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      Microbe-ID: an open source toolbox for microbial genotyping and species identification

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          Abstract

          Development of tools to identify species, genotypes, or novel strains of invasive organisms is critical for monitoring emergence and implementing rapid response measures. Molecular markers, although critical to identifying species or genotypes, require bioinformatic tools for analysis. However, user-friendly analytical tools for fast identification are not readily available. To address this need, we created a web-based set of applications called Microbe-ID that allow for customizing a toolbox for rapid species identification and strain genotyping using any genetic markers of choice. Two components of Microbe-ID, named Sequence-ID and Genotype-ID, implement species and genotype identification, respectively. Sequence-ID allows identification of species by using BLAST to query sequences for any locus of interest against a custom reference sequence database. Genotype-ID allows placement of an unknown multilocus marker in either a minimum spanning network or dendrogram with bootstrap support from a user-created reference database. Microbe-ID can be used for identification of any organism based on nucleotide sequences or any molecular marker type and several examples are provided. We created a public website for demonstration purposes called Microbe-ID ( microbe-id.org) and provided a working implementation for the genus Phytophthora ( phytophthora-id.org). In Phytophthora-ID, the Sequence-ID application allows identification based on ITS or cox spacer sequences. Genotype-ID groups individuals into clonal lineages based on simple sequence repeat (SSR) markers for the two invasive plant pathogen species P. infestans and P. ramorum. All code is open source and available on github and CRAN. Instructions for installation and use are provided at https://github.com/grunwaldlab/Microbe-ID.

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          Most cited references37

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          Invasive methicillin-resistant Staphylococcus aureus infections in the United States.

          As the epidemiology of infections with methicillin-resistant Staphylococcus aureus (MRSA) changes, accurate information on the scope and magnitude of MRSA infections in the US population is needed. To describe the incidence and distribution of invasive MRSA disease in 9 US communities and to estimate the burden of invasive MRSA infections in the United States in 2005. Active, population-based surveillance for invasive MRSA in 9 sites participating in the Active Bacterial Core surveillance (ABCs)/Emerging Infections Program Network from July 2004 through December 2005. Reports of MRSA were investigated and classified as either health care-associated (either hospital-onset or community-onset) or community-associated (patients without established health care risk factors for MRSA). Incidence rates and estimated number of invasive MRSA infections and in-hospital deaths among patients with MRSA in the United States in 2005; interval estimates of incidence excluding 1 site that appeared to be an outlier with the highest incidence; molecular characterization of infecting strains. There were 8987 observed cases of invasive MRSA reported during the surveillance period. Most MRSA infections were health care-associated: 5250 (58.4%) were community-onset infections, 2389 (26.6%) were hospital-onset infections; 1234 (13.7%) were community-associated infections, and 114 (1.3%) could not be classified. In 2005, the standardized incidence rate of invasive MRSA was 31.8 per 100,000 (interval estimate, 24.4-35.2). Incidence rates were highest among persons 65 years and older (127.7 per 100,000; interval estimate, 92.6-156.9), blacks (66.5 per 100,000; interval estimate, 43.5-63.1), and males (37.5 per 100,000; interval estimate, 26.8-39.5). There were 1598 in-hospital deaths among patients with MRSA infection during the surveillance period. In 2005, the standardized mortality rate was 6.3 per 100,000 (interval estimate, 3.3-7.5). Molecular testing identified strains historically associated with community-associated disease outbreaks recovered from cultures in both hospital-onset and community-onset health care-associated infections in all surveillance areas. Invasive MRSA infection affects certain populations disproportionately. It is a major public health problem primarily related to health care but no longer confined to intensive care units, acute care hospitals, or any health care institution.
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            High-throughput genotyping by whole-genome resequencing.

            The next-generation sequencing technology coupled with the growing number of genome sequences opens the opportunity to redesign genotyping strategies for more effective genetic mapping and genome analysis. We have developed a high-throughput method for genotyping recombinant populations utilizing whole-genome resequencing data generated by the Illumina Genome Analyzer. A sliding window approach is designed to collectively examine genome-wide single nucleotide polymorphisms for genotype calling and recombination breakpoint determination. Using this method, we constructed a genetic map for 150 rice recombinant inbred lines with an expected genotype calling accuracy of 99.94% and a resolution of recombination breakpoints within an average of 40 kb. In comparison to the genetic map constructed with 287 PCR-based markers for the rice population, the sequencing-based method was approximately 20x faster in data collection and 35x more precise in recombination breakpoint determination. Using the sequencing-based genetic map, we located a quantitative trait locus of large effect on plant height in a 100-kb region containing the rice "green revolution" gene. Through computer simulation, we demonstrate that the method is robust for different types of mapping populations derived from organisms with variable quality of genome sequences and is feasible for organisms with large genome sizes and low polymorphisms. With continuous advances in sequencing technologies, this genome-based method may replace the conventional marker-based genotyping approach to provide a powerful tool for large-scale gene discovery and for addressing a wide range of biological questions.
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              MLST revisited: the gene-by-gene approach to bacterial genomics.

              Multilocus sequence typing (MLST) was proposed in 1998 as a portable sequence-based method for identifying clonal relationships among bacteria. Today, in the whole-genome era of microbiology, the need for systematic, standardized descriptions of bacterial genotypic variation remains a priority. Here, to meet this need, we draw on the successes of MLST and 16S rRNA gene sequencing to propose a hierarchical gene-by-gene approach that reflects functional and evolutionary relationships and catalogues bacteria 'from domain to strain'. Our gene-based typing approach using online platforms such as the Bacterial Isolate Genome Sequence Database (BIGSdb) allows the scalable organization and analysis of whole-genome sequence data.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                18 August 2016
                2016
                : 4
                : e2279
                Affiliations
                [1 ]Department of Botany and Plant Pathology, Oregon State University , Corvallis, OR, United States
                [2 ]Horticultural Crops Research Laboratory, USDA Agricultural Research Service , Corvallis, OR, United States
                [3 ]Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University , Geneva, NY, United States
                [4 ]Molecular and Cellular Biology Graduate Program and Center for Genome Biology and Biocomputing, Oregon State University , Corvallis, OR, United States
                [5 ] Current affiliation: Department of Plant Pathology, University of Nebraska , Lincoln, NE, United States
                Article
                2279
                10.7717/peerj.2279
                4994078
                27602267
                fbe2da74-0b4f-444a-b937-18cb7a30a9b2
                ©2016 Tabima et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 29 April 2016
                : 2 July 2016
                Funding
                Funded by: US Department of Agriculture (USDA) Agricultural Research Service
                Award ID: 5358-22000-039-00D
                Funded by: USDA National Institute of Food and Agriculture (NIFA)
                Award ID: 2011-68004-30154
                Funded by: USDA ARS Floriculture Nursery Research Initiative
                Funded by: USDA NIFA
                Award ID: 2014-51181-22384
                Award ID: 2012-67012-19844
                This research is supported in part by the US Department of Agriculture (USDA) Agricultural Research Service Grant 5358-22000-039-00D (NJG), USDA National Institute of Food and Agriculture (NIFA) Grant 2011-68004-30154 (NJG), the USDA ARS Floriculture Nursery Research Initiative (NJG), USDA NIFA Grant 2014-51181-22384 (JHC and NJG), and USDA NIFA 2012-67012-19844 (SEE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Agricultural Science
                Bioinformatics
                Microbiology
                Mycology
                Taxonomy

                taxonomy,identification,molecular diagnostics,phytophthora,genotyping,pathogen

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