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      ICEberg 3.0: functional categorization and analysis of the integrative and conjugative elements in bacteria

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          Abstract

          ICEberg 3.0 ( https://tool2-mml.sjtu.edu.cn/ICEberg3/) is an upgraded database that provides comprehensive insights into bacterial integrative and conjugative elements (ICEs). In comparison to the previous version, three key enhancements were introduced: First, through text mining and manual curation, it now encompasses details of 2065 ICEs, 607 IMEs and 275 CIMEs, including 430 with experimental support. Secondly, ICEberg 3.0 systematically categorizes cargo gene functions of ICEs into six groups based on literature curation and predictive analysis, providing a profound understanding of ICEs’diverse biological traits. The cargo gene prediction pipeline is integrated into the online tool ICEfinder 2.0. Finally, ICEberg 3.0 aids the analysis and exploration of ICEs from the human microbiome. Extracted and manually curated from 2405 distinct human microbiome samples, the database comprises 1386 putative ICEs, offering insights into the complex dynamics of Bacteria-ICE-Cargo networks within the human microbiome. With the recent updates, ICEberg 3.0 enhances its capability to unravel the intricacies of ICE biology, particularly in the characterization and understanding of cargo gene functions and ICE interactions within the microbiome. This enhancement may facilitate the investigation of the dynamic landscape of ICE biology and its implications for microbial communities.

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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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            Structure, Function and Diversity of the Healthy Human Microbiome

            Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin, and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics, and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analyzed the largest cohort and set of distinct, clinically relevant body habitats to date. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families, and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology, and translational applications of the human microbiome.
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              Improved metagenomic analysis with Kraken 2

              Although Kraken’s k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.
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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
                05 January 2024
                23 October 2023
                23 October 2023
                : 52
                : D1
                : D732-D737
                Affiliations
                State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University , Shanghai 200030, China
                State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University , Shanghai 200030, China
                State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University , Shanghai 200030, China
                State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University , Shanghai 200030, China
                State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University , Shanghai 200030, China
                Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University , Melbourne, VIC 3800, Australia
                Monash Data Futures Institute, Monash University , Melbourne, VIC 3800, Australia
                State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University , Shanghai 200030, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 2162932943; Fax: +86 2162932418; Email: hyou@ 123456sjtu.edu.cn

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

                Author information
                https://orcid.org/0000-0003-4805-2144
                https://orcid.org/0000-0001-8031-9086
                https://orcid.org/0000-0001-9439-1660
                Article
                gkad935
                10.1093/nar/gkad935
                10767825
                37870467
                413b1977-a85e-4397-9eb4-5c1a91782db1
                © The Author(s) 2023. 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
                : 12 October 2023
                : 07 October 2023
                : 09 September 2023
                Page count
                Pages: 6
                Funding
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 32070572
                Funded by: Science and Technology Commission of Shanghai Municipality, DOI 10.13039/501100003399;
                Award ID: 19JC1413000
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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