CNGBdb hosts a vast amount of molecular data and other information that is indexed by CNGBdb Search. These data include literature, project, sample, experiment, run, assembly, variation, gene, protein, sequence et al.

On the homepage of CNGBdb, you can enter any meaningful word or number to find relevant information. For example, gene name (TP53), species, disease, etc. CNGBdb supports word search. For example, if you search for "homo", it will return search results that match "homo" and will not return search results that match "ho" or "hom". More complex query syntax will be added in the CNGBdb iteration version.

Query examples

Following the aforementioned query syntax, users can search according to data content and characteristics.

A few examples of queries that can be performed using CNGBdb Search are listed below.

Search for literature PMID24971553.

Search for gene TP53.

Search for protein Ovarian cancer-related protein 1.

Search for project CNP0000028.

Search for sample CNS0000027.

Search results

CNGBdb contains information on 10 data structures of project, sample, experiment, run, assembly, literature, variation, protein, sequence, gene. Searching by keyword on the homepage, all results in 10 data structures will be returned by default. On the page of search results, you can see the top 3 search results with the highest relevance of each sub database. If you want to view more results, click on “More results” below each sub database to view. Select one of the sub databases from the drop-down list on the left side of the search bar on the homepage to search, corresponding search results of the sub database will be returned. Scroll down the page, more search results up to 100 will be loaded, Search results after 100 will not be displayed. If the results you want are still not found in the 100 search results, it is recommended to modify the search terms to re-search.

Users can also re-search by inputting the search term through the search bar of the search result page, and the search bar of the search results page has the same function as the home search bar.


The navigation filter on the left allows users for a compact view and easy navigation across different databases. It provides a means for exploring the search results grouped in relevant databases and drilling down the scope of the results.

Detailed data and Related data

If you click on the number of a certain data, you can go to the details page of the data to view more detailed information. For example, click the literature number (PMID24971553) on the search result page to jump to the literature details page (/search/literature/PMID24971553/).

If you click on Related data for a particular entry you can explore its cross-references to other databases resources of CNGBdb, such as in the variation database of the search results, click on the gene in a certain data, you can link to the gene information page of the gene database.

Synonym conversion for CNGBdb search

CNGBdb search configures synonymous organisms (the synonym table is mainly from taxonomy database) and medical subject words (the synonym table is mainly comes from mesh). When you search for a keyword, the synonym of the keyword can also be retrieved, for example, Oryza sativa‘s scientific name is Oryza sativa L, Genbank common name is rice, Inherited blast name is monocots. When you search for Oryza sativa, all of its synonyms including Oryza sativa L, rice, monocots can also be retrieved.

Search fields

The 10 data structures of CNGBdb support different search fields. The search fields are as follows.

StructureSearch fields
Literature Title, Author, Journal, Publication type, Source, Abstract, Keywords
Gene Source, Organism, Symbol, Title, Also knowns as
Variation HGVS/Genome variation, Organism, Gene(s), Condition(s), Condition ID, Phenotype(s), Phenotype ID, Project ID, Literature ID, Source
Protein Protein name(s), Source, Entry name, Organism, Gene(s), Keywords
Sequence Gene(s), Source, Keywords, Literature ID, Molecule Type, Organism, Title
ProjectProject ID, Accession in other database, Title, DOI
SampleEBB accession ID, Sample ID, Accession in other database, Data type, Organism, Related accession, Sample name, Sample model, Deposited in, Organism ID
ExperimentExperiment ID, Accession in other database, Related accession, Platform, Title, Library name
AssemblySource, Assembly ID, Assembly name, Synonyms, Related accession, Submitter, RefSeq category

Data source references & Database links

1. Millet: Jia G, Huang X, Zhi H, et al. A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica). Nature genetics. 2013;45(8):957-61.

2. 1KP: Matasci N, Hung LH, Yan Z, et al. Data access for the 1,000 Plants (1KP) project. GigaScience. 2014;3:17.

3. 1KITE: Misof B, Liu S, Meusemann K, et al. Phylogenomics resolves the timing and pattern of insect evolution. Science. 2014;346(6210):763-7.

4. HPO: Kohler S, Vasilevsky NA, Engelstad M, et al. The Human Phenotype Ontology in 2017. Nucleic acids research. 2017;45(D1):D865-D76.

5. NCBI: Coordinators NR. Database resources of the National Center for Biotechnology Information. Nucleic acids research. 2018;46(D1):D8-D13

6. dbSNP: Smigielski EM, Sirotkin K, Ward M, et al. dbSNP: a database of single nucleotide polymorphisms. Nucleic acids research. 2000;28(1):352-5

7. SRA: Kodama Y, Shumway M, Leinonen R, et al. The Sequence Read Archive: explosive growth of sequencing data. Nucleic acids research. 2012;40(Database issue):D54-6.

8. Assembly: Kitts PA, Church DM, Thibaud-Nissen F, et al. Assembly: a resource for assembled genomes at NCBI. Nucleic acids research. 2016;44(D1):D73-80.

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.

11. Taxonomy: Federhen S. The NCBI Taxonomy database. Nucleic acids research. 2012;40(Database issue):D136-43.

12. GEO: Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic acids research. 2013;41(Database issue):D991-5.

13. dbvar: Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic acids research. 2013;41(Database issue):D991-5.

14. Clinvar: Landrum MJ, Lee JM, Benson M, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic acids research. 2016;44(D1):D862-8.

15. OMIM: Amberger JS, Bocchini CA, Schiettecatte F, et al. Online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic acids research. 2015;43(Database issue):D789-98.

16. dbgap: Mailman MD, Feolo M, Jin Y, et al. The NCBI dbGaP database of genotypes and phenotypes. Nature genetics. 2007;39(10):1181-6.

17. EBI: Park YM, Squizzato S, Buso N, et al. The EBI search engine: EBI search as a service-making biological data accessible for all. Nucleic acids research. 2017;45(W1):W545-W9.

18.UniProt : The UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47: D506-515 (2019).

19. WoRMS Editorial Board (2019). World Register of Marine Species. Available from at VLIZ. Accessed 2019-03-06. doi:10.14284/170.

20. Zou Y123,Xue W12,Luo G, et al. 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat Biotechnol,2019/2;37(2):179-185.

21. A new genomic blueprint of the human gut microbiota. Nature (2019) doi: 10.1038/s41586-019-0965-1

22. Almeida A, Mitchell AL, Boland M, Forster SC,Gloor GB, Tarkowska A, Lawley TD and Finn, RD Qin J, Li R, Raes J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010 Mar 4;464(7285):59-65.

23. Qin J, Li Y, Cai Z, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490(7418):55-60.

24. Le Chatelier E, Nielsen T, Qin J, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500(7464):541-6.

25. Li J, Jia H, Cai X, et al. An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol. 2014;32(8):834-41.

26. Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. Cell Host Microbe. 2015;17(6):852.

27. Zhang X, Zhang D, Jia H, et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat Med. 2015;21(8):895-905.

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.

29. Yu J, Feng Q, Wong SH, et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut. 2015;66(1):70-78.

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

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57. 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.

58. 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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6. HPO:

7. CHPO:
























Data archiving

The data archive services of CNGBdb include CNGB Nucleotide Sequence Archive (CNSA) , Pan immune repertoire database (PIRD) and GigaDB, which are committed to the submission, storage and sharing of data for biological sequencing research projects, samples, experiments, assembly, variations, etc. They’re designed to provide researchers around the world with the comprehensive data and information resources today, enabling researchers to use data with maximum authority.

CNSA: CNGB Nucleotide Sequence Archive

PIRD:Pan immune repertoire database


Scientific database

The CNGBdb Scientific databases will build data applications in different fields based on the underlying data structures and data of CNGBdb, aiming to provide scientific data services for different research areas, such as biodiversity, microbe, cancer, immune, reproductive health, pathogen, etc., meet the needs of researchers in different fields, enhance the value of data, and promote data development and application.

PIRD:Pan immune repertoire database

GDRD:Genetic Disease and Rare Disease

DISSECT:Data Integration Solution for Systematic Exploration of Cancer Traits

MDB:Microbiome Database

PVD:Pathogen Variation Database

Data analysis

Based on the underlying data, CNGBdb builds a distributed high-performance computing platform, and deploys application services such as BLAST, Cancer Data Analysis, Pathogen Identification.


DISSECT:Data Integration Solution for Systematic Exploration of Cancer Traits

PVD:Pathogen Variation Database

Data visualization

Visualization is designed to visualize the biological data of CNGBdb using multiple visualization techniques, including the visualization of genomes, transcriptome, proteome and so on.

Data management

CNGB Data Access (CDA) provides users with the services of approval, authorization, and distribution of controlled data. Whether data is authorized for access is determined by the data owner/organization.

Data standard

CNGBdb integrates data structures and standards of international omics, health, and medicine, such as The International Nucleotide Sequence Database Collaboration (INSDC), The Global Alliance for Genomics and Health GA4GH (GA4GH), Global Genome Biodiversity Network (GGBN), American College of Medical Genetics and Genomics (ACMG), and constructs standardized data standards and structures with wide compatibility.