ArticleNucleic acids research2007
Operon prediction using both genome-specific and general genomic information.
Article in Nucleic acids research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
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98 citing papers in PubMed.
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- Systematic analysis of the underlying genomic architecture for transcriptional-translational coupling in prokaryotes.NAR genomics and bioinformatics · 2022Article
- OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data.PLoS computational biology · 2022Article
- Detecting operons in bacterial genomes via visual representation learning.Scientific reports · 2021Article
- Prediction and analysis of metagenomic operons via MetaRon: a pipeline for prediction of Metagenome and whole-genome opeRons.BMC genomics · 2021Article
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- Response of the Biocontrol Agent Pseudomonas pseudoalcaligenes AVO110 to Rosellinia necatrix Exudate.Applied and environmental microbiology · 2019Article
- Prediction and characterization of promoters and ribosomal binding sites ofBiotechnology for biofuels · 2019Article
- The Transcriptional landscape of Streptococcus pneumoniae TIGR4 reveals a complex operon architecture and abundant riboregulation critical for growth and virulence.PLoS pathogens · 2018Article
- SLING: a tool to search for linked genes in bacterial datasets.Nucleic acids research · 2018Article
- Coherent Feedforward Regulation of Gene Expression by Caulobacter σJournal of bacteriology · 2018Article
- Genomic organization underlying deletional robustness in bacterial metabolic systems.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Comparison of Alicyclobacillus acidocaldarius o-Succinylbenzoate Synthase to Its Promiscuous N-Succinylamino Acid Racemase/ o-Succinylbenzoate Synthase Relatives.Biochemistry · 2018Article
38 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
We have carried out a systematic analysis of the contribution of a set of selected features that include three new features to the accuracy of operon prediction. Our analyses have led to a number of new insights about operon prediction, including that (i) different features have different levels of discerning power when used on adjacent gene pairs with different ranges of intergenic distance, (ii) certain features are universally useful for operon prediction while others are more genome-specific and (iii) the prediction reliability of operons is dependent on intergenic distances. Based on these new insights, our newly developed operon-prediction program achieves more accurate operon prediction than the previous ones, and it uses features that are most readily available from genomic sequences. Our prediction results indicate that our (non-linear) decision tree-based classifier can predict operons in a prokaryotic genome very accurately when a substantial number of operons in the genome are already known. For example, the prediction accuracy of our program can reach 90.2 and 93.7% on Bacillus subtilis and Escherichia coli genomes, respectively. When no such information is available, our (linear) logistic function-based classifier can reach the prediction accuracy at 84.6 and 83.3% for E.coli and B.subtilis, respectively.
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