Knowledge of bacterial gene function is crucial to making progress in biomedical research that relies on genomic information. Experimental discovery and validafion of gene function is the foundation on which all gene funcfional annotation is based. Gene annotafion is propagated forward by bioinformatics methods from these experimentally validated genes to all sequenced genomes. In reverse, bioinformafics methods are used to collate informafion about genes, including sequence domains, motifs, etc. in order to infer possible functions that require validation. The Data Management Core will assist the projects in both directions by development and use of web-based database and bioinformatics software applications. A laboratory information management system (LIMS) will be constructed that records and displays informafion about candidate gene experiments in order to coordinate project acfivifies and share information among investigators. Web-based database applications will be developed to integrate and display experimental results with annotation of Acinetobacter baumannii genomes and bioinformatics predictions, along with search functionality to support database queries. Bioinformatics applications will also be applied or created that will improve the quality of gene identification in Ab sequences, update Ab genome annotation, determine the prevalence of target genes across bacterial species, collate bioinformatics predicfions and database searches to increase knowledge about target proteins and idenfify gene functional units (conserved gene clusters). These tools will assist project Pis with interpretation of experimental results, the identification of new target genes and sRNAs for experimental research, and sharing of results with the broader research community.
Information resources will be developed by the Data Management core that will enable the interpretation of experimental results in the broader context of bacterial genomics to uncover potential sources of antibiotic susceptibility in Acinetobacter baumannii, the determination of the prevalence of assayed Ab genes in other pathogenic bacteria in order to prioritize research targets according to their range of occurrence, and the dissemination of research results to the broader research community.
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|Schweppe, Devin K; Chavez, Juan D; Bruce, James E (2016) XLmap: an R package to visualize and score protein structure models based on sites of protein cross-linking. Bioinformatics 32:306-8|
|Chavez, Juan D; Eng, Jimmy K; Schweppe, Devin K et al. (2016) A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry. PLoS One 11:e0167547|
|Schweppe, Devin K; Zheng, Chunxiang; Chavez, Juan D et al. (2016) XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data. Bioinformatics 32:2716-8|
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