Features for computational operon prediction in prokaryotes
LI-Yeh Chuang2012, Briefings in functional genomics
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Abstract
Accurate prediction of operons can improve the functional annotation and application of genes within operons in prokaryotes. Here, we review several features: (i) intergenic distance, (ii) metabolic pathways, (iii) homologous genes, (iv) promoters and terminators, (v) gene order conservation, (vi) microarray, (vii) clusters of orthologous groups, (viii) gene length ratio, (ix) phylogenetic profiles, (x) operon length/size and (xi) STRING database scores, as well as some other features, which have been applied in recent operon prediction methods in prokaryotes in the literature. Based on a comparison of the prediction performances of these features, we conclude that other, as yet undiscovered features, or feature selection with a receiver operating characteristic analysis before algorithm processing can improve operon prediction in prokaryotes.
Key takeaways
- Recently, various genomic features have been found to be associated with operon structures; these features have been used to predict operons.
- The intergenic distance between two adjacent genes within the same operon tends to be rather short, while the distance between two adjacent genes of different operons is relatively long [34].
- It showed high accuracies of 94.6% and 93.3% for the experimentally defined operons in E. coli and B. subtilis, respectively, suggesting that COG is a useful feature for operon prediction.
- As genes within one operon are co-transcribed in the same orientation, the length/size characteristic can be used to predict if genes in a genome are clustered into operons.
- The NN method currently provides the highest accuracy for operon prediction in the E. coli and B. subtilis genomes ( Table 2), suggesting that some newly identified features could be used for operon prediction.
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Operon Prediction in Bacterial Genomes
Lecture Notes in Computer Science, 2008
The Brazilian Symposium on Bioinformatics (BSB) 2008 was held at Santo André (São Paulo), Brazil, August 28-30, 2008. BSB 2008 was the third symposium in the BSB series, although BSB was preceded by the Brazilian Workshop on Bioinformatics (WOB). This previous event had three consecutive editions in 2002 (Gramado, Rio Grande do Sul), 2003 (Macaé, Rio de Janeiro), and 2004 (Brasília, Distrito Federal). The change from workshop to symposium reflects the increasing quality and interest behind this meeting.
A powerful non-homology method for the prediction of operons in prokaryotes
Bioinformatics, 2002
The prediction of the transcription unit organization of genomes is an important clue in the inference of functional relationships of genes, the interpretation and evaluation of transcriptome experiments, and the overall inference of the regulatory networks governing the expression of genes in response to the environment. Though several methods have been devised to predict operons, most need a high characterization of the genome analysed. Loglikelihoods derived from inter-genic distance distributions work surprisingly well to predict operons in Escherichia coli and are available for any genome as soon as the gene sets are predicted. Results: Here we provide evidence that the very same method is applicable to any prokaryotic genome. First, the method has the same efficiency when evaluated using a collection of experimentally known operons of Bacillus subtilis. Second, operons among most if not all prokaryotes seem to have the same tendencies to keep short distances between their genes, the most frequent distances being the overlaps of four and one base pairs. The universality of this structural feature allows us to predict the organization of transcription units in all prokaryotes. Third, predicted operons contain a higher proportion of genes with related phylogenetic profiles and conservation of adjacency than predicted borders of transcription units.
Operon Prediction for Sequenced Bacterial Genomes without Experimental Information
Applied and Environmental Microbiology, 2007
Various computational approaches have been proposed for operon prediction, but most algorithms rely on experimental or functional data that are only available for a small subset of sequenced genomes. In this study, we explored the possibility of using phylogenetic information to aid in operon prediction, and we constructed a Bayesian hidden Markov model that incorporates comparative genomic data with traditional predictors, such as intergenic distances. The prediction algorithm performs as well as the best previously reported method, with several significant advantages. It uses fewer data sources and so it is easier to implement, and the method is more broadly applicable than previous methods-it can be applied to essentially every gene in any sequenced bacterial genome. Furthermore, we show that near-optimal performance is easily reached with a generic set of comparative genomes and does not depend on a specific relationship between the subject genome and the comparative set. We applied the algorithm to the Bacillus anthracis genome and found that it successfully predicted all previously verified B. anthracis operons. To further test its performance, we chose a predicted operon (BA1489-92) containing several genes with little apparent functional relatedness and tested their cotranscriptional nature. Experimental evidence shows that these genes are cotranscribed, and the data have interesting implications for B. anthracis biology. Overall, our findings show that this algorithm is capable of highly sensitive and accurate operon prediction in a wide range of bacterial genomes and that these predictions can lead to the rapid discovery of new functional relationships among genes.
Transcriptome dynamics-based operon prediction in prokaryotes
BMC Bioinformatics, 2014
Streptomyces spp. produce a variety of valuable secondary metabolites, which are regulated in a spatio-temporal manner by a complex network of inter-connected gene products. Using a compilation of genome-scale temporal transcriptome data for the model organism, Streptomyces coelicolor, under different environmental and genetic perturbations, we have developed a supervised machine-learning method for operon prediction in this microorganism. We demonstrate that, using features dependent on transcriptome dynamics and genome sequence, a support vector machines (SVM)-based classification algorithm can accurately classify`90% of gene pairs in a set of known operons. Based on model predictions for the entire genome, we verified the co-transcription of more than 250 gene pairs by RT-PCR. These results vastly increase the database of known operons in S. coelicolor and provide valuable information for exploring gene function and regulation to harness the potential of this differentiating microorganism for synthesis of natural products.
Operons Across Prokaryotes: Genomic Analyses and Predictions 300+ Genomes Later
Current Genomics, 2006
With operon predictions based on conservation of gene order and 330 Prokaryotic genomes available, I am able to show that in most of the genomes analyzed about 70% of the genes in operons are separated by distances of -20 to 30 base pairs. Most of the differences in this tendency might be related to the appearance of signals inside the operons. However, a closer look at the extreme exceptions confirms that annotation problems are partly responsible for inter-genic distance distributions that deviate from that of operons in Escherichia coli K12. I also argue that the inter-genic distances of adjacent genes in different transcription units (transcription unit boundaries or TUBs) should be expected to be more variable than those for genes in the same operon. Using phylogenetic profiles I show that predictions adjusted on a per genome basis might help increase the accuracy of operon predictions. Improvements in automated annotation might be necessary to fully evaluate the overall tendencies of genes in operons towards short inter-genic distances, and to better understand differences in regulatory complexity across Prokaryotes.
Nebulon: a system for the inference of functional relationships of gene products from the rearrangement of predicted operons
Nucleic Acids Research, 2005
Since operons are unstable across Prokaryotes, it has been suggested that perhaps they re-combine in a conservative manner. Thus, genes belonging to a given operon in one genome might re-associate in other genomes revealing functional relationships among gene products. We developed a system to build networks of functional relationships of gene products based on their organization into operons in any available genome. The operon predictions are based on inter-genic distances. Our system can use different kinds of thresholds to accept a functional relationship, either related to the prediction of operons, or to the number of non-redundant genomes that support the associations. We also work by shells, meaning that we decide on the number of linking iterations to allow for the complementation of related gene sets. The method shows high reliability benchmarked against knowledge-bases of functional interactions. We also illustrate the use of Nebulon in finding new members of regulons, and of other functional groups of genes. Operon rearrangements produce thousands of high-quality new interactions per prokaryotic genome, and thousands of confirmations per genome to other predictions, making it another important tool for the inference of functional interactions from genomic context.
OperomeDB: A Database of Condition-Specific Transcription Units in Prokaryotic Genomes
BioMed Research International, 2015
Background. In prokaryotic organisms, a substantial fraction of adjacent genes are organized into operons—codirectionally organized genes in prokaryotic genomes with the presence of a common promoter and terminator. Although several available operon databases provide information with varying levels of reliability, very few resources provide experimentally supported results. Therefore, we believe that the biological community could benefit from having a new operon prediction database with operons predicted using next-generation RNA-seq datasets.Description. We present operomeDB, a database which provides an ensemble of all the predicted operons for bacterial genomes using available RNA-sequencing datasets across a wide range of experimental conditions. Although several studies have recently confirmed that prokaryotic operon structure is dynamic with significant alterations across environmental and experimental conditions, there are no comprehensive databases for studying such variati...
A Teaching-Learning-Based Optimization for Operon Prediction
Asian Journal of Engineering and Technology, 2014
Operons are the basic unit of transcription and can be used to understand the transcription regulation in a given prokaryotic genome. Currently, the sequence and gene coordinates of organisms can be rapidly identified, but their operons remain unknown. Moreover, the experimental methods detecting operons are extremely difficult and time-consuming to execute. Operon prediction as pretreatment can greatly reduce the cost of performing an experimental assay. Many algorithms and biological properties have been proposed but the resulting predictions still require improvement in terms of sensitivity, specificity, and accuracy. This study uses a teaching-learning-based optimization (TLBO) algorithm with three biological properties for operon prediction: the intergenic distance, the metabolic pathway, and the cluster of orthologous groups (COG). These properties for the Escherichia coli genome are used to train the evaluation standards of fitness function of gene pairs. The experimental res...
Transcriptome dynamics-based operon prediction and verification in Streptomyces coelicolor
2007
Streptomyces spp. produce a variety of valuable secondary metabolites, which are regulated in a spatio-temporal manner by a complex network of interconnected gene products. Using a compilation of genome-scale temporal transcriptome data for the model organism, Streptomyces coelicolor, under different environmental and genetic perturbations, we have developed a supervised machine-learning method for operon prediction in this microorganism. We demonstrate that, using features dependent on transcriptome dynamics and genome sequence, a support vector machines (SVM)-based classification algorithm can accurately classify`90% of gene pairs in a set of known operons. Based on model predictions for the entire genome, we verified the co-transcription of more than 250 gene pairs by RT-PCR. These results vastly increase the database of known operons in S. coelicolor and provide valuable information for exploring gene function and regulation to harness the potential of this differentiating microorganism for synthesis of natural products.
The distinctive signatures of promoter regions and operon junctions across prokaryotes
Nucleic Acids Research, 2006
Here we show that regions upstream of first transcribed genes have oligonucleotide signatures that distinguish them from regions upstream of genes in the middle of operons. Databases of experimentally confirmed transcription units do not exist for most genomes. Thus, to expand the analyses into genomes with no experimentally confirmed data, we used genes conserved adjacent in evolutionarily distant genomes as representatives of genes inside operons. Likewise, we used divergently transcribed genes as representative examples of first transcribed genes. In model organisms, the trinucleotide signatures of regions upstream of these representative genes allow for operon predictions with accuracies close to those obtained with known operon data (0.8). Signature-based operon predictions have more similar phylogenetic profiles and higher proportions of genes in the same pathways than predicted transcription unit boundaries (TUBs). These results confirm that we are separating genes with related functions, as expected for operons, from genes not necessarily related, as expected for genes in different transcription units. We also test the quality of the predictions using microarray data in six genomes and show that the signaturepredicted operons tend to have high correlations of expression. Oligonucleotide signatures should expand the number of tools available to identify operons even in poorly characterized genomes.
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An important step in understanding the regulation of a prokaryotic genome is the generation of its transcription unit map. The current strongest operon predictor depends on the distributions of intergenic distances (IGD) separating adjacent genes within and between operons. Unfortunately, experimental data on these distance distributions are limited to Escherichia coli and Bacillus subtilis. We suggest a new graph algorithmic approach based on comparative genomics to identify clusters of conserved genes independent of IGD and conservation of gene order. As a consequence, distance distributions of operon pairs for any arbitrary prokaryotic genome can be inferred. For E.coli, the algorithm predicts 854 conserved adjacent pairs with a precision of 85%. The IGD distribution for these pairs is virtually identical to the E.coli operon pair distribution. Statistical analysis of the predicted pair IGD distribution allows estimation of a genome-specific operon IGD cut-off, obviating the requirement for a training set in IGD-based operon prediction. We apply the method to a representative set of eight genomes, and show that these genome-specific IGD distributions differ considerably from each other and from the distribution in E.coli.
Detecting uber-operons in prokaryotic genomes
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We present a study on computational identification of uber-operons in a prokaryotic genome, each of which represents a group of operons that are evolutionarily or functionally associated through operons in other (reference) genomes. Uber-operons represent a rich set of footprints of operon evolution, whose full utilization could lead to new and more powerful tools for elucidation of biological pathways and networks than what operons have provided, and a better understanding of prokaryotic genome structures and evolution. Our prediction algorithm predicts uber-operons through identifying groups of functionally or transcriptionally related operons, whose gene sets are conserved across the target and multiple reference genomes. Using this algorithm, we have predicted uber-operons for each of a group of 91 genomes, using the other 90 genomes as references. In particular, we predicted 158 uberoperons in Escherichia coli K12 covering 1830 genes, and found that many of the uber-operons correspond to parts of known regulons or biological pathways or are involved in highly related biological processes based on their Gene Ontology (GO) assignments. For some of the predicted uberoperons that are not parts of known regulons or pathways, our analyses indicate that their genes are highly likely to work together in the same biological processes, suggesting the possibility of new regulons and pathways. We believe that our uberoperon prediction provides a highly useful capability and a rich information source for elucidation of complex biological processes, such as pathways in microbes. All the prediction results are available at our Uber-Operon Database: http://csbl.bmb.uga. edu/uber, the first of its kind.
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Proceedings / ... International Conference on Intelligent Systems for Molecular Biology ; ISMB. International Conference on Intelligent Systems for Molecular Biology, 2000
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High accuracy operon prediction method based on STRING database scores
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Operons in Escherichia coli: Genomic analyses and predictions
Proceedings of the National Academy of Sciences, 2000
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