MGnify: the microbiome sequence data analysis resource in 2023.
MGnify: the microbiome sequence data analysis resource in 2023.
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DOI：
10.1093/nar/gkac1080
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发表时间：
2023-01-06
影响因子：
14.9
通讯作者：
Finn, Robert D.
中科院分区：
文献类型：
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作者：
Richardson, Lorna;Allen, Ben;Baldi, Germana;Beracochea, Martin;Bileschi, Maxwell L.;Burdett, Tony;Burgin, Josephine;Caballero-Perez, Juan;Cochrane, Guy;Colwell, Lucy J.;Curtis, Tom;Escobar-Zepeda, Alejandra;Gurbich, Tatiana A.;Kale, Varsha;Korobeynikov, Anton;Raj, Shriya;Rogers, Alexander B.;Sakharova, Ekaterina;Sanchez, Santiago;Wilkinson, Darren J.;Finn, Robert D.
MGnify平台(https://www.ebi.ac.uk/metagenomics))方便了微生物组衍生的核酸序列的组装、分析和存档。该平台为近50万项分析提供了分类分配和功能注释，这些分析涵盖了来自各种不同环境的元编码、元翻译和元基因组数据集。在过去的3年里，MGnify不仅在包含的数据集的数量方面有所增长，而且还增加了所提供的分析的广度，例如对长阅读序列的分析。MGnify蛋白质数据库现在超过24亿个从元基因组组装中预测的非冗余序列。这个集合现在被组织到一个关系数据库中，使得通过导航回源组装和样本元数据来了解蛋白质的基因组环境成为可能，这标志着一项重大改进。为了在MGnify中已经提供的功能注释之外进行扩展，我们应用了基于深度学习的注释方法。MGnify的应用编程接口(API)和网站的基础技术已经升级，我们通过引入耦合的Jupyter实验室环境，实现了对MGnify数据进行下游分析的能力。MGnify资源概述：来自广泛环境的微生物组衍生序列的组装和注释引起了对微生物多样性及其编码的功能谱的新见解。
The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment. Overview of MGnify resources: the assembly and annotation of microbiome-derived sequences from a broad range of environments has given rise to new insights into microbial diversity and the functional repertoire they encode.
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影响因子：
14.9
作者：
Mistry J;Chuguransky S;Williams L;Qureshi M;Salazar GA;Sonnhammer ELL;Tosatto SCE;Paladin L;Raj S;Richardson LJ;Finn RD;Bateman A
通讯作者：
Bateman A
影响因子：
7.9
作者：
通讯作者：
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
DOI：
10.1002/anie.202203061
发表时间：
2022-09-12
期刊：
Angewandte Chemie (International ed. in English)
影响因子：
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作者：
Eiamthong B;Meesawat P;Wongsatit T;Jitdee J;Sangsri R;Patchsung M;Aphicho K;Suraritdechachai S;Huguenin-Dezot N;Tang S;Suginta W;Paosawatyanyong B;Babu MM;Chin JW;Pakotiprapha D;Bhanthumnavin W;Uttamapinant C
通讯作者：
Uttamapinant C
影响因子：
9.2
作者：
通讯作者：
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