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      Fostering Convergence: Strategies for Designing a Graduate Training Program at the Intersection of Environmental Engineering and Computational Sciences

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          Higher education for sustainability by means of transdisciplinary case studies: an innovative approach for solving complex, real-world problems

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            mobileOG-db: a Manually Curated Database of Protein Families Mediating the Life Cycle of Bacterial Mobile Genetic Elements

            ABSTRACT Bacterial mobile genetic elements (MGEs) encode functional modules that perform both core and accessory functions for the element, the latter of which are often only transiently associated with the element. The presence of these accessory genes, which are often close homologs to primarily immobile genes, incur high rates of false positives and, therefore, limits the usability of these databases for MGE annotation. To overcome this limitation, we analyzed 10,776,849 protein sequences derived from eight MGE databases to compile a comprehensive set of 6,140 manually curated protein families that are linked to the “life cycle” (integration/excision, replication/recombination/repair, transfer, stability/transfer/defense, and phage-specific processes) of plasmids, phages, integrative, transposable, and conjugative elements. We overlay experimental information where available to create a tiered annotation scheme of high-quality annotations and annotations inferred exclusively through bioinformatic evidence. We additionally provide an MGE-class label for each entry (e.g., plasmid or integrative element), and assign to each entry a major and minor category. The resulting database, mobileOG-db (for mobile orthologous groups), comprises over 700,000 deduplicated sequences encompassing five major mobileOG categories and more than 50 minor categories, providing a structured language and interpretable basis for an array of MGE-centered analyses. mobileOG-db can be accessed at mobileogdb.flsi.cloud.vt.edu/, where users can select, refine, and analyze custom subsets of the dynamic mobilome. IMPORTANCE The analysis of bacterial mobile genetic elements (MGEs) in genomic data is a critical step toward profiling the root causes of antibiotic resistance, phenotypic or metabolic diversity, and the evolution of bacterial genera. Existing methods for MGE annotation pose high barriers of biological and computational expertise to properly harness. To bridge this gap, we systematically analyzed 10,776,849 proteins derived from eight databases of MGEs to identify 6,140 MGE protein families that can serve as candidate hallmarks, i.e., proteins that can be used as “signatures” of MGEs to aid annotation. The resulting resource, mobileOG-db, provides a multilevel classification scheme that encompasses plasmid, phage, integrative, and transposable element protein families categorized into five major mobileOG categories and more than 50 minor categories. mobileOG-db thus provides a rich resource for simple and intuitive element annotation that can be integrated seamlessly into existing MGE detection pipelines and colocalization analyses.
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              Interdisciplinary and transdisciplinary research and practice: Balancing expectations of the ‘old’ academy with the future model of universities as ‘problem solvers’

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                Author and article information

                Journal
                Environ Sci Technol
                Environ Sci Technol
                es
                esthag
                Environmental Science & Technology
                American Chemical Society
                0013-936X
                1520-5851
                01 March 2024
                12 March 2024
                : 58
                : 10
                : 4465-4468
                Affiliations
                []Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis , St. Louis, Missouri 63130, United States
                []Washington University Center for the Environment , St. Louis, Missouri 63130, United States
                [§ ]Department of Computer Science and Engineering, Washington University in St. Louis , St. Louis, Missouri 63130, United States
                Author notes
                Author information
                https://orcid.org/0000-0003-1546-5647
                https://orcid.org/0000-0002-4634-5640
                https://orcid.org/0000-0002-5380-8893
                Article
                10.1021/acs.est.3c10491
                10938632
                38428924
                65183dfe-9d05-4609-bc00-79f08acc49ca
                © 2024 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 12 December 2023
                Funding
                Funded by: National Science Foundation, Division of Chemical, Bioenergy, Environmental and Transport Systems, doi NA;
                Award ID: CBET-2047470
                Funded by: National Science Foundation, Division of Graduate Education, doi NA;
                Award ID: DGE-2244165
                Categories
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                es3c10491
                es3c10491

                General environmental science
                graduate education,interdisciplinary,convergent research,artificial intelligence,machine learning

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