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      Comparative genomics of Mycobacterium africanum Lineage 5 and Lineage 6 from Ghana suggests distinct ecological niches

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          Abstract

          Mycobacterium africanum ( Maf) causes a substantial proportion of human tuberculosis in some countries of West Africa, but little is known on this pathogen. We compared the genomes of 253 Maf clinical isolates from Ghana, including N = 175 Lineage 5 (L5) and N = 78 Lineage 6 (L6). We found that the genomic diversity of L6 was higher than in L5 despite the smaller sample size. Regulatory proteins appeared to evolve neutrally in L5 but under purifying selection in L6. Even though over 90% of the human T cell epitopes were conserved in both lineages, L6 showed a higher ratio of non-synonymous to synonymous single nucleotide variation in these epitopes overall compared to L5. Of the 10% human T cell epitopes that were variable, most carried mutations that were lineage-specific. Our findings indicate that Maf L5 and L6 differ in some of their population genomic characteristics, possibly reflecting different selection pressures linked to distinct ecological niches.

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            The immune epitope database (IEDB) 3.0

            The IEDB, www.iedb.org, contains information on immune epitopes—the molecular targets of adaptive immune responses—curated from the published literature and submitted by National Institutes of Health funded epitope discovery efforts. From 2004 to 2012 the IEDB curation of journal articles published since 1960 has caught up to the present day, with >95% of relevant published literature manually curated amounting to more than 15 000 journal articles and more than 704 000 experiments to date. The revised curation target since 2012 has been to make recent research findings quickly available in the IEDB and thereby ensure that it continues to be an up-to-date resource. Having gathered a comprehensive dataset in the IEDB, a complete redesign of the query and reporting interface has been performed in the IEDB 3.0 release to improve how end users can access this information in an intuitive and biologically accurate manner. We here present this most recent release of the IEDB and describe the user testing procedures as well as the use of external ontologies that have enabled it.
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              Global phylogeography of Mycobacterium tuberculosis and implications for tuberculosis product development.

              New tools for controlling tuberculosis are urgently needed. Despite our emerging understanding of the biogeography of Mycobacterium tuberculosis, the implications for development of new diagnostics, drugs, and vaccines is unknown. M tuberculosis has a clonal genetic population structure that is geographically constrained. Evidence suggests strain-specific differences in virulence and immunogenicity in light of this global phylogeography. We propose a strain selection framework, based on robust phylogenetic markers, which will allow for systematic and comprehensive evaluation of new tools for tuberculosis control.
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                Author and article information

                Contributors
                sebastien.gagneux@swisstph.ch
                dyboah-Manu@noguchi.ug.edu.gh
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                26 July 2018
                26 July 2018
                2018
                : 8
                : 11269
                Affiliations
                [1 ]ISNI 0000 0004 1937 1485, GRID grid.8652.9, Noguchi Memorial Institute for Medical Research, , University of Ghana, Legon, ; Accra, Ghana
                [2 ]ISNI 0000 0004 1937 1485, GRID grid.8652.9, Department of Biochemistry, , Cell and Molecular Biology, University of Ghana, Legon, ; Accra, Ghana
                [3 ]ISNI 0000 0004 0587 0574, GRID grid.416786.a, Swiss Tropical and Public Health Institute, ; Basel, Switzerland
                [4 ]ISNI 0000 0004 1937 0642, GRID grid.6612.3, University of Basel, ; Basel, Switzerland
                [5 ]ISNI 0000000121885934, GRID grid.5335.0, Wellcome Trust Sanger Institute, , University of Cambridge, ; Hinxton, United Kingdom
                [6 ]ISNI 0000 0001 2153 5088, GRID grid.11505.30, Institute of Tropical Medicine, ; Antwerp, Belgium
                [7 ]ISNI 0000 0004 0546 3805, GRID grid.415489.5, Chest Clinic, Korle-Bu Teaching Hospital, ; Accra, Ghana
                [8 ]GRID grid.460805.f, 37 Military Hospital, ; Accra, Ghana
                [9 ]ISNI 0000 0004 0374 4427, GRID grid.460777.5, Chest Department, , Tamale Teaching Hospital, ; Tamale, Ghana
                [10 ]ISNI 0000 0004 1937 1485, GRID grid.8652.9, Public Health Department, , University of Ghana Hospital, Legon, ; Accra, Ghana
                [11 ]ISNI 0000 0004 0606 294X, GRID grid.415063.5, Medical Research Council Unit The Gambia at The London School of Hygiene and Tropical Medicine, ; Banjul, The Gambia
                [12 ]ISNI 0000 0004 0493 9170, GRID grid.418187.3, Molecular and Experimental Mycobacteriology, , Research Center Borstel, ; Borstel, Germany
                [13 ]German Center for Infection Research, Partner Site Hamburg-Borstel-Lübeck, Lübeck, Germany
                [14 ]National Reference Laboratory for Mycobacteria, Cotonou, Benin
                [15 ]ISNI 0000 0001 2156 2780, GRID grid.5801.c, Department of Biosystems Science and Engineering, , ETH Zurich, ; Basel, Switzerland
                [16 ]ISNI 0000 0000 8809 1613, GRID grid.7372.1, Division of Microbiology & Immunity, , Warwick Medical School, University of Warwick, ; Coventry, CV4 7AL UK
                Author information
                http://orcid.org/0000-0001-8982-9488
                http://orcid.org/0000-0002-4162-0228
                http://orcid.org/0000-0001-8929-193X
                http://orcid.org/0000-0002-7069-5958
                http://orcid.org/0000-0003-1512-6194
                Article
                29620
                10.1038/s41598-018-29620-2
                6062541
                30050166
                0ae524b7-f5d9-4bfc-8533-ec55b015758f
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 November 2017
                : 16 July 2018
                Funding
                Funded by: Swiss National Science Foundation grants 310030_166687, IZRJZ3_164171 and IZLSZ3_170834,the European Research Council grant 309540-EVODRTB and SystemsX.ch
                Funded by: Wellcome Trust grant intemediate fellowship. Grant number 097134/Z/11/Z
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