Nebulon: a system for the inference of functional relationships of gene products from the rearrangement of predicted operons

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.     

Prediction and experimental testing of ferric uptake regulator regulons in vibrios

Iron homeostasis is in many bacteria regulated by the ferric uptake regulator (Fur). Despite the available information on Fur regulons, it is likely that there are Fur-regulated genes and operons that are unique to vibrios, and knowledge into these can potentially provide new insights into vibrio virulence and pathogenesis. We constructed a vibrio-specific alignment matrix based on Fur-binding sites from the literature and used existing software (Patser) to search five published vibrio genomes and the Vibrio salmonicida draft genome for Fur-regulated genes. The consensus Fur-binding site from our matrix is 5'-AATGANAATNATTNTCATT-3'. Fur-binding motifs were found associated with 50-61 single genes and 16-20 operons in each genome. Predictions were tested by monitoring the expression of a subset of genes and operons in V. salmonicida. Six previously undescribed Fur-regulated genes showed increased expression under iron-restrictive conditions. Our work provides a comprehensive list of predicted Fur regulons in six vibrio genomes, which may be used to generate new hypotheses for future experiments.     

Predicting regulons and their cis-regulatory motifs by comparative genomics

We have combined and compared three techniques for predicting functional interactions based on comparative genomics (methods based on conserved operons, protein fusions and correlated evolution) and optimized these methods to predict coregulated sets of genes in 24 complete genomes, including Saccharomyces cerevisiae, Caernorhabditis elegans and 22 prokaryotes. The method based on conserved operons was the most useful for this purpose. Upstream regions of the genes comprising these predicted regulons were then used to search for regulatory motifs in 22 prokaryotic genomes using the motif-discovery program AlignACE. Many significant upstream motifs, including five known Escherichia coli regulatory motifs, were identified in this manner. The presence of a significant regulatory motif was used to refine the members of the predicted regulons to generate a final set of predicted regulons that share significant regulatory elements.     

Operon prediction using both genome-specific and general genomic information

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.     

Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes

Although cis-regulatory binding sites (CRBSs) are at least as important as the coding sequences in a genome, our general understanding of them in most sequenced genomes is very limited due to the lack of efficient and accurate experimental and computational methods for their characterization, which has largely hindered our understanding of many important biological processes. In this article, we describe a novel algorithm for genome-wide de novo prediction of CRBSs with high accuracy. We designed our algorithm to circumvent three identified difficulties for CRBS prediction using comparative genomics principles based on a new method for the selection of reference genomes, a new metric for measuring the similarity of CRBSs, and a new graph clustering procedure. When operon structures are correctly predicted, our algorithm can predict 81% of known individual binding sites belonging to 94% of known cis-regulatory motifs in the Escherichia coli K12 genome, while achieving high prediction specificity. Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, suggesting that it is very robust, and thus can be applied to any other sequenced prokaryotic genome. When compared with the prior state-of-the-art algorithms, our algorithm outperforms them in both prediction sensitivity and specificity.     

A hybrid gene team model and its application to genome analysis

It is well-known that functionally related genes occur in a physically clustered form, especially operons in bacteria. By leveraging on this fact, there has recently been an interesting problem formulation known as gene team model, which searches for a set of genes that co-occur in a pair of closely related genomes. However, many gene teams, even experimentally verified operons, frequently scatter within other genomes. Thus, the gene team model should be refined to reflect this observation. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraints. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. We also compared the result from our hybrid model to those from the traditional gene team model. We also show that predicted gene teams can be used for various genome analysis: operon prediction, phylogenetic analysis of organisms, contextual sequence analysis and genome annotation. Our program is fast enough to provide a service on the web at http://platcom.informatics.indiana.edu/platcom/. Users can select any combination of 97 genomes to predict gene teams.     

Expansion of the BioCyc collection of pathway/genome databases to 160 genomes

The BioCyc database collection is a set of 160 pathway/genome databases (PGDBs) for most eukaryotic and prokaryotic species whose genomes have been completely sequenced to date. Each PGDB in the BioCyc collection describes the genome and predicted metabolic network of a single organism, inferred from the MetaCyc database, which is a reference source on metabolic pathways from multiple organisms. In addition, each bacterial PGDB includes predicted operons for the corresponding species. The BioCyc collection provides a unique resource for computational systems biology, namely global and comparative analyses of genomes and metabolic networks, and a supplement to the BioCyc resource of curated PGDBs. The Omics viewer available through the BioCyc website allows scientists to visualize combinations of gene expression, proteomics and metabolomics data on the metabolic maps of these organisms. This paper discusses the computational methodology by which the BioCyc collection has been expanded, and presents an aggregate analysis of the collection that includes the range of number of pathways present in these organisms, and the most frequently observed pathways. We seek scientists to adopt and curate individual PGDBs within the BioCyc collection. Only by harnessing the expertise of many scientists we can hope to produce biological databases, which accurately reflect the depth and breadth of knowledge that the biomedical research community is producing.     

PPO: predictor for prokaryotic operons

SUMMARY: We present an operon predictor for prokaryotic operons (PPO), which can predict operons in the entire prokaryotic genome. The prediction algorithm used in PPO allows the user to select binary particle swarm optimization (BPSO), a genetic algorithm (GA) or some other methods introduced in the literature to predict operons. The operon predictor on our web server and the provided database are easy to access and use. The main features offered are: (i) selection of the prediction algorithm; (ii) adjustable parameter settings of the prediction algorithm; (iii) graphic visualization of results; (iv) integrated database queries; (v) listing of experimentally verified operons; and (vi) related tools. Availability and implementation: PPO is freely available at http://bio.kuas.edu.tw/PPO/.     

Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

We present a computational method for operon prediction based on a comparative genomics approach. A group of consecutive genes is considered as a candidate operon if both their gene sequences and functions are conserved across several phylogenetically related genomes. In addition, various supporting data for operons are also collected through the application of public domain computer programs, and used in our prediction method. These include the prediction of conserved gene functions, promoter motifs and terminators. An apparent advantage of our approach over other operon prediction methods is that it does not require many experimental data (such as gene expression data and pathway data) as input. This feature makes it applicable to many newly sequenced genomes that do not have extensive experimental information. In order to validate our prediction, we have tested the method on Escherichia coli K12, in which operon structures have been extensively studied, through a comparative analysis against Haemophilus influenzae Rd and Salmonella typhimurium LT2. Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp. WH8102, through a comparative genome analysis with two other cyanobacterial genomes, Prochlorococcus marinus sp. MED4 and P.marinus sp. MIT9313. Our results are consistent with previously reported results and statistics on operons in the literature.     

Conservation of ribosomal protein gene ordering in 16 complete genomes

The organization of ribosomal proteins in 16 prokaryotic genomes was studied as an example of comparative genome analyses of gene systems. Hypothetical ribosomal protein-containing operons were constructed. These operons also contained putative genes and other non-ribosomal genes. The correspondences among these genes across different organisms were clarified by sequence homology computations. In this way a cross tabulation of 70 ribosomal proteins genes was constructed. On average, these were organized into 9-14 operons in each genome. There were also 25 non-ribosomal or putative genes in these mainly ribosomal protein operons. Hence the table contains 95 genes in total. It was found that: (i) the conservation of the block of about 20 r-proteins in the L3 and L4 operons across almost the entire eubacteria and ar-chaebacteria is remarkable; (ii) some operons only belong to eubacteria or archaebacte-ria; (iii) although the ribosomal protein operons are highly conserved within domain, there are fine variat     

Conservation of ribosomal protein gene ordering in 16 complete genomes

The organization of ribosomal proteins in 16 prokaryotic genomes was studied as an example of comparative genome analyses of gene systems. Hypothetical ribosomal protein-containing operons were constructed. These operons also contained putative genes and other non-ribosomal genes. The correspondences among these genes across different organisms were clarified by sequence homology computations. In this way a cross tabulation of 70 ribosomal proteins genes was constructed. On average, these were organized into 9-14 operons in each genome. There were also 25 non-ribosomal or putative genes in these mainly ribosomal protein operons. Hence the table contains 95 genes in total. It was found that: (i) the conservation of the block of about 20 r-proteins in the L3 and L4 operons across almost the entire eubacteria and archaebacteria is remarkable; (ii) some operons only belong to eubacteria or archaebacteria; (iii) although the ribosomal protein operons are highly conserved within domain, there are fine variations in some operons across different organisms within each domain, and these variations are informative on the evolutionary relations among the organisms. This method provides a new potential for studying the origin and evolution of old species.     

Use of contiguity on the chromosome to predict functional coupling

The availability of a growing number of completely sequenced genomes opens new opportunities for understanding of complex biological systems. Success of genome-based biology will, to a large extent, depend on the development of new approaches and tools for efficient comparative analysis of the genomes and their organization. We have developed a technique for detecting possible functional coupling between genes based on detection of potential operons. The approach involves computation of "pairs of close bidirectional best hits", which are pairs of genes that apparently occur within operons in multiple genomes. Using these pairs, one can compose evidence (based on the number of distinct genomes and the phylogenetic distance between the orthologous pairs) that a pair of genes is potentially functionally coupled. The technique has revealed a surprisingly rich and apparently accurate set of functionally coupled genes. The approach depends on the use of a relatively large number of genomes, and the amount of detected coupling grows dramatically as the number of genomes increases.