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  • 1
    Online Resource
    Online Resource
    Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. D1 ( 2022-01-07), p. D27-D38
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. D1 ( 2022-01-07), p. D27-D38
    Abstract: The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab951
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2023
    Oxford University Press (OUP) ; 2023
    In:  Nucleic Acids Research Vol. 51, No. D1 ( 2023-01-06), p. D18-D28
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 51, No. D1 ( 2023-01-06), p. D18-D28
    Abstract: The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global academic and industrial communities. With the explosive accumulation of multi-omics data generated at an unprecedented rate, CNCB-NGDC constantly expands and updates core database resources by big data archive, integrative analysis and value-added curation. In the past year, efforts have been devoted to integrating multiple omics data, synthesizing the growing knowledge, developing new resources and upgrading a set of major resources. Particularly, several database resources are newly developed for infectious diseases and microbiology (MPoxVR, KGCoV, ProPan), cancer-trait association (ASCancer Atlas, TWAS Atlas, Brain Catalog, CCAS) as well as tropical plants (TCOD). Importantly, given the global health threat caused by monkeypox virus and SARS-CoV-2, CNCB-NGDC has newly constructed the monkeypox virus resource, along with frequent updates of SARS-CoV-2 genome sequences, variants as well as haplotypes. All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkac1073
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021
    Oxford University Press (OUP) ; 2021
    In:  Nucleic Acids Research Vol. 49, No. D1 ( 2021-01-08), p. D18-D28
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. D1 ( 2021-01-08), p. D18-D28
    Abstract: The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkaa1022
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Database Resources of the National Genomics Data Center in 2020
    Oxford University Press (OUP) ; 2019
    In:  Nucleic Acids Research ( 2019-11-08)
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), ( 2019-11-08)
    Abstract: The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkz913
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Database Resources of the BIG Data Center in 2019
    Oxford University Press (OUP) ; 2019
    In:  Nucleic Acids Research Vol. 47, No. D1 ( 2019-01-08), p. D8-D14
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 47, No. D1 ( 2019-01-08), p. D8-D14
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gky993
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Database Resources of the BIG Data Center in 2018
    Oxford University Press (OUP) ; 2018
    In:  Nucleic Acids Research Vol. 46, No. D1 ( 2018-01-04), p. D14-D20
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 46, No. D1 ( 2018-01-04), p. D14-D20
    Abstract: The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkx897
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    Reconstruction Method of Ocean Front Model Based on Fuzzy Cluster Analysis of Sound Speed Profile
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Access Vol. 9 ( 2021), p. 112714-112725
    Details
    In: IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), Vol. 9 ( 2021), p. 112714-112725
    Type of Medium: Online Resource
    ISSN: 2169-3536
    URL: Article
    DOI: 10.1109/ACCESS.2021.3103745
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
    detail.hit.zdb_id: 2687964-5
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  • 8
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    Online Resource
    Ocean Front Reconstruction Method Based on K-Means Algorithm Iterative Hierarchical Clustering Sound Speed Profile
    MDPI AG ; 2021
    In:  Journal of Marine Science and Engineering Vol. 9, No. 11 ( 2021-11-08), p. 1233-
    Details
    In: Journal of Marine Science and Engineering, MDPI AG, Vol. 9, No. 11 ( 2021-11-08), p. 1233-
    Abstract: As one of the most common mesoscale phenomena in the ocean, the ocean front is defined as a narrow transition zone between two water masses with obviously different properties. In this study, we proposed an ocean front reconstruction method based on the K-means algorithm iterative hierarchical clustering sound speed profile (SSP). This method constructed the frontal zone from the perspective of SSP. Meanwhile, considering that acoustic ray tracing is a very sensitive tool for detecting the location of ocean fronts because of the strong dependence of the transmission loss (TL) on SSP structure, this paper verified the feasibility of the method from the perspective of the TL calculation. Compared with other existing methods, this method has the key step of iterative hierarchical clustering according to the accuracy of clustering results. The results of iterative hierarchical clustering of the SSP can reconstruct the ocean front. Using this method, we reconstructed the ocean front in the Gulf Stream-related sea area and obtained the three-dimensional structure of the Gulf Stream front (GSF). The three-dimensional structure was divided into seven layers in the depth range of 0–1000 m. Iterative hierarchical clustering SSP by K-means algorithm provides a new method for judging the frontal zone and reconstructing the geometric model of the ocean front in different depth ranges.
    Type of Medium: Online Resource
    ISSN: 2077-1312
    URL: Article
    DOI: 10.3390/jmse9111233
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2738390-8
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  • 9
    Online Resource
    Online Resource
    Reconstruction of Ocean Front Model Based on Sound Speed Clustering and Its Effectiveness in Ocean Acoustic Forecasting
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 18 ( 2021-09-12), p. 8461-
    Details
    In: Applied Sciences, MDPI AG, Vol. 11, No. 18 ( 2021-09-12), p. 8461-
    Abstract: As a mesoscale phenomenon of the ocean, the ocean front can directly affect the structural characteristics of sound speed profiles and further affect the acoustic propagation characteristics of the sea area. In this paper, we use the fuzzy C-means (FCM) algorithm to cluster the surface sound speed in the sea area of the Kuroshio Extension (KE) and detect the frontal zone of Kuroshio Extension (KEF). At the same time, the sound speed profile (SSP) is used instead of the temperature profile to establish the model of the sound speed field in the front area of the Kuroshio Extension and to improve the theoretical model of the ocean front. Compared with the actual ocean front calculated by reanalysis data, the root means square error (RSME) of the transmission loss (TL) calculated by the model is controlled below 6 dB, which proves the validity of the model. Finally, we propose the melt function in the model to forecast the depth change of the acoustic convergence area. Compared with the actual calculation result based on reanalysis data, the root means square error (RSME) of the depth forecasting after the frontal zone is 43.3 m. This reconstruction method does not rely on the high spatial resolution data of the whole sea depth and can be of referential significance to acoustic detection in the ocean front environment.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app11188461
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 10
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    Online Resource
    Immune-Ageing Evaluation of Peripheral T and NK Lymphocyte Subsets in Chinese Healthy Adults
    Springer Science and Business Media LLC ; 2023
    In:  Phenomics Vol. 3, No. 4 ( 2023-08), p. 360-374
    Details
    In: Phenomics, Springer Science and Business Media LLC, Vol. 3, No. 4 ( 2023-08), p. 360-374
    Abstract: Ageing is often accompanied with a decline in immune system function, resulting in immune ageing. Numerous studies have focussed on the changes in different lymphocyte subsets in diseases and immunosenescence. The change in immune phenotype is a key indication of the diseased or healthy status. However, the changes in lymphocyte number and phenotype brought about by ageing have not been comprehensively analysed. Here, we analysed T and natural killer (NK) cell subsets, the phenotype and cell differentiation states in 43,096 healthy individuals, aged 20–88 years, without known diseases. Thirty-six immune parameters were analysed and the reference ranges of these subsets were established in different age groups divided into 5-year intervals. The data were subjected to random forest machine learning for immune-ageing modelling and confirmed using the neural network analysis. Our initial analysis and machine modelling prediction showed that naïve T cells decreased with ageing, whereas central memory T cells (Tcm) and effector memory T cells (Tem) increased cluster of differentiation (CD) 28-associated T cells. This is the largest study to investigate the correlation between age and immune cell function in a Chinese population, and provides insightful differences, suggesting that healthy adults might be considerably influenced by age and sex. The age of a person's immune system might be different from their chronological age. Our immune-ageing modelling study is one of the largest studies to provide insights into ‘immune-age’ rather than ‘biological-age’. Through machine learning, we identified immune factors influencing the most through ageing and built a model for immune-ageing prediction. Our research not only reveals the impact of age on immune parameter differences within the Chinese population, but also provides new insights for monitoring and preventing some diseases in clinical practice.
    Type of Medium: Online Resource
    ISSN: 2730-583X , 2730-5848
    URL: Article
    DOI: 10.1007/s43657-023-00106-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 3045731-2
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