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      Identification and prioritization of macrolideresistance genes with hypothetical annotation inStreptococcus pneumoniae

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      1 , 1 , *
      Bioinformation
      Biomedical Informatics
      resistance genes, hypothetical genes, Streptococcus pneumoniae

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          Abstract

          Macrolide resistant Streptococcus pneumoniae infections have limited treatment options. While some resistance mechanisms are well established, ample understanding is limited by incomplete genome annotation (hypothetical genes). Some hypothetical genes encode a domain of unknown function (DUF), a conserved protein domain with uncharacterized function. Here, we identify and confirm macrolide resistance genes. We further explore DUFs from macrolide resistance hypothetical genes to prioritize them for experimental characterization. We found gene similarities between two macrolide resistance gene signatures from untreated and either erythromycin- or spiramycin-treated resistant Streptococcus pneumoniae. We confirmed the association of these gene sets with macrolide resistance through comparison to gene signatures from (i) second erythromycin resistant Streptococcus pneumoniae strain, and (ii) erythromycin-treated sensitive Streptococcus pneumoniae strain, both from non-overlapping datasets. Examination into which cellular processes these macrolide resistance genes belong found connections to known resistance mechanisms such as increased amino acid biosynthesis and efflux genes, and decreased ribonucleotide biosynthesis genes, highlighting the predictive ability of the method used. 22 genes had hypothetical annotation with 10 DUFs associated with macrolide resistance. DUF characterization could uncover novel co-therapies that restore macrolide efficacy across multiple macrolide resistant species. Application of the methods to other antibiotic resistances could revolutionize treatment of resistant infections

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Ontological analysis of gene expression data: current tools, limitations, and open problems.

            Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.
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              Prediction of antibiotic resistance by gene expression profiles

              Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype–genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype–genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances.
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                Author and article information

                Journal
                Bioinformation
                Bioinformation
                Bioinformation
                Bioinformation
                Biomedical Informatics
                0973-2063
                2018
                9 December 2018
                : 14
                : 9
                : 488-498
                Affiliations
                [1 ]Davenport University, 200 S. Grand Ave, Lansing, MI, 48933, USA
                Author notes
                Article
                97320630014488
                10.6026/97320630014488
                6563660
                d8e3e4ef-b752-412b-a2d4-73222105fa80
                © 2018 Biomedical Informatics

                This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.

                History
                : 21 November 2018
                : 22 November 2018
                Categories
                Hypothesis

                Bioinformatics & Computational biology
                resistance genes,hypothetical genes,streptococcus pneumoniae

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