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      eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale

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          Abstract

          Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows for: 1) de novo gene prediction from raw contigs, 2) built-in pairwise orthology prediction, 3) fast protein domain discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de.

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          Most cited references23

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          Prokka: rapid prokaryotic genome annotation.

          T Seemann (2014)
          The multiplex capability and high yield of current day DNA-sequencing instruments has made bacterial whole genome sequencing a routine affair. The subsequent de novo assembly of reads into contigs has been well addressed. The final step of annotating all relevant genomic features on those contigs can be achieved slowly using existing web- and email-based systems, but these are not applicable for sensitive data or integrating into computational pipelines. Here we introduce Prokka, a command line software tool to fully annotate a draft bacterial genome in about 10 min on a typical desktop computer. It produces standards-compliant output files for further analysis or viewing in genome browsers. Prokka is implemented in Perl and is freely available under an open source GPLv2 license from http://vicbioinformatics.com/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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            Prodigal: prokaryotic gene recognition and translation initiation site identification

            Background The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. Results With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. Conclusion We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.
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              UniProt: the universal protein knowledgebase in 2021

              (2020)
              Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Mol Biol Evol
                Mol Biol Evol
                molbev
                Molecular Biology and Evolution
                Oxford University Press
                0737-4038
                1537-1719
                December 2021
                01 October 2021
                01 October 2021
                : 38
                : 12
                : 5825-5829
                Affiliations
                [1 ] Centro de Biotecnologia y Genomica de Plantas, Universidad Politécnica de Madrid (UPM) – Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM , Madrid, Spain
                [2 ] Biobyte Solutions GmbH , Heidelberg, Germany
                [3 ] European Molecular Biology Laboratory, Structural and Computational Biology Unit , Heidelberg, Germany
                [4 ] Department of Bioinformatics, Biocenter, University of Würzburg , Würzburg, Germany
                [5 ] Yonsei Frontier Lab (YFL), Yonsei University , Seoul, South Korea
                Author notes
                Corresponding authors: E-mails: huerta.jaime@ 123456inia.es ; bork@ 123456embl.de .
                Author information
                https://orcid.org/0000-0001-5263-533X
                Article
                msab293
                10.1093/molbev/msab293
                8662613
                34597405
                2bdb0c51-33e5-47f2-b5a3-a1383e50026c
                © The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Pages: 5
                Funding
                Funded by: National Programme for Fostering Excellence in Scientific and Technical Research;
                Award ID: PGC2018-098073-A-I00
                Funded by: Severo Ochoa Centres of Excellence Programme;
                Award ID: SEV-2016-0672
                Funded by: State Research Agency;
                Funded by: Research Technical Support Staff Aid;
                Award ID: PTA2019-017593-I/AEI/10.13039/501100011033
                Funded by: European Research Council, DOI 10.13039/100010663;
                Award ID: ERC-2014-AdG)—GA669830
                Funded by: BMBF, DOI 10.13039/501100002347;
                Award ID: #031A537B
                Categories
                Resources
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                metagenomics,functional annotation,computational genomics,bioinformatics
                Molecular biology
                metagenomics, functional annotation, computational genomics, bioinformatics

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