Dataset Information

Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank.

ABSTRACT: We have developed GeneBase, a full parser of the National Center for Biotechnology Information (NCBI) Gene database, which generates a fully structured local database with an intuitive user-friendly graphic interface for personal computers. Features of all the annotated eukaryotic genes are accessible through three main software tables, including for each entry details such as the gene summary, the gene exon/intron structure and the specific Gene Ontology attributions. The structuring of the data, the creation of additional calculation fields and the integration with nucleotide sequences allow users to make many types of comparisons and calculations that are useful for data retrieval and analysis. We provide an original example analysis of the existing introns across all the available species, through which the classic biological problem of the 'minimal intron' may find a solution using available data. Based on all currently available data, we can define the shortest known eukaryotic GT-AG intron length, setting the physical limit at the 30 base pair intron belonging to the human MST1L gene. This 'model intron' will shed light on the minimal requirement elements of recognition used for conventional splicing functioning. Remarkably, this size is indeed consistent with the sum of the splicing consensus sequence lengths.

SUBMITTER: Piovesan A

PROVIDER: S-EPMC4675715 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank.

Piovesan Allison A Caracausi Maria M Ricci Marco M Strippoli Pierluigi P Vitale Lorenza L Pelleri Maria Chiara MC

DNA research : an international journal for rapid publication of reports on genes and genomes 20151117 6

We have developed GeneBase, a full parser of the National Center for Biotechnology Information (NCBI) Gene database, which generates a fully structured local database with an intuitive user-friendly graphic interface for personal computers. Features of all the annotated eukaryotic genes are accessible through three main software tables, including for each entry details such as the gene summary, the gene exon/intron structure and the specific Gene Ontology attributions. The structuring of the dat ...[more]

PMID: 26581719

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Project description:BackgroundWith the increasing pace of new Genetically Modified Organisms (GMOs) authorized or in pipeline for commercialization worldwide, the task of the laboratories in charge to test the compliance of food, feed or seed samples with their relevant regulations became difficult and costly. Many of them have already adopted the so called "matrix approach" to rationalize the resources and efforts used to increase their efficiency within a limited budget. Most of the time, the "matrix approach" is implemented using limited information and some proprietary (if any) computational tool to efficiently use the available data.ResultsThe developed GMOseek software is designed to support decision making in all the phases of routine GMO laboratory testing, including the interpretation of wet-lab results. The tool makes use of a tabulated matrix of GM events and their genetic elements, of the laboratory analysis history and the available information about the sample at hand. The tool uses an optimization approach to suggest the most suited screening assays for the given sample. The practical GMOseek user interface allows the user to customize the search for a cost-efficient combination of screening assays to be employed on a given sample. It further guides the user to select appropriate analyses to determine the presence of individual GM events in the analyzed sample, and it helps taking a final decision regarding the GMO composition in the sample. GMOseek can also be used to evaluate new, previously unused GMO screening targets and to estimate the profitability of developing new GMO screening methods.ConclusionThe presented freely available software tool offers the GMO testing laboratories the possibility to select combinations of assays (e.g. quantitative real-time PCR tests) needed for their task, by allowing the expert to express his/her preferences in terms of multiplexing and cost. The utility of GMOseek is exemplified by analyzing selected food, feed and seed samples from a national reference laboratory for GMO testing and by comparing its performance to existing tools which use the matrix approach. GMOseek proves superior when tested on real samples in terms of GMO coverage and cost efficiency of its screening strategies, including its capacity of simple interpretation of the testing results.

Project description:Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django. Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms. Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks.

Dataset Information

Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank.

Publications

Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI Gene databank.

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