Dataset Information

P2RP: a Web-based framework for the identification and analysis of regulatory proteins in prokaryotic genomes.

ABSTRACT: BACKGROUND: Regulatory proteins (RPs) such as transcription factors (TFs) and two-component system (TCS) proteins control how prokaryotic cells respond to changes in their external and/or internal state. Identification and annotation of TFs and TCSs is non-trivial, and between-genome comparisons are often confounded by different standards in annotation. There is a need for user-friendly, fast and convenient tools to allow researchers to overcome the inherent variability in annotation between genome sequences. RESULTS: We have developed the web-server P2RP (Predicted Prokaryotic Regulatory Proteins), which enables users to identify and annotate TFs and TCS proteins within their sequences of interest. Users can input amino acid or genomic DNA sequences, and predicted proteins therein are scanned for the possession of DNA-binding domains and/or TCS domains. RPs identified in this manner are categorised into families, unambiguously annotated, and a detailed description of their features generated, using an integrated software pipeline. P2RP results can then be outputted in user-specified formats. CONCLUSION: Biologists have an increasing need for fast and intuitively usable tools, which is why P2RP has been developed as an interactive system. As well as assisting experimental biologists to interrogate novel sequence data, it is hoped that P2RP will be built into genome annotation pipelines and re-annotation processes, to increase the consistency of RP annotation in public genomic sequences. P2RP is the first publicly available tool for predicting and analysing RP proteins in users' sequences. The server is freely available and can be accessed along with documentation at http://www.p2rp.org.

SUBMITTER: Barakat M

PROVIDER: S-EPMC3637814 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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P2RP: a Web-based framework for the identification and analysis of regulatory proteins in prokaryotic genomes.

Barakat Mohamed M Ortet Philippe P Whitworth David E DE

BMC genomics 20130420

<h4>Background</h4>Regulatory proteins (RPs) such as transcription factors (TFs) and two-component system (TCS) proteins control how prokaryotic cells respond to changes in their external and/or internal state. Identification and annotation of TFs and TCSs is non-trivial, and between-genome comparisons are often confounded by different standards in annotation. There is a need for user-friendly, fast and convenient tools to allow researchers to overcome the inherent variability in annotation betw ...[more]

PMID: 23601859

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Project description:BackgroundGene organization dynamics is actively studied because it provides useful evolutionary information, makes functional annotation easier and often enables to characterize pathogens. There is therefore a strong interest in understanding the variability of this trait and the possible correlations with life-style. Two kinds of events affect genome organization: on one hand translocations and recombinations change the relative position of genes shared by two genomes (i.e. the backbone gene order); on the other, insertions and deletions leave the backbone gene order unchanged but they alter the gene neighborhoods by breaking the syntenic regions. A complete picture about genome organization evolution therefore requires to account for both kinds of events.ResultsWe developed an approach where we model chromosomes as graphs on which we compute different stability estimators; we consider genome rearrangements as well as the effect of gene insertions and deletions. In a first part of the paper, we fit a measure of backbone gene order conservation (hereinafter called backbone stability) against phylogenetic distance for over 3000 genome comparisons, improving existing models for the divergence in time of backbone stability. Intra- and inter-specific comparisons were treated separately to focus on different time-scales. The use of multiple genomes of a same species allowed to identify genomes with diverging gene order with respect to their conspecific. The inter-species analysis indicates that pathogens are more often unstable with respect to non-pathogens. In a second part of the text, we show that in pathogens, gene content dynamics (insertions and deletions) have a much more dramatic effect on genome organization stability than backbone rearrangements.ConclusionIn this work, we studied genome organization divergence taking into account the contribution of both genome order rearrangements and genome content dynamics. By studying species with multiple sequenced genomes available, we were able to explore genome organization stability at different time-scales and to find significant differences for pathogen and non-pathogen species. The output of our framework also allows to identify the conserved gene clusters and/or partial occurrences thereof, making possible to explore how gene clusters assembled during evolution.

Project description:MotivationAccurate prediction of genes encoding small proteins (on the order of 50 amino acids or less) remains an elusive open problem in bioinformatics. Some of the best methods for gene prediction use either sequence composition analysis or sequence similarity to a known protein coding sequence. These methods often fail for small proteins, however, either due to a lack of experimentally verified small protein coding genes or due to the limited statistical significance of statistics on small sequences. Our approach is based upon the hypothesis that true small proteins will be under selective pressure for encoding the particular amino acid sequence, for ease of translation by the ribosome and for structural stability. This stability can be achieved either independently or as part of a larger protein complex. Given this assumption, it follows that small proteins should display conserved local protein structure properties much like larger proteins. Our method incorporates neural-net predictions for three local structure alphabets within a comparative genomic approach using a genomic alignment of 22 closely related bacteria genomes to generate predictions for whether or not a given open reading frame (ORF) encodes for a small protein.ResultsWe have applied this method to the complete genome for Escherichia coli strain K12 and looked at how well our method performed on a set of 60 experimentally verified small proteins from this organism. Out of a total of 11 407 possible ORFs, we found that 6 of the top 10 and 27 of the top 100 predictions belonged to the set of 60 experimentally verified small proteins. We found 35 of all the true small proteins within the top 200 predictions. We compared our method to Glimmer, using a default Glimmer protocol and a modified small ORF Glimmer protocol with a lower minimum size cutoff. The default Glimmer protocol identified 16 of the true small proteins (all in the top 200 predictions), but failed to predict on 34 due to size cutoffs. The small ORF Glimmer protocol made predictions for all the experimentally verified small proteins but only contained 9 of the 60 true small proteins within the top 200 predictions.Contactjsamayoa@jhu.edu

Dataset Information

P2RP: a Web-based framework for the identification and analysis of regulatory proteins in prokaryotic genomes.

Publications

P2RP: a Web-based framework for the identification and analysis of regulatory proteins in prokaryotic genomes.

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