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      PLSDB: advancing a comprehensive database of bacterial plasmids

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          Abstract

          Plasmids are known to contain genes encoding for virulence factors and antibiotic resistance mechanisms. Their relevance in metagenomic data processing is steadily growing. However, with the increasing popularity and scale of metagenomics experiments, the number of reported plasmids is rapidly growing as well, amassing a considerable number of false positives due to undetected misassembles. Here, our previously published database PLSDB provides a reliable resource for researchers to quickly compare their sequences against selected and annotated previous findings. Within two years, the size of this resource has more than doubled from the initial 13,789 to now 34,513 entries over the course of eight regular data updates. For this update, we aggregated community feedback for major changes to the database featuring new analysis functionality as well as performance, quality, and accessibility improvements. New filtering steps, annotations, and preprocessing of existing records improve the quality of the provided data. Additionally, new features implemented in the web-server ease user interaction and allow for a deeper understanding of custom uploaded sequences, by visualizing similarity information. Lastly, an application programming interface was implemented along with a python library, to allow remote database queries in automated workflows. The latest release of PLSDB is freely accessible under https://www.ccb.uni-saarland.de/plsdb.

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          Graphical Abstract

          PLSDB aggregates plasmid and meta data from different public resources. This data passes through several enhanced annotation and filtering steps. Finally, the information is presented in a web-server, providing two new functionalities.

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          Most cited references37

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          BLAST+: architecture and applications

          Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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            CD-HIT: accelerated for clustering the next-generation sequencing data

            Summary: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. Availability: http://cd-hit.org. Contact: liwz@sdsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database

              Abstract The Comprehensive Antibiotic Resistance Database (CARD; https://card.mcmaster.ca) is a curated resource providing reference DNA and protein sequences, detection models and bioinformatics tools on the molecular basis of bacterial antimicrobial resistance (AMR). CARD focuses on providing high-quality reference data and molecular sequences within a controlled vocabulary, the Antibiotic Resistance Ontology (ARO), designed by the CARD biocuration team to integrate with software development efforts for resistome analysis and prediction, such as CARD’s Resistance Gene Identifier (RGI) software. Since 2017, CARD has expanded through extensive curation of reference sequences, revision of the ontological structure, curation of over 500 new AMR detection models, development of a new classification paradigm and expansion of analytical tools. Most notably, a new Resistomes & Variants module provides analysis and statistical summary of in silico predicted resistance variants from 82 pathogens and over 100 000 genomes. By adding these resistance variants to CARD, we are able to summarize predicted resistance using the information included in CARD, identify trends in AMR mobility and determine previously undescribed and novel resistance variants. Here, we describe updates and recent expansions to CARD and its biocuration process, including new resources for community biocuration of AMR molecular reference data.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                07 January 2022
                25 November 2021
                25 November 2021
                : 50
                : D1
                : D273-D278
                Affiliations
                Chair for Clinical Bioinformatics, Saarland University , 66123 Saarbrücken, Germany
                Chair for Clinical Bioinformatics, Saarland University , 66123 Saarbrücken, Germany
                Chair for Clinical Bioinformatics, Saarland University , 66123 Saarbrücken, Germany
                Department of Microbial Natural Products, Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI) , Campus E8 1, 66123 Saarbrücken, Germany
                Chair for Clinical Bioinformatics, Saarland University , 66123 Saarbrücken, Germany
                Chair for Clinical Bioinformatics, Saarland University , 66123 Saarbrücken, Germany
                Department of Microbial Natural Products, Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI) , Campus E8 1, 66123 Saarbrücken, Germany
                Department of Pharmacy, Saarland University , 66123 Saarbrücken, Germany
                Chair for Clinical Bioinformatics, Saarland University , 66123 Saarbrücken, Germany
                Department of Microbial Natural Products, Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI) , Campus E8 1, 66123 Saarbrücken, Germany
                Author notes
                To whom correspondence should be addressed. Tel: +49 681 30268611; Fax: +49 681 30268610; Email: andreas.keller@ 123456ccb.uni-saarland.de
                Author information
                https://orcid.org/0000-0002-9627-9223
                https://orcid.org/0000-0002-8223-3750
                https://orcid.org/0000-0003-1967-2918
                https://orcid.org/0000-0002-5361-0895
                Article
                gkab1111
                10.1093/nar/gkab1111
                8728149
                34850116
                e88a0749-fdb9-4d95-ae4e-4bd75cbef1a1
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                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
                : 22 October 2021
                : 20 October 2021
                : 15 September 2021
                Page count
                Pages: 6
                Funding
                Funded by: Saarland University, DOI 10.13039/501100005690;
                Funded by: Helmholtz-Institute for Pharmaceutical Research Saarland;
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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