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      dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data

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          Abstract

          The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

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          Most cited references65

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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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            Fast and sensitive protein alignment using DIAMOND.

            The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity.
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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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                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
                29 November 2021
                29 November 2021
                : 50
                : D1
                : D460-D470
                Affiliations
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Science and Engineering, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Science and Engineering, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                Department of Computer Science and Information Engineering, National Central University , Taoyuan 32001, Taiwan
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Science and Engineering, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Science and Engineering, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                Department of Biomedical Sciences and Engineering, National Central University , Taoyuan 32001, Taiwan
                Graduate Institute of Biomedical Informatics, Taipei Medical University , Taipei 10675, Taiwan
                Department of Computer Science and Information Engineering, National Central University , Taoyuan 32001, Taiwan
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                Warshel Institute for Computational Biology, The Chinese University of Hong Kong , Shenzhen 518172, China
                School of Life and Health Sciences, The Chinese University of Hong Kong , Shenzhen 518172, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 755 2351 9551; Email:  leetzongyi@ 123456cuhk.edu.cn
                Correspondence may also be addressed to Zhuo Wang. Tel: +86 755 5171 8241; Email: wangzhuo@ 123456cuhk.edu.cn

                The authors wish it to be known that, in their opinion, the first three authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0001-6158-5207
                https://orcid.org/0000-0002-6778-7034
                https://orcid.org/0000-0002-6762-6249
                https://orcid.org/0000-0002-7076-8432
                https://orcid.org/0000-0001-8475-7868
                Article
                gkab1080
                10.1093/nar/gkab1080
                8690246
                34850155
                53c89132-9f07-47ef-a931-4ab532666805
                © 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
                : 25 October 2021
                : 16 October 2021
                : 26 August 2021
                Page count
                Pages: 11
                Funding
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 32070659
                Funded by: Guangdong Province Basic and Applied Basic Research Fund;
                Award ID: 2021A1515012447
                Funded by: Ganghong Young Scholar Development Fund;
                Award ID: 2021E007
                Funded by: Science, Technology and Innovation Commission of Shenzhen Municipality, DOI 10.13039/501100010877;
                Award ID: JCYJ20200109150003938
                Funded by: Chinese University of Hong Kong, DOI 10.13039/501100004853;
                Award ID: P2-2021-ZYL-001-A
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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