Literature DB >> 17914227

Identification and annotation of promoter regions in microbial genome sequences on the basis of DNA stability.

Vetriselvi Rangannan1, Manju Bansal.   

Abstract

Analysis of various predicted structural properties of promoter regions in prokaryotic as well as eukaryotic genomes had earlier indicated that they have several common features,such as lower stability, higher curvature and less bendability, when compared with their neighboring regions. Based on the difference in stability between neighboring upstream and downstream regions in the vicinity of experimentally determined transcription start sites, a promoter prediction algorithm has been developed to identify prokaryotic promoter sequences in whole genomes. The average free energy (E) over known promoter sequences and the difference (D) between E and the average free energy over the entire genome (G)are used to search for promoters in the genomic sequences. Using these cutoff values to predict promoter regions across entire Escherichia coli genome,we achieved a reliability of 70% when the predicted promoters were cross verified against the 960 transcription start sites (TSSs) listed in the Ecocyc database. Annotation of the whole E.coli genome for promoter region could be carried out with 49% accuracy. The method is quite general and it can be used to annotate the promoter regions of other prokaryotic genomes.

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Year:  2007        PMID: 17914227     DOI: 10.1007/s12038-007-0085-1

Source DB:  PubMed          Journal:  J Biosci        ISSN: 0250-5991            Impact factor:   1.826


  23 in total

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Authors:  A G Pedersen; P Baldi; Y Chauvin; S Brunak
Journal:  Comput Chem       Date:  1999-06-15

Review 2.  Models for prediction and recognition of eukaryotic promoters.

Authors:  T Werner
Journal:  Mamm Genome       Date:  1999-02       Impact factor: 2.957

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Journal:  Comput Appl Biosci       Date:  1996-10

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Authors:  J W Fickett; A G Hatzigeorgiou
Journal:  Genome Res       Date:  1997-09       Impact factor: 9.043

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Authors:  H Margalit; B A Shapiro; R Nussinov; J Owens; R L Jernigan
Journal:  Biochemistry       Date:  1988-07-12       Impact factor: 3.162

6.  Predicting DNA duplex stability from the base sequence.

Authors:  K J Breslauer; R Frank; H Blöcker; L A Marky
Journal:  Proc Natl Acad Sci U S A       Date:  1986-06       Impact factor: 11.205

7.  Predicting Pol II promoter sequences using transcription factor binding sites.

Authors:  D S Prestridge
Journal:  J Mol Biol       Date:  1995-06-23       Impact factor: 5.469

8.  Promoter prediction and annotation of microbial genomes based on DNA sequence and structural responses to superhelical stress.

Authors:  Huiquan Wang; Craig J Benham
Journal:  BMC Bioinformatics       Date:  2006-05-05       Impact factor: 3.169

9.  EcoCyc: a comprehensive database resource for Escherichia coli.

Authors:  Ingrid M Keseler; Julio Collado-Vides; Socorro Gama-Castro; John Ingraham; Suzanne Paley; Ian T Paulsen; Martín Peralta-Gil; Peter D Karp
Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

10.  A novel method for prokaryotic promoter prediction based on DNA stability.

Authors:  Aditi Kanhere; Manju Bansal
Journal:  BMC Bioinformatics       Date:  2005-01-05       Impact factor: 3.169

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  13 in total

1.  iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.

Authors:  Hao Lin; En-Ze Deng; Hui Ding; Wei Chen; Kuo-Chen Chou
Journal:  Nucleic Acids Res       Date:  2014-10-31       Impact factor: 16.971

2.  DNA-energetics-based analyses suggest additional genes in prokaryotes.

Authors:  Garima Khandelwal; Jalaj Gupta; B Jayaram
Journal:  J Biosci       Date:  2012-07       Impact factor: 1.826

3.  Eukaryotic and prokaryotic promoter prediction using hybrid approach.

Authors:  Hao Lin; Qian-Zhong Li
Journal:  Theory Biosci       Date:  2010-11-03       Impact factor: 1.919

4.  DNA free energy-based promoter prediction and comparative analysis of Arabidopsis and rice genomes.

Authors:  Czuee Morey; Sushmita Mookherjee; Ganesan Rajasekaran; Manju Bansal
Journal:  Plant Physiol       Date:  2011-04-29       Impact factor: 8.340

5.  Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.

Authors:  Meng Zhang; Cangzhi Jia; Fuyi Li; Chen Li; Yan Zhu; Tatsuya Akutsu; Geoffrey I Webb; Quan Zou; Lachlan J M Coin; Jiangning Song
Journal:  Brief Bioinform       Date:  2022-03-10       Impact factor: 11.622

6.  Distinguishing between productive and abortive promoters using a random forest classifier in Mycoplasma pneumoniae.

Authors:  Verónica Lloréns-Rico; Maria Lluch-Senar; Luis Serrano
Journal:  Nucleic Acids Res       Date:  2015-03-16       Impact factor: 16.971

7.  Recognition of prokaryotic promoters based on a novel variable-window Z-curve method.

Authors:  Kai Song
Journal:  Nucleic Acids Res       Date:  2011-09-27       Impact factor: 16.971

8.  Assessing the effects of data selection and representation on the development of reliable E. coli sigma 70 promoter region predictors.

Authors:  Mostafa M Abbas; Mostafa M Mohie-Eldin; Yasser El-Manzalawy
Journal:  PLoS One       Date:  2015-03-24       Impact factor: 3.240

9.  IntergenicDB: a database for intergenic sequences.

Authors:  Daniel Luis Notari; Aurione Molin; Vanessa Davanzo; Douglas Picolotto; Helena Graziottin Ribeiro; Scheila de Avila E Silva
Journal:  Bioinformation       Date:  2014-06-30

10.  Composing a Tumor Specific Bacterial Promoter.

Authors:  Igor V Deyneko; Nadine Kasnitz; Sara Leschner; Siegfried Weiss
Journal:  PLoS One       Date:  2016-05-12       Impact factor: 3.240

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