Operon Predictions Research Articles

Integrated Omics approach for Prediction of Operons like gene clusters in plants: Tools, Techniques, and Future aspects

Organized expression of genes within the genomes of microbes is a well-established concept under the name of operons. Similarly, the recent developments in the field of genetics and biochemistry has exposed operon-like genetic arrangements called Biosynthetic gene clusters (BGCs) in plants that has revolutionized the way we approach applied plant genetics for human use. Plant Gene clusters contain signature and tailoring genes. Signature genes are responsible for forming the backbone of the structure of the molecule. Tailoring genes are the group of genes that support these gene clusters to perform their functions. Recent genetics and chemical studies have shined light on an interesting aspect of plant metabolism, which is the accumulation of genes i.e. gene clusters involved in unique metabolic pathways in plants. Further utilizing the advanced genetic engineering tools provide opportunity to modify the plant genome at gene level for production of beneficial products for humans. In this review we have looked at the background, mechanism, discovery, significance, general methodology and techniques and current and future prospects of Biosynthetic gene clusters BGCs. Also, we shall be looking at some of the tools (examples like Blast and PlantiSmash) having applications in studying these gene clusters, their properties and their functions.

Diel gene expression improves software prediction of cyanobacterial operons.

Cyanobacteria are important participants in global biogeochemical process, but their metabolic processes and genomic functions are incompletely understood. In particular, operon structure, which can provide valuable metabolic and genomic insight, is difficult to determine experimentally, and algorithmic operon predictions probably underestimate actual operon extent. A software method is presented for enhancing current operon predictions by incorporating information from whole-genome time-series expression studies, using a Machine Learning classifier. Results are presented for the marine cyanobacterium Crocosphaera watsonii. A total of 15 operon enhancements are proposed. The source code is publicly available.

SLIDR and SLOPPR: flexible identification of spliced leader trans-splicing and prediction of eukaryotic operons from RNA-Seq data

BackgroundSpliced leader (SL) trans-splicing replaces the 5′ end of pre-mRNAs with the spliced leader, an exon derived from a specialised non-coding RNA originating from elsewhere in the genome. This process is essential for resolving polycistronic pre-mRNAs produced by eukaryotic operons into monocistronic transcripts. SL trans-splicing and operons may have independently evolved multiple times throughout Eukarya, yet our understanding of these phenomena is limited to only a few well-characterised organisms, most notably C. elegans and trypanosomes. The primary barrier to systematic discovery and characterisation of SL trans-splicing and operons is the lack of computational tools for exploiting the surge of transcriptomic and genomic resources for a wide range of eukaryotes.ResultsHere we present two novel pipelines that automate the discovery of SLs and the prediction of operons in eukaryotic genomes from RNA-Seq data. SLIDR assembles putative SLs from 5′ read tails present after read alignment to a reference genome or transcriptome, which are then verified by interrogating corresponding SL RNA genes for sequence motifs expected in bona fide SL RNA molecules. SLOPPR identifies RNA-Seq reads that contain a given 5′ SL sequence, quantifies genome-wide SL trans-splicing events and predicts operons via distinct patterns of SL trans-splicing events across adjacent genes. We tested both pipelines with organisms known to carry out SL trans-splicing and organise their genes into operons, and demonstrate that (1) SLIDR correctly detects expected SLs and often discovers novel SL variants; (2) SLOPPR correctly identifies functionally specialised SLs, correctly predicts known operons and detects plausible novel operons.ConclusionsSLIDR and SLOPPR are flexible tools that will accelerate research into the evolutionary dynamics of SL trans-splicing and operons throughout Eukarya and improve gene discovery and annotation for a wide range of eukaryotic genomes. Both pipelines are implemented in Bash and R and are built upon readily available software commonly installed on most bioinformatics servers. Biological insight can be gleaned even from sparse, low-coverage datasets, implying that an untapped wealth of information can be retrieved from existing RNA-Seq datasets as well as from novel full-isoform sequencing protocols as they become more widely available.

In Silico Prediction and Analysis of Unusual Lantibiotic Resistance Operons in the Genus Corynebacterium.

Post-translationally modified, (methyl-)lanthionine-containing peptides are produced by several Gram-positive bacteria. These so-called lantibiotics have potent activity against various bacterial pathogens including multidrug-resistant strains and are thus discussed as alternatives to antibiotics. Several naturally occurring mechanisms of resistance against lantibiotics have been described for bacteria, including cell envelope modifications, ABC-transporters, lipoproteins and peptidases. Corynebacterium species are widespread in nature and comprise important pathogens, commensals as well as environmentally and biotechnologically relevant species. Yet, little is known about lantibiotic biosynthesis and resistance in this genus. Here, we present a comprehensive in silico prediction of lantibiotic resistance traits in this important group of Gram-positive bacteria. Our analyses suggest that enzymes for cell envelope modification, peptidases as well as ABC-transporters involved in peptide resistance are widely distributed in the genus. Based on our predictions, we analyzed the susceptibility of six Corynebacterium species to nisin and found that those without dedicated resistance traits are more susceptible and unable to adapt to higher concentrations. In addition, we were able to identify lantibiotic resistance operons encoding for peptidases, ABC-transporters and two-component systems with an unusual predicted structure that are conserved in the genus Corynebacterium. Heterologous expression shows that these operons indeed confer resistance to the lantibiotic nisin.

Detecting operons in bacterial genomes via visual representation learning

Contiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts.

Prediction and analysis of metagenomic operons via MetaRon: a pipeline for prediction of Metagenome and whole-genome opeRons

BackgroundEfficient regulation of bacterial genes in response to the environmental stimulus results in unique gene clusters known as operons. Lack of complete operonic reference and functional information makes the prediction of metagenomic operons a challenging task; thus, opening new perspectives on the interpretation of the host-microbe interactions.ResultsIn this work, we identified whole-genome and metagenomic operons via MetaRon (Metagenome and whole-genome opeRon prediction pipeline). MetaRon identifies operons without any experimental or functional information. MetaRon was implemented on datasets with different levels of complexity and information. Starting from its application on whole-genome to simulated mixture of three whole-genomes (E. coli MG1655, Mycobacterium tuberculosis H37Rv and Bacillus subtilis str. 16), E. coli c20 draft genome extracted from chicken gut and finally on 145 whole-metagenome data samples from human gut. MetaRon consistently achieved high operon prediction sensitivity, specificity and accuracy across E. coli whole-genome (97.8, 94.1 and 92.4%), simulated genome (93.7, 75.5 and 88.1%) and E. coli c20 (87, 91 and 88%,), respectively. Finally, we identified 1,232,407 unique operons from 145 paired-end human gut metagenome samples. We also report strong association of type 2 diabetes with Maltose phosphorylase (K00691), 3-deoxy-D-glycero-D-galacto-nononate 9-phosphate synthase (K21279) and an uncharacterized protein (K07101).ConclusionWith MetaRon, we were able to remove two notable limitations of existing whole-genome operon prediction methods: (1) generalizability (ability to predict operons in unrelated bacterial genomes), and (2) whole-genome and metagenomic data management. We also demonstrate the use of operons as a subset to represent the trends of secondary metabolites in whole-metagenome data and the role of secondary metabolites in the occurrence of disease condition. Using operonic data from metagenome to study secondary metabolic trends will significantly reduce the data volume to more precise data. Furthermore, the identification of metabolic pathways associated with the occurrence of type 2 diabetes (T2D) also presents another dimension of analyzing the human gut metagenome. Presumably, this study is the first organized effort to predict metagenomic operons and perform a detailed analysis in association with a disease, in this case type 2 diabetes. The application of MetaRon to metagenomic data at diverse scale will be beneficial to understand the gene regulation and therapeutic metagenomics.

Flexible comparative genomics of prokaryotic transcriptional regulatory networks

BackgroundComparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed.ResultsHere we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota.ConclusionsOur results demonstrate the applicability of the CGB pipeline in multiple settings. CGB’s ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks.

Prediction and large-scale analysis of primary operons in plastids reveals unique genetic features in the evolution of chloroplasts.

While bacterial operons have been thoroughly studied, few analyses of chloroplast operons exist, limiting the ability to study fundamental elements of these structures and utilize them for synthetic biology. Here, we describe the creation of a plastome-specific operon database (link provided below) achieved by combining experimental tools and predictive modeling. Using a Reverse-Transcription-PCR based method and published data, we determined the transcription-state of 213 gene pairs from four plastomes of evolutionary distinct organisms. By analyzing sequence-based features computed for our dataset, we were able to highlight fundamental characteristics differentiating between operon pairs and non-operon pairs. These include an interesting tendency toward maintaining similar messenger RNA-folding profiles in operon gene pairs, a feature that failed to yield any informative separation in cyanobacteria, suggesting that it catches unique traits of operon gene expression, which have evolved post-endosymbiosis. Subsequently, we used this feature set to train a random-forest classifier for operon prediction. As our results demonstrate the ability of our predictor to obtain accurate (84%) and robust predictions on unlabeled datasets, we proceeded to building operon maps for 2018 sequenced plastids. Our database may now present new opportunities for promoting metabolic engineering and synthetic biology in chloroplasts.

Operon-mapper: a web server for precise operon identification in bacterial and archaeal genomes.

SummaryOperon-mapper is a web server that accurately, easily and directly predicts the operons of any bacterial or archaeal genome sequence. The operon predictions are based on the intergenic distance of neighboring genes as well as the functional relationships of their protein-coding products. To this end, Operon-mapper finds all the ORFs within a given nucleotide sequence, along with their genomic coordinates, orthology groups and functional relationships. We believe that Operon-mapper, due to its accuracy, simplicity and speed, as well as the relevant information that it generates, will be a useful tool for annotating and characterizing genomic sequences.Availability and implementation http://biocomputo.ibt.unam.mx/operon_mapper/

Contribution of bacterial promoter elements to transcription start site detection accuracy.

Accurately detecting transcription start sites (TSS) is a starting point for understanding gene transcription, and an important ingredient in a number of applications necessary for functional gene annotation, such as gene and operon predictions. Available methods for TSS detection in bacteria use very different description of the bacterial promoter structure and all of them show low accuracy. It is therefore unclear which promoter features should be included in TSS recognition, and how their accuracy impacts the search detection. We here address this question for [Formula: see text] and [Formula: see text] (an alternative [Formula: see text] factor) promoters in E. coli. We find that [Formula: see text]35 element, which is considered exchangeable, and is often not included in TSS search, contributes to the search accuracy equally (for [Formula: see text], or more (for [Formula: see text] than the ubiquitous [Formula: see text]10 element. Surprisingly, the sequence of the spacer between [Formula: see text]35 and [Formula: see text]10 promoter elements, which is commonly included in TSS detection, significantly decreases the search accuracy for [Formula: see text] promoters. However, the spacer sequence improves the search accuracy for [Formula: see text] promoters, which we attribute to a presence of sequence conservation. Overall, there is as much as [Formula: see text]50% false positive reduction for optimally implemented promoter features in [Formula: see text], underlying necessity for accurate promoter element alignments.

Computational operon prediction in whole-genomes and metagenomes.

Microbial diversity in unique environmental settings enables abrupt responses catalysed by altering the gene regulation and formation of gene clusters called operons. Operons increases bacterial adaptability, which in turn increases their survival. This review article presents the emergence of computational operon prediction methods for whole microbial genomes and metagenomes, and discusses their strengths and limitations. Most of the whole-genome operon prediction methods struggle to generalize on unrelated genomes. The applicability of universal whole-genome operon prediction methods to metagenomic data is an interesting yet less investigated question. We have evaluated the potential of various operon prediction features for genomic and metagenomic data. Most of operon prediction methods with high accuracy have been compiled into databases. Despite of the high predictive performance, the data among many databases are not completely consistent for similar species. We performed a correlation analysis between the computationally predicted operon databases and experimentally validated data for Escherichia coli, Bacillus subtilis and Mycobacterium tuberculosis. Operon prediction for most of the less characterized microbes cannot be verified due to absence of experimentally validated operons. The generation of validated information for other microbes would test the authenticity of operon databases for other less annotated microbes as well. Advances in sequencing technologies and development of better analysis methods will help researchers to overcome the technological hurdles (such as long sequencing reads and improved contig size) and further improve operon predictions and better utilize operonic information.

CONDOP: an R package for CONdition-Dependent Operon Predictions.

CONDOP is implemented in R and is freely available at CRAN. vittorio.fortino@helsinki.fiSupplementary information: Supplementary data are available at Bioinformatics online.

REMap: Operon map of M. tuberculosis based on RNA sequence data

A map of the transcriptional organization of genes of an organism is a basic tool that is necessary to understand and facilitate a more accurate genetic manipulation of the organism. Operon maps are largely generated by computational prediction programs that rely on gene conservation and genome architecture and may not be physiologically relevant. With the widespread use of RNA sequencing (RNAseq), the prediction of operons based on actual transcriptome sequencing rather than computational genomics alone is much needed. Here, we report a validated operon map of Mycobacterium tuberculosis, developed using RNAseq data from both the exponential and stationary phases of growth. At least 58.4% of M.tuberculosis genes are organized into 749 operons. Our prediction algorithm, REMap (RNA Expression Mapping of operons), considers the many cases of transcription coverage of intergenic regions, and avoids dependencies on functional annotation and arbitrary assumptions about gene structure. As a result, we demonstrate that REMap is able to more accurately predict operons, especially those that contain long intergenic regions or functionally unrelated genes, than previous operon prediction programs. The REMap algorithm is publicly available as a user-friendly tool that can be readily modified to predict operons in other bacteria.

An analysis of the validity and utility of the proximon proposition.

The utilization of metagenomic functional interactions represents a key technique for metagenomic functional annotation efforts. By definition, metagenomic operons represent such interactions, but many operon predictions protocols rely on information about orthology and/or gene function that is frequently unavailable for metagenomic genes. Recently, the concept of the metagenomic proximon was proposed for use in metagenomic scenarios where supplemental information is sparse. In this paper, we examine the validity and utility of the proximon proposition by measuring the extent to which proximons emulate actual operons. Using the Escherichia coli K-12 genome, we compare proximons and operons from the same genome and observe the configurations and cardinalities among their corresponding mappings. The results demonstrate that the vast majority of proximons map discretely to a single operon in a conservative fashion where a typical proximon is synonymous to an equivalent or truncated operon. However, a large proportion of operons had no corresponding mappings to any proximon. Various perspectives of operon and proximon intersection are discussed, along with the potential limitations for proximon detection and usage.

Operon prediction by Markov clustering.

The prediction of operons is a critical step for the reconstruction of biochemical and regulatory networks at the whole genome level. In this paper, a novel operon prediction model is proposed based on Markov Clustering (MCL). The model employs a graph-clustering method by MCL for prediction and does not need a classifier. In the cross-species validation, the accuracies of E. coli K12, Bacillus subtilis and P. furiosus are 92.1, 86.9 and 87.3%, respectively. Experimental results show that the proposed method has a powerful capability of operon prediction. The compiled program and test data sets are publicly available at http://ccst.jlu.edu.cn/JCSB/OPMC/.

Structure of Neisseria meningitidis lipoprotein GNA1162.

GNA1162, a predicted lipoprotein from Neisseria meningitidis, is a potential candidate for a universal vaccine against meningococcal disease caused by N. meningitidis serogroup B. Here, the crystal structure of GNA1162 at 1.89 Å resolution determined by single-wavelength anomalous dispersion (SAD) is reported. The structure of GNA1162 appears to be a dimer in the crystallographic asymmetric unit as well as in solution. The overall structure of the dimer indicates that each monomer inserts its C-terminal α5 helix into the hydrophobic groove of the other molecule. Moreover, the β4 strands of each monomer lie antiparallel to each other and interact through multiple main-chain hydrogen bonds. Through structural comparisons and operon predictions, it is hypothesized that GNA1162 is part of a transport system and assists in transport and reassembly. The crystal structure of GNA1162 sheds light on its possible function and provides potentially valuable information for the design of a vaccine against meningococcal disease.

Metagenomic Annotation Networks: Construction and Applications

The derivation and comparison of biological interaction networks are vital for understanding the functional capacity and hierarchical organization of integrated microbial communities. In the current work we present metagenomic annotation networks as a novel taxonomy-free approach for understanding the functional architecture of metagenomes. Specifically, metagenomic operon predictions are exploited to derive functional interactions that are translated and categorized according to their associated functional annotations. The result is a collection of discrete networks of weighted annotation linkages. These networks are subsequently examined for the occurrence of annotation modules that portray the functional and organizational characteristics of various microbial communities. A variety of network perspectives and annotation categories are applied to recover a diverse range of modules with different degrees of annotative cohesiveness. Applications to biocatalyst discovery and human health issues are discussed, as well as the limitations of the current implementation.

Features for computational operon prediction in prokaryotes

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.

Activation of Archaeal Transcription Mediated by Recruitment of Transcription Factor B

Archaeal promoters consist of a TATA box and a purine-rich adjacent upstream sequence (transcription factor B (TFB)-responsive element (BRE)), which are bound by the transcription factors TATA box-binding protein (TBP) and TFB. Currently, only a few activators of archaeal transcription have been experimentally characterized. The best studied activator, Ptr2, mediates activation by recruitment of TBP. Here, we present a detailed biochemical analysis of an archaeal transcriptional activator, PF1088, which was identified in Pyrococcus furiosus by a bioinformatic approach. Operon predictions suggested that an upstream gene, pf1089, is polycistronically transcribed with pf1088. We demonstrate that PF1088 stimulates in vitro transcription by up to 7-fold when the pf1089 promoter is used as a template. By DNase I and hydroxyl radical footprinting experiments, we show that the binding site of PF1088 is located directly upstream of the BRE of pf1089. Mutational analysis indicated that activation requires the presence of the binding site for PF1088. Furthermore, we show that activation of transcription by PF1088 is dependent upon the presence of an imperfect BRE and is abolished when the pf1089 BRE is replaced with a BRE from a strong archaeal promoter. Gel shift experiments showed that TFB recruitment to the pf1089 operon is stimulated by PF1088, and TFB seems to stabilize PF1088 operator binding even in the absence of TBP. Taken together, these results represent the first biochemical evidence for a transcriptional activator working as a TFB recruitment factor in Archaea, for which the designation TFB-RF1 is suggested.

ProOpDB: Prokaryotic Operon DataBase

The Prokaryotic Operon DataBase (ProOpDB, http://operons.ibt.unam.mx/OperonPredictor) constitutes one of the most precise and complete repositories of operon predictions now available. Using our novel and highly accurate operon identification algorithm, we have predicted the operon structures of more than 1200 prokaryotic genomes. ProOpDB offers diverse alternatives by which a set of operon predictions can be retrieved including: (i) organism name, (ii) metabolic pathways, as defined by the KEGG database, (iii) gene orthology, as defined by the COG database, (iv) conserved protein domains, as defined by the Pfam database, (v) reference gene and (vi) reference operon, among others. In order to limit the operon output to non-redundant organisms, ProOpDB offers an efficient method to select the most representative organisms based on a precompiled phylogenetic distances matrix. In addition, the ProOpDB operon predictions are used directly as the input data of our Gene Context Tool to visualize their genomic context and retrieve the sequence of their corresponding 5′ regulatory regions, as well as the nucleotide or amino acid sequences of their genes.