Literature DB >> 19593472

Relative stability of DNA as a generic criterion for promoter prediction: whole genome annotation of microbial genomes with varying nucleotide base composition.

Vetriselvi Rangannan1, Manju Bansal.   

Abstract

The rapid increase in genome sequence information has necessitated the annotation of their functional elements, particularly those occurring in the non-coding regions, in the genomic context. Promoter region is the key regulatory region, which enables the gene to be transcribed or repressed, but it is difficult to determine experimentally. Hence an in silico identification of promoters is crucial in order to guide experimental work and to pin point the key region that controls the transcription initiation of a gene. In this analysis, we demonstrate that while the promoter regions are in general less stable than the flanking regions, their average free energy varies depending on the GC composition of the flanking genomic sequence. We have therefore obtained a set of free energy threshold values, for genomic DNA with varying GC content and used them as generic criteria for predicting promoter regions in several microbial genomes, using an in-house developed tool PromPredict. On applying it to predict promoter regions corresponding to the 1144 and 612 experimentally validated TSSs in E. coli (50.8% GC) and B. subtilis (43.5% GC) sensitivity of 99% and 95% and precision values of 58% and 60%, respectively, were achieved. For the limited data set of 81 TSSs available for M. tuberculosis (65.6% GC) a sensitivity of 100% and precision of 49% was obtained.

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Year:  2009        PMID: 19593472     DOI: 10.1039/B906535K

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  17 in total

1.  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

2.  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

3.  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

4.  Some novel features of strong promoters discovered in Cytophaga hutchinsonii.

Authors:  Guoqing Fan; Wenxia Song; Zhiwei Guan; Weican Zhang; Xuemei Lu
Journal:  Appl Microbiol Biotechnol       Date:  2022-03-23       Impact factor: 4.813

5.  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

6.  PromBase: a web resource for various genomic features and predicted promoters in prokaryotic genomes.

Authors:  Vetriselvi Rangannan; Manju Bansal
Journal:  BMC Res Notes       Date:  2011-07-22

7.  Promoter prediction in E. coli based on SIDD profiles and Artificial Neural Networks.

Authors:  Charles Bland; Abigail S Newsome; Aleksandra A Markovets
Journal:  BMC Bioinformatics       Date:  2010-10-07       Impact factor: 3.169

8.  Analysis of transcriptional regulation of the human miR-17-92 cluster; evidence for involvement of Pim-1.

Authors:  Maren Thomas; Kerstin Lange-Grünweller; Dorothee Hartmann; Lara Golde; Julia Schlereth; Dennis Streng; Achim Aigner; Arnold Grünweller; Roland K Hartmann
Journal:  Int J Mol Sci       Date:  2013-06-07       Impact factor: 5.923

9.  DNA structural properties in the classification of genomic transcription regulation elements.

Authors:  Pieter Meysman; Kathleen Marchal; Kristof Engelen
Journal:  Bioinform Biol Insights       Date:  2012-07-02

10.  Structural properties of prokaryotic promoter regions correlate with functional features.

Authors:  Pieter Meysman; Julio Collado-Vides; Enrique Morett; Roberto Viola; Kristof Engelen; Kris Laukens
Journal:  PLoS One       Date:  2014-02-07       Impact factor: 3.240

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