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      IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes

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          Abstract

          The Integrated Microbial Genomes & Microbiomes system v.5.0 (IMG/M: https://img.jgi.doe.gov/m/) contains annotated datasets categorized into: archaea, bacteria, eukarya, plasmids, viruses, genome fragments, metagenomes, cell enrichments, single particle sorts, and metatranscriptomes. Source datasets include those generated by the DOE’s Joint Genome Institute (JGI), submitted by external scientists, or collected from public sequence data archives such as NCBI. All submissions are typically processed through the IMG annotation pipeline and then loaded into the IMG data warehouse. IMG’s web user interface provides a variety of analytical and visualization tools for comparative analysis of isolate genomes and metagenomes in IMG. IMG/M allows open access to all public genomes in the IMG data warehouse, while its expert review (ER) system (IMG/MER: https://img.jgi.doe.gov/mer/) allows registered users to access their private genomes and to store their private datasets in workspace for sharing and for further analysis. IMG/M data content has grown by 60% since the last report published in the 2017 NAR Database Issue. IMG/M v.5.0 has a new and more powerful genome search feature, new statistical tools, and supports metagenome binning.

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          Most cited references18

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification

            Abstract Many antibiotics, chemotherapeutics, crop protection agents and food preservatives originate from molecules produced by bacteria, fungi or plants. In recent years, genome mining methodologies have been widely adopted to identify and characterize the biosynthetic gene clusters encoding the production of such compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ has assisted researchers in efficiently performing this, both as a web server and a standalone tool. Here, we present the thoroughly updated antiSMASH version 4, which adds several novel features, including prediction of gene cluster boundaries using the ClusterFinder method or the newly integrated CASSIS algorithm, improved substrate specificity prediction for non-ribosomal peptide synthetase adenylation domains based on the new SANDPUMA algorithm, improved predictions for terpene and ribosomally synthesized and post-translationally modified peptides cluster products, reporting of sequence similarity to proteins encoded in experimentally characterized gene clusters on a per-protein basis and a domain-level alignment tool for comparative analysis of trans-AT polyketide synthase assembly line architectures. Additionally, several usability features have been updated and improved. Together, these improvements make antiSMASH up-to-date with the latest developments in natural product research and will further facilitate computational genome mining for the discovery of novel bioactive molecules.
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              Is Open Access

              HMMER web server: 2015 update

              The HMMER website, available at http://www.ebi.ac.uk/Tools/hmmer/, provides access to the protein homology search algorithms found in the HMMER software suite. Since the first release of the website in 2011, the search repertoire has been expanded to include the iterative search algorithm, jackhmmer. The continued growth of the target sequence databases means that traditional tabular representations of significant sequence hits can be overwhelming to the user. Consequently, additional ways of presenting homology search results have been developed, allowing them to be summarised according to taxonomic distribution or domain architecture. The taxonomy and domain architecture representations can be used in combination to filter the results according to the needs of a user. Searches can also be restricted prior to submission using a new taxonomic filter, which not only ensures that the results are specific to the requested taxonomic group, but also improves search performance. The repertoire of profile hidden Markov model libraries, which are used for annotation of query sequences with protein families and domains, has been expanded to include the libraries from CATH-Gene3D, PIRSF, Superfamily and TIGRFAMs. Finally, we discuss the relocation of the HMMER webserver to the European Bioinformatics Institute and the potential impact that this will have.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                08 January 2019
                05 October 2018
                05 October 2018
                : 47
                : Database issue , Database issue
                : D666-D677
                Affiliations
                [1 ]Department of Energy, Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA
                [2 ]Resphera Biosciences, Baltimore, MD, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 925 296 5697; Fax: +1 925 296 5850; Email: IMAChen@ 123456lbl.gov . Correspondence may also be addressed to Natalia N. Ivanova. Email: NNIvanova@ 123456lbl.gov . Correspondence may also be addressed to Nikos C. Kyrpides. Email: nckyrpides@ 123456lbl.gov
                Article
                gky901
                10.1093/nar/gky901
                6323987
                30289528
                4266a081-6710-4c25-a2d0-a8aa733147e5
                Published by Oxford University Press on behalf of Nucleic Acids Research 2018.

                This work is written by (a) US Government employee(s) and is in the public domain in the US.

                History
                : 24 September 2018
                : 17 September 2018
                : 05 September 2018
                Page count
                Pages: 12
                Funding
                Funded by: U.S. Department of Energy 10.13039/100000015
                Award ID: DE-AC02-05CH11231
                Categories
                Database Issue

                Genetics
                Genetics

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