093\/nar\/gkh046","volume":"32","author":"Q Dong","year":"2004","unstructured":"Dong Q, Schlueter SD, Brendel V: PlantGDB, plant genome database and analysis tools. Nucleic Acids Res. 2004, 32 (Database): D354-359.","journal-title":"Nucleic Acids Res"},{"issue":"Database","key":"132_CR22","doi-asserted-by":"publisher","first-page":"D154","DOI":"10.1093\/nar\/gkm952","volume":"36","author":"S Griffiths-Jones","year":"2008","unstructured":"Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, 36 (Database): D154-158.","journal-title":"Nucleic Acids Res"},{"key":"132_CR23","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: 1992","author":"BE Boser","year":"1992","unstructured":"Boser BE, Guyon IM, Vapnik VN: A Training Algorithm for Optimal Margin Classifiers. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory: 1992. 1992, 144-152. Pittsburgh: ACM press"},{"issue":"1","key":"132_CR24","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. Comput Chem. 2001, 26 (1): 51-56. 10.1016\/S0097-8485(01)00099-7","journal-title":"Comput Chem"},{"issue":"5","key":"132_CR25","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1006\/jmbi.1995.0349","volume":"249","author":"DS Prestridge","year":"1995","unstructured":"Prestridge DS: Predicting Pol II promoter sequences using transcription factor binding sites. J Mol Biol. 1995, 249 (5): 923-932. 10.1006\/jmbi.1995.0349","journal-title":"J Mol Biol"},{"issue":"9","key":"132_CR26","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1093\/bioinformatics\/btp033","volume":"25","author":"AM Waterhouse","year":"2009","unstructured":"Waterhouse AM, Procter JB, Martin DMA, Clamp Ml, Barton GJ: Jalview Version 2 - a multiple sequence alignment editor and analysis workbench. Bioinformatics. 2009, 25 (9): 1189-1191. 10.1093\/bioinformatics\/btp033","journal-title":"Bioinformatics"},{"issue":"1","key":"132_CR27","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1186\/1471-2164-12-108","volume":"12","author":"V Thakur","year":"2011","unstructured":"Thakur V, Wanchana S, Xu M, Bruskiewich R, Quick W, Mosig A, Zhu XG: Characterization of statistical features for plant microRNA prediction. BMC Genomics. 2011, 12 (1): 108- 10.1186\/1471-2164-12-108","journal-title":"BMC Genomics"},{"issue":"Suppl","key":"132_CR28","doi-asserted-by":"publisher","first-page":"S29","DOI":"10.1186\/1471-2105-10-S1-S29","volume":"10","author":"Q Wang","year":"2009","unstructured":"Wang Q, Wan L, Li D, Zhu L, Qian M, Deng M: Searching for bidirectional promoters in Arabidopsis thaliana. BMC Bioinformatics. 2009, 10 (Suppl): S29- 10.1186\/1471-2105-10-S1-S29","journal-title":"BMC Bioinformatics"}],"container-title":["Algorithms for Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1748-7188-6-19.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T20:38:10Z","timestamp":1741293490000},"score":1,"resource":{"primary":{"URL":"https:\/\/almob.biomedcentral.com\/articles\/10.1186\/1748-7188-6-19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,6,28]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2011,12]]}},"alternative-id":["132"],"URL":"https:\/\/doi.org\/10.1186\/1748-7188-6-19","relation":{},"ISSN":["1748-7188"],"issn-type":[{"type":"electronic","value":"1748-7188"}],"subject":[],"published":{"date-parts":[[2011,6,28]]},"assertion":[{"value":"20 January 2011","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2011","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2011","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"19"}}