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      Expanding Tiny Earth to genomics: a bioinformatics approach for an undergraduate class to characterize antagonistic strains

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          ABSTRACT

          The evolution of multidrug resistant pathogens and the diminishing supply of effective antibiotics are global crisis. Tiny Earth (TE) is undergraduate curriculum that encourage students to pursue science careers by engagement in authentic drug discovery research. Through the TE program, students identify environmental strains that inhibit other bacteria. Although these isolates may produce antibiotics based on the antagonistic phenotype, understanding the activity in regard to genome content remains elusive. Previously, we developed a transposon mutagenesis module for use with TE to identify genes involved in antibiotic production. Here, we extend this approach to a second semester undergraduate course to understand the origin of antagonism and genome diversity. Using a bioinformatics strategy, we identified gene clusters involved in activity, and with annotated genomes in hand, students were able to characterize strain diversity. Genomes were analyzed using different computational tools, including average nucleotide identity for species identification and whole genome comparisons. Because the focus of TE involves the evolution of drug resistance, predicted products in strains were identified and verified using a drug susceptibility assay. An application of this curriculum by TE members would assist in efforts with antibiotic discovery.

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          Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis

          The spread of antibiotic-resistant bacteria poses a substantial threat to morbidity and mortality worldwide. Due to its large public health and societal implications, multidrug-resistant tuberculosis has been long regarded by WHO as a global priority for investment in new drugs. In 2016, WHO was requested by member states to create a priority list of other antibiotic-resistant bacteria to support research and development of effective drugs.
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            Is Open Access

            PHASTER: a better, faster version of the PHAST phage search tool

            PHASTER (PHAge Search Tool – Enhanced Release) is a significant upgrade to the popular PHAST web server for the rapid identification and annotation of prophage sequences within bacterial genomes and plasmids. Although the steps in the phage identification pipeline in PHASTER remain largely the same as in the original PHAST, numerous software improvements and significant hardware enhancements have now made PHASTER faster, more efficient, more visually appealing and much more user friendly. In particular, PHASTER is now 4.3× faster than PHAST when analyzing a typical bacterial genome. More specifically, software optimizations have made the backend of PHASTER 2.7X faster than PHAST, while the addition of 80 CPUs to the PHASTER compute cluster are responsible for the remaining speed-up. PHASTER can now process a typical bacterial genome in 3 min from the raw sequence alone, or in 1.5 min when given a pre-annotated GenBank file. A number of other optimizations have also been implemented, including automated algorithms to reduce the size and redundancy of PHASTER's databases, improvements in handling multiple (metagenomic) queries and higher user traffic, along with the ability to perform automated look-ups against 14 000 previously PHAST/PHASTER annotated bacterial genomes (which can lead to complete phage annotations in seconds as opposed to minutes). PHASTER's web interface has also been entirely rewritten. A new graphical genome browser has been added, gene/genome visualization tools have been improved, and the graphical interface is now more modern, robust and user-friendly. PHASTER is available online at www.phaster.ca.
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              Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data.

              We present a hierarchical genome-assembly process (HGAP) for high-quality de novo microbial genome assemblies using only a single, long-insert shotgun DNA library in conjunction with Single Molecule, Real-Time (SMRT) DNA sequencing. Our method uses the longest reads as seeds to recruit all other reads for construction of highly accurate preassembled reads through a directed acyclic graph-based consensus procedure, which we follow with assembly using off-the-shelf long-read assemblers. In contrast to hybrid approaches, HGAP does not require highly accurate raw reads for error correction. We demonstrate efficient genome assembly for several microorganisms using as few as three SMRT Cell zero-mode waveguide arrays of sequencing and for BACs using just one SMRT Cell. Long repeat regions can be successfully resolved with this workflow. We also describe a consensus algorithm that incorporates SMRT sequencing primary quality values to produce de novo genome sequence exceeding 99.999% accuracy.
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                Author and article information

                Journal
                FEMS Microbiology Letters
                Oxford University Press (OUP)
                1574-6968
                January 2020
                January 01 2020
                January 2020
                January 01 2020
                January 23 2020
                : 367
                : 1
                Affiliations
                [1 ]Department of Biological Sciences, Bowling Green State University, Bowling Green, OH 43403, USA
                Article
                10.1093/femsle/fnaa018
                1a3a977d-5478-48c2-b8e4-8b9104f1b1e3
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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