Recognition of prokaryotic promoters based on a novel variable-window Z-curve method

Transcription is the first step in gene expression, and it is the step at which most of the regulation of expression occurs. Although sequenced prokaryotic genomes provide a wealth of information, transcriptional regulatory networks are still poorly understood using the available genomic information, largely because accurate prediction of promoters is difficult. To improve promoter recognition performance, a novel variable-window Z-curve method is developed to extract general features of prokaryotic promoters. The features are used for further classification by the partial least squares technique. To verify the prediction performance, the proposed method is applied to predict promoter fragments of two representative prokaryotic model organisms (Escherichia coli and Bacillus subtilis). Depending on the feature extraction and selection power of the proposed method, the promoter prediction accuracies are improved markedly over most existing approaches: for E. coli, the accuracies are 96.05% (σ70 promoters, coding negative samples), 90.44% (σ70 promoters, non-coding negative samples), 92.13% (known sigma-factor promoters, coding negative samples), 92.50% (known sigma-factor promoters, non-coding negative samples), respectively; for B. subtilis, the accuracies are 95.83% (known sigma-factor promoters, coding negative samples) and 99.09% (known sigma-factor promoters, non-coding negative samples). Additionally, being a linear technique, the computational simplicity of the proposed method makes it easy to run in a matter of minutes on ordinary personal computers or even laptops. More importantly, there is no need to optimize parameters, so it is very practical for predicting other species promoters without any prior knowledge or prior information of the statistical properties of the samples

Increased retention of functional fusions to toxic genes in new two-hybrid libraries of the E. coli strain MG1655 and B. subtilis strain 168 genomes, prepared without passaging through E. coli

BACKGROUND: Cloning of genes in expression libraries, such as the yeast two-hybrid system (Y2H), is based on the assumption that the loss of target genes is minimal, or at worst, managable. However, the expression of genes or gene fragments that are capable of interacting with E. coli or yeast gene products in these systems has been shown to be growth inhibitory, and therefore these clones are underrepresented (or completely lost) in the amplified library. RESULTS: Analysis of candidate genes as Y2H fusion constructs has shown that, while stable in E. coli and yeast for genetic studies, they are rapidly lost in growth conditions for genomic libraries. This includes the rapid loss of a fragment of the E. coli cell division gene ftsZ which encodes the binding site for ZipA and FtsA. Expression of this clone causes slower growth in E. coli. This clone is also rapidly lost in yeast, when expressed from a GAL1 promoter, relative to a vector control, but is stable when the promoter is repressed. We have demonstrated in this report that the construction of libraries for the E. coli and B. subtilis genomes without passaging through E. coli is practical, but the number of transformants is less than for libraries cloned using E. coli as a host. Analysis of several clones in the libraries that are strongly growth inhibitory in E. coli include genes for many essential cellular processes, such as transcription, translation, cell division, and transport. CONCLUSION: Expression of Y2H clones capable of interacting with E. coli and yeast targets are rapidly lost, causing a loss of complexity. The strategy for preparing Y2H libraries described here allows the retention of genes that are toxic when inappropriately expressed in E. coli, or yeast, including many genes that represent potential antibacterial targets. While these methods are generally applicable to the generation of Y2H libraries from any source, including mammalian and plant genomes, the potential of functional clones interacting with host proteins to inhibit growth would make this approach most relevant for the study of prokaryotic genomes

Hidden localization motifs: naturally occurring peroxisomal targeting signals in non-peroxisomal proteins

BACKGROUND: Can sequence segments coding for subcellular targeting or for posttranslational modifications occur in proteins that are not substrates in either of these processes? Although considerable effort has been invested in achieving low false-positive prediction rates, even accurate sequence-analysis tools for the recognition of these motifs generate a small but noticeable number of protein hits that lack the appropriate biological context but cannot be rationalized as false positives. RESULTS: We show that the carboxyl termini of a set of definitely non-peroxisomal proteins with predicted peroxisomal targeting signals interact with the peroxisomal matrix protein receptor peroxin 5 (PEX5) in a yeast two-hybrid test. Moreover, we show that examples of these proteins - chicken lysozyme, human tyrosinase and the yeast mitochondrial ribosomal protein L2 (encoded by MRP7) - are imported into peroxisomes in vivo if their original sorting signals are disguised. We also show that even prokaryotic proteins can contain peroxisomal targeting sequences. CONCLUSIONS: Thus, functional localization signals can evolve in unrelated protein sequences as a result of neutral mutations, and subcellular targeting is hierarchically organized, with signal accessibility playing a decisive role. The occurrence of silent functional motifs in unrelated proteins is important for the development of sequence-based function prediction tools and the interpretation of their results. Silent functional signals have the potential to acquire importance in future evolutionary scenarios and in pathological conditions

A putative RNA-interference-based immune system in prokaryotes: computational analysis of the predicted enzymatic machinery, functional analogies with eukaryotic RNAi, and hypothetical mechanisms of action

BACKGROUND: All archaeal and many bacterial genomes contain Clustered Regularly Interspaced Short Palindrome Repeats (CRISPR) and variable arrays of the CRISPR-associated (cas) genes that have been previously implicated in a novel form of DNA repair on the basis of comparative analysis of their protein product sequences. However, the proximity of CRISPR and cas genes strongly suggests that they have related functions which is hard to reconcile with the repair hypothesis. RESULTS: The protein sequences of the numerous cas gene products were classified into ~25 distinct protein families; several new functional and structural predictions are described. Comparative-genomic analysis of CRISPR and cas genes leads to the hypothesis that the CRISPR-Cas system (CASS) is a mechanism of defense against invading phages and plasmids that functions analogously to the eukaryotic RNA interference (RNAi) systems. Specific functional analogies are drawn between several components of CASS and proteins involved in eukaryotic RNAi, including the double-stranded RNA-specific helicase-nuclease (dicer), the endonuclease cleaving target mRNAs (slicer), and the RNA-dependent RNA polymerase. However, none of the CASS components is orthologous to its apparent eukaryotic functional counterpart. It is proposed that unique inserts of CRISPR, some of which are homologous to fragments of bacteriophage and plasmid genes, function as prokaryotic siRNAs (psiRNA), by base-pairing with the target mRNAs and promoting their degradation or translation shutdown. Specific hypothetical schemes are developed for the functioning of the predicted prokaryotic siRNA system and for the formation of new CRISPR units with unique inserts encoding psiRNA conferring immunity to the respective newly encountered phages or plasmids. The unique inserts in CRISPR show virtually no similarity even between closely related bacterial strains which suggests their rapid turnover, on evolutionary scale. Corollaries of this finding are that, even among closely related prokaryotes, the most commonly encountered phages and plasmids are different and/or that the dominant phages and plasmids turn over rapidly. CONCLUSION: We proposed previously that Cas proteins comprise a novel DNA repair system. The association of the cas genes with CRISPR and, especially, the presence, in CRISPR units, of unique inserts homologous to phage and plasmid genes make us abandon this hypothesis. It appears most likely that CASS is a prokaryotic system of defense against phages and plasmids that functions via the RNAi mechanism. The functioning of this system seems to involve integration of fragments of foreign genes into archaeal and bacterial chromosomes yielding heritable immunity to the respective agents. However, it appears that this inheritance is extremely unstable on the evolutionary scale such that the repertoires of unique psiRNAs are completely replaced even in closely related prokaryotes, presumably, in response to rapidly changing repertoires of dominant phages and plasmids. This article was reviewed by: Eric Bapteste, Patrick Forterre, and Martijn Huynen. OPEN PEER REVIEW: Reviewed by Eric Bapteste, Patrick Forterre, and Martijn Huynen. For the full reviews, please go to the Reviewers' comments section

Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters

Promoters are DNA sequences located upstream of the gene region and play a central role in gene expression. Computational techniques show good accuracy in gene prediction but are less successful in predicting promoters, primarily because of the high number of false positives that reflect characteristics of the promoter sequences. Many machine learning methods have been used to address this issue. Neural Networks (NN) have been successfully used in this field because of their ability to recognize imprecise and incomplete patterns characteristic of promoter sequences. In this paper, NN was used to predict and recognize promoter sequences in two data sets: (i) one based on nucleotide sequence information and (ii) another based on stability sequence information. The accuracy was approximately 80% for simulation (i) and 68% for simulation (ii). In the rules extracted, biological consensus motifs were important parts of the NN learning process in both simulations

Genome-scale bacterial transcriptional regulatory networks: reconstruction and integrated analysis with metabolic models

Advances in sequencing technology are resulting in the rapid emergence of large numbers of complete genome sequences. High throughput annotation and metabolic modeling of these genomes is now a reality. The high throughput reconstruction and analysis of genome-scale transcriptional regulatory networks represents the next frontier in microbial bioinformatics. The fruition of this next frontier will depend upon the integration of numerous data sources relating to mechanisms, components, and behavior of the transcriptional regulatory machinery, as well as the integration of the regulatory machinery into genome-scale cellular models. Here we review existing repositories for different types of transcriptional regulatory data, including expression data, transcription factor data, and binding site locations, and we explore how these data are being used for the reconstruction of new regulatory networks. From template network based methods to de novo reverse engineering from expression data, we discuss how regulatory networks can be reconstructed and integrated with metabolic models to improve model predictions and performance. Finally, we explore the impact these integrated models can have in simulating phenotypes, optimizing the production of compounds of interest or paving the way to a whole-cell model.J.P.F. acknowledges funding from [SFRH/BD/70824/2010] of the FCT (Portuguese Foundation for Science and Technology) PhD program. The work was supported in part by the ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness), National Funds through the FCT within projects [FCOMP-01-0124-FEDER015079] (ToMEGIM—Computational Tools for Metabolic Engineering using Genome-scale Integrated Models) and FCOMP-01-0124-FEDER009707 (HeliSysBio—molecular Systems Biology in Helicobacter pylori), the U.S. Department of Energy under contract [DE-ACO2-06CH11357] and the National Science Foundation under [0850546]

Human Promoter Prediction Using DNA Numerical Representation

With the emergence of genomic signal processing, numerical representation techniques for DNA alphabet set {A, G, C, T} play a key role in applying digital signal processing and machine learning techniques for processing and analysis of DNA sequences. The choice of the numerical representation of a DNA sequence affects how well the biological properties can be reflected in the numerical domain for the detection and identification of the characteristics of special regions of interest within the DNA sequence. This dissertation presents a comprehensive study of various DNA numerical and graphical representation methods and their applications in processing and analyzing long DNA sequences. Discussions on the relative merits and demerits of the various methods, experimental results and possible future developments have also been included. Another area of the research focus is on promoter prediction in human (Homo Sapiens) DNA sequences with neural network based multi classifier system using DNA numerical representation methods. In spite of the recent development of several computational methods for human promoter prediction, there is a need for performance improvement. In particular, the high false positive rate of the feature-based approaches decreases the prediction reliability and leads to erroneous results in gene annotation.To improve the prediction accuracy and reliability, DigiPromPred a numerical representation based promoter prediction system is proposed to characterize DNA alphabets in different regions of a DNA sequence.The DigiPromPred system is found to be able to predict promoters with a sensitivity of 90.8% while reducing false prediction rate for non-promoter sequences with a specificity of 90.4%. The comparative study with state-of-the-art promoter prediction systems for human chromosome 22 shows that our proposed system maintains a good balance between prediction accuracy and reliability. To reduce the system architecture and computational complexity compared to the existing system, a simple feed forward neural network classifier known as SDigiPromPred is proposed. The SDigiPromPred system is found to be able to predict promoters with a sensitivity of 87%, 87%, 99% while reducing false prediction rate for non-promoter sequences with a specificity of 92%, 94%, 99% for Human, Drosophila, and Arabidopsis sequences respectively with reconfigurable capability compared to existing system

Reconstructing prokaryotic transcriptional regulatory networks: lessons from actinobacteria

Reconstruction of transcriptional regulatory networks of uncharacterized bacteria is a main challenge for the post-genomic era. Recent studies, including one in BMC Systems Biology, address this problem in the relatively underexplored actinobacteria clade, which includes major pathogenic and economically relevant taxa