{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:54:08Z","timestamp":1767920048371,"version":"3.49.0"},"reference-count":59,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2020,1,7]],"date-time":"2020-01-07T00:00:00Z","timestamp":1578355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Department of Biotechnology, Ministry of Science and Technology, Government of India"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Despite conservation in general architecture of promoters and protein\u2013DNA interaction interface of RNA polymerases among various prokaryotes, identification of promoter regions in the whole genome sequences remains a daunting challenge. The available tools for promoter prediction do not seem to address the problem satisfactorily, apparently because the biochemical nature of promoter signals is yet to be understood fully. Using 28 structural and 3 energetic parameters, we found that prokaryotic promoter regions have a unique structural and energy state, quite distinct from that of coding regions and the information for this signature state is in-built in their sequences. We developed a novel promoter prediction tool from these 31 parameters using various statistical techniques.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here, we introduce SEProm, a novel tool that is developed by studying and utilizing the in-built structural and energy information of DNA sequences, which is applicable to all prokaryotes including archaea. Compared to five most recent, diverged and current best available tools, SEProm performs much better, predicting promoters with an \u2018F-value\u2019 of 82.04 and \u2018Precision\u2019 of 81.08. The next best \u2018F-value\u2019 was obtained with PromPredict (72.14) followed by BProm (68.37). On the basis of \u2018Precision\u2019 value, the next best \u2018Precision\u2019 was observed for Pepper (75.39) followed by PromPredict (72.01). SEProm maintained the lead even when comparison was done on two test organisms (not involved in training for SEProm).<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The software is freely available with easy to follow instructions (www.scfbio-iitd.res.in\/software\/TSS_Predict.jsp).<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz941","type":"journal-article","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T12:09:44Z","timestamp":1577966984000},"page":"2375-2384","source":"Crossref","is-referenced-by-count":22,"title":["A novel method<i>SEProm<\/i>for prokaryotic promoter prediction based on DNA structure and energetics"],"prefix":"10.1093","volume":"36","author":[{"given":"Akhilesh","family":"Mishra","sequence":"first","affiliation":[{"name":"Supercomputing Facility for Bioinformatics & Computational Biology"},{"name":"Kusuma School of Biological Sciences, Indian Institute of Technology , New Delhi 110016, India"}]},{"given":"Sahil","family":"Dhanda","sequence":"additional","affiliation":[{"name":"Supercomputing Facility for Bioinformatics & Computational Biology"}]},{"given":"Priyanka","family":"Siwach","sequence":"additional","affiliation":[{"name":"Supercomputing Facility for Bioinformatics & Computational Biology"},{"name":"Department of Biotechnology, Chaudhary Devi Lal University , Sirsa 125055, India"}]},{"given":"Shruti","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"Supercomputing Facility for Bioinformatics & Computational Biology"}]},{"given":"B","family":"Jayaram","sequence":"additional","affiliation":[{"name":"Supercomputing Facility for Bioinformatics & Computational Biology"},{"name":"Kusuma School of Biological Sciences, Indian Institute of Technology , New Delhi 110016, India"},{"name":"Department of Chemistry, Indian Institute of Technology , New Delhi 110016, India"}]}],"member":"286","published-online":{"date-parts":[[2020,1,7]]},"reference":[{"key":"2023013110170843500_btz941-B1","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1101\/gr.6991408","article-title":"Generic eukaryotic core promoter prediction using structural features of DNA","volume":"18","author":"Abeel","year":"2008","journal-title":"Genome Res"},{"key":"2023013110170843500_btz941-B2","doi-asserted-by":"crossref","first-page":"R98","DOI":"10.1186\/gb-2011-12-10-r98","article-title":"The transcriptional landscape of Chlamydia pneumoniae","volume":"12","author":"Albrecht","year":"2011","journal-title":"Genome Biol"},{"key":"2023013110170843500_btz941-B3","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1186\/s12864-016-2920-y","article-title":"Genome-wide identification of transcriptional start sites in the halo archaeon Haloferax volcanii based on differential RNA-Seq (dRNA-Seq)","volume":"17","author":"Babski","year":"2016","journal-title":"BMC Genomics"},{"key":"2023013110170843500_btz941-B4","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1529\/biophysj.104.045252","article-title":"Molecular dynamics simulations of the 136 unique tetranucleotide sequences of DNA oligonucleotides-I. 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