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CNGBdb 手册

最近更新 / 版本号：2025-03-14 14:41:13

CNGBdb搜索

CNGBdb拥有大量分子数据和其他信息，并由CNGBdb搜索建立了索引。 这些数据包括文献、项目、样本、实验、测序、组装、变异、基因、蛋白质、序列等。

在CNGBdb的首页，您可以输入任意的有意义的词或是编号来查找相关的信息。例如，基因名(TP53)、物种、疾病等。CNGBdb支持单词搜索，例如，如果搜索“homo”，则会返回匹配“homo”的搜索结果，不会返回匹配“ho” 或 “hom”的搜索结果。更多复杂查询语法，将在CNGBdb迭代版本补充。

查询实例

遵循上述查询语法，用户可以根据数据内容和特征进行搜索。

下面列出了可以使用CNGBdb搜索执行的一些查询示例。

搜索文献PMID24971553。

搜索基因TP53。

搜索蛋白Ovarian cancer-related protein 1。

搜索项目CNP0000028。

搜索样本CNS0000027。

搜索结果

CNGBdb包含项目、样本、实验、测序、组装、文献、变异、蛋白、序列、基因10个数据结构的信息，通过首页输入关键词进行检索，默认返回10个数据结构的所有结果。在搜索结果页面，可以看到每个库的相关度最高的前3条检索结果。如果要查看更多的结果数据，点击各个库下面的更多结果进行查看。在首页搜索栏左侧的下拉列表中选择其中的一个子库，进行检索，检索结果将返回该子库的搜索结果。下拉滚动页面，将加载更多的检索结果，最多显示100条，100条之后的检索结果将不显示。若100条检索结果中仍然未查找到您想要的结果，建议修改检索词进行重新检索。

用户也可通过搜索结果页面的搜索栏，输入检索词进行重新检索，搜索结果页面的搜索栏与首页搜索栏的功能一致。

过滤

左侧的导航过滤器允许用户在不同数据库中进行简洁浏览和轻松导航。 它提供了一种方法，用于探索在相关数据库中分组的搜索结果，并深入研究结果的范围。

详细数据和相关数据

如果您点击某条数据的编号，可以进入到该条数据的详情页面，查看数据的更详细的信息，如在搜索结果页面点击文献的编号(PMID24971553)，将跳转到文献的详情页面(/search/literature/PMID24971553/)。

如果单击特定条目的相关数据，您可以探索CNGBdb引用的其他数据库资源。如在搜索结果的变异库数据，点击某条数据里的基因，可以链接到基因库的基因信息页。

检索同义词转换

CNGBdb搜索配置了物种同义词(同义词表主要来源于分类数据库)及医学主题词(同义词表主要来源于mesh)您在检索某个关键词的时候，该关键词的同义词也能检索到，例如Oryza sativa，其学名为Oryza sativa L，常用名为 rice，Inherited blast name为monocots。您在检索Oryza sativa时，它的同义词Oryza sativa L、rice、monocots也能被检索到。

搜索字段

CNGBdb的10个数据结构，支持不同的搜索字段，搜索字段如下表

结构 搜索字段
文献 标题，作者，期刊，发表类型，来源标识，摘要，关键词
基因 来源标识，物种，基因名，标题，别名
变异 HGVS/基因组变异，物种，基因，疾病，疾病编号，表型，表型编号，项目编号，文献编号，来源标识，基因编号
蛋白 蛋白名称，来源标识，入口标识，物种，基因，关键词，文献编号
序列 基因，来源标识，关键词，文献编号，分子类型，物种，标题
项目 项目编号，其它数据库编号，标题，DOI
样本 EBB编号，样本编号，其它数据库编号，物种，关联编号，样本名称，样本模型，样本保存单位，物种编号
实验 实验编号，其它数据库编号，关联编号，测序平台，标题，文库名称
组装 来源标识，组装编号，组装名称，别名，关联编号，提交者，RefSeq分类

数据源参考文献或数据库链接

CNGBdb数据归档，数据搜索，数据计算，科学数据库等服务引用了部分外部公开的数据，其中主要的外部数据源参考文献或数据源网站链接如下：

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  9. Refseq: Pruitt KD, Tatusova T, Brown GR, et al. NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic acids research. 2012;40(Database issue):D130-5.
  10. Gene: Brown GR, Hem V, Katz KS, et al. Gene: a gene-centered information resource at NCBI. Nucleic acids research. 2015;43(Database issue):D36-42.
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  28. Feng Q, Liang S, Jia H, et al. Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nat Commun. 2015;6:6528. Published 2015 Mar 11. doi:10.1038/ncomms7528.
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  30. Xie H, Guo R, Zhong H, et al. Shotgun Metagenomics of 250 Adult Twins Reveals Genetic and Environmental Impacts on the Gut Microbiome. Cell Syst. 2016;3(6):572-584.e3.
  31. Liu R, Hong J, Xu X, et al. Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention. Nat Med. 2017;23(7):859-868.
  32. He Q, Gao Y, Jie Z, et al. Two distinct metacommunities characterize the gut microbiota in Crohn's disease patients. Gigascience. 2017;6(7):1-11.
  33. Kuang YS, Lu JH, Li SH, et al. Connections between the human gut microbiome and gestational diabetes mellitus. Gigascience. 2017;6(8):1-12.
  34. Jie Z, Xia H, Zhong SL, et al. The gut microbiome in atherosclerotic cardiovascular disease. Nat Commun. 2017;8(1):845. Published 2017 Oct 10. doi:10.1038/s41467-017-00900-1.
  35. Gu Y, Wang X, Li J, et al. Analyses of gut microbiota and plasma bile acids enable stratification of patients for antidiabetic treatment. Nat Commun. 2017;8(1):1785. Published 2017 Nov 27. doi:10.1038/s41467-017-01682-2
  36. Shah SP, Roth A, Goya R, et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature. 2012;486(7403):395-9. Published 2012 Apr 4. doi:10.1038/nature10933.
  37. Banerji S, Cibulskis K, Rangel-Escareno C, et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012;486(7403):405-9. Published 2012 Jun 20. doi:10.1038/nature11154
  38. Pereira B, Chin SF, Rueda OM, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun. 2016;7:11479. Published 2016 May 10. doi:10.1038/ncomms11479
  39. Nik-Zainal S, Alexandrov LB, Wedge DC, et al. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012;149(5):979-93.
  40. Stephens PJ, Tarpey PS, Davies H, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486(7403):400-4. Published 2012 May 16. doi:10.1038/nature11017
  41. Schulze K, Imbeaud S, Letouzé E, et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet. 2015;47(5):505-511.
  42. Kan Z, Zheng H, Liu X, et al. Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma. Genome Res. 2013;23(9):1422-33.
  43. Wu K, Zhang X, Li F, et al. Frequent alterations in cytoskeleton remodelling genes in primary and metastatic lung adenocarcinomas. Nat Commun. 2015;6:10131. Published 2015 Dec 9. doi:10.1038/ncomms10131
  44. Imielinski M, Berger AH, Hammerman PS, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell. 2012;150(6):1107-20.
  45. Li C, Gao Z, Li F, et al. Whole Exome Sequencing Identifies Frequent Somatic Mutations in Cell-Cell Adhesion Genes in Chinese Patients with Lung Squamous Cell Carcinoma. Sci Rep. 2015;5:14237. Published 2015 Oct 27. doi:10.1038/srep14237
  46. Krishnan VG, Ebert PJ, Ting JC, et al. Whole-genome sequencing of asian lung cancers: second-hand smoke unlikely to be responsible for higher incidence of lung cancer among Asian never-smokers. Cancer Res. 2014;74(21):6071-81.
  47. Fernandez-Cuesta L, Peifer M, Lu X, et al. Frequent mutations in chromatin-remodelling genes in pulmonary carcinoids. Nat Commun. 2014;5:3518. Published 2014 Mar 27. doi:10.1038/ncomms4518
  48. Ren S, Wei GH, Liu D, et al. Whole-genome and Transcriptome Sequencing of Prostate Cancer Identify New Genetic Alterations Driving Disease Progression[published online ahead of print, 2017 Sep 18]. Eur Urol. 2017;S0302-2838(17)30720-0. doi:10.1016/j.eururo.2017.08.027
  49. Zehir A, Benayed R, Shah RH, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703-713.
  50. Jiang L, Huang J, Higgs BW, et al. Genomic Landscape Survey Identifies SRSF1 as a Key Oncodriver in Small Cell Lung Cancer. PLoS Genet. 2016;12(4):e1005895. Published 2016 Apr 19. doi:10.1371/journal.pgen.1005895
  51. George J, Lim JS, Jang SJ, et al. Comprehensive genomic profiles of small cell lung cancer. Nature. 2015;524(7563):47-53.
  52. Umemura S, Mimaki S, Makinoshima H, et al. Therapeutic priority of the PI3K/AKT/mTOR pathway in small cell lung cancers as revealed by a comprehensive genomic analysis. J Thorac Oncol. 2014;9(9):1324-31.
  53. Rudin CM, Durinck S, Stawiski EW, et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat Genet. 2012;44(10):1111-6.
  54. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164.
  55. Li MM, Datto M, Duncavage EJ, et al. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint
  56. Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017;19(1):4-23.
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  58. Landrum MJ, Lee JM, Benson M, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2015;44(D1):D862-8.
  59. 1000 Genomes Project Consortium, Auton A, Brooks LD, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68-74.
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  66. https://www.ncbi.nlm.nih.gov
  67. https://www.ebi.ac.uk
  68. https://www.ddbj.nig.ac.jp/index-e.html
  69. http://www.internationalgenome.org
  70. https://phytozome.jgi.doe.gov
  71. HPO: https://hpo.jax.org/app
  72. CHPO: http://www.chinahpo.org
  73. https://sites.google.com/site/jpopgen/dbNSFP
  74. http://evs.gs.washington.edu/EVS
  75. https://genomics.scripps.edu/browser
  76. https://www.ebi.ac.uk/gwas/homeCF
  77. http://www.plantkingdomgdb.com
  78. http://coffee-genome.org
  79. http://banana-genome-hub.southgreen.fr
  80. http://202.127.18.221/bamboo/index.php
  81. http://web.malab.cn
  82. http://bioinfo.bti.cornell.edu/cgi-bin/kiwi/home.cgi
  83. http://chi.mpipz.mpg.de/assembly.html
  84. http://citrus.hzau.edu.cn/orange/download/index.php
  85. http://cucurbitgenomics.org
  86. http://ibi.zju.edu.cn/RiceWeedomes/Echinochloa
  87. http://strawberry-garden.kazusa.or.jp
  88. http://treegenesdb.org/Drupal
  89. http://ngs-data-archive.psc.riken.jp
  90. http://www.kazusa.or.jp
  91. https://solgenomics.net
  92. http://treegenesdb.org/Drupal
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  94. https://genomevolution.org/CoGe
  95. http://gigadb.org

数据归档

将数据和分析结果递交到CNGBdb等公共数据库进行开放共享是科学研究的重要步骤。通过数据共享，全球研究人员可以共同促进基因组学、生物多样性等多个领域的快速发展。CNGBdb为多种数据类型提供了定制化的工具，确保数据的可访问性、可重用性和长期影响力，从而加速生命与健康领域的协作科学进程。

CNGBdb的数据递交归档服务包含核酸序列归档(CNSA)和时空数据库归档系统。CNSA提供多种数据，例如fastq，变异，组装，代谢等数据的归档服务。时空数据库归档系统主要提供时空组数据的归档和可视化服务。

科学数据库

CNGBdb科学数据库将基于CNGBdb的底层数据结构和数据，构建不同领域方向的数据应用，旨在针对不同的研究领域提供科学数据服务，如生物多样性、微生物、癌症、免疫、生殖健康、病原等，满足不同领域的研究人员需求，提升数据价值，促进数据开发应用。

详见：https://db.cngb.org/scientific_database/

数据分析

CNGBdb基于底层归档数据，搭建分布式高性能计算平台，部署BLAST，癌症大数据分析，病原鉴定分析等应用服务。

详见：https://db.cngb.org/analysis/

数据可视化

CNGBdb可视化旨在可视化生命数据。基于CNGBdb底层数据结构，对基因组、转录组、蛋白质组等多组学数据进行多维度的可视化。