Database Commons a catalog of biological databases

Database Commons - CIG-DB

CIG-DB

Citations: 3

z-index 0.12

Short name CIG-DB
Full name Cancer-related Immunological Gene Database
Description Cancer-related Immunological Gene Database (CIG-DB) is the database for human or mouse immunoglobulin and T cell receptor genes investigated in cancer studies.
URL http://www.scchr-cigdb.jp/
Year founded 2010
Last update & version     
Availability Free to all users
University/Institution hosted Shizuoka Cancer Center Research Institute
Address 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8777, Japan
City Shizuoka
Province/State
Country/Region Japan
Contact name Yasuto Akiyama
Contact email www-admin@scchr-cigdb.jp
Data type(s)
Major organism(s)
Keyword(s)
  • antibody
  • T cell receptor
Publication(s)
  • CIG-DB: the database for human or mouse immunoglobulin and T cell receptor genes available for cancer studies. [PMID: 20663186]

    Yoji Nakamura, Tomoyoshi Komiyama, Motoki Furue, Takashi Gojobori, Yasuto Akiyama
    BMC bioinformatics 2010:11
    3 Citations (Google Scholar as of 2016-03-02)

    Abstract: Immunoglobulin (IG or antibody) and the T-cell receptor (TR) are pivotal proteins in the immune system of higher organisms. In cancer immunotherapy, the immune responses mediated by tumor-epitope-binding IG or TR play important roles in anticancer effects. Although there are public databases specific for immunological genes, their contents have not been associated with clinical studies. Therefore, we developed an integrated database of IG/TR data reported in cancer studies (the Cancer-related Immunological Gene Database [CIG-DB]). This database is designed as a platform to explore public human and murine IG/TR genes sequenced in cancer studies. A total of 38,308 annotation entries for IG/TR proteins were collected from GenBank/DDBJ/EMBL and the Protein Data Bank, and 2,740 non-redundant corresponding MEDLINE references were appended. Next, we filtered the MEDLINE texts by MeSH terms, titles, and abstracts containing keywords related to cancer. After we performed a manual check, we classified the protein entries into two groups: 611 on cancer therapy (Group I) and 1,470 on hematological tumors (Group II). Thus, a total of 2,081 cancer-related IG and TR entries were tabularized. To effectively classify future entries, we developed a computational method based on text mining and canonical discriminant analysis by parsing MeSH/title/abstract words. We performed a leave-one-out cross validation for the method, which showed high accuracy rates: 94.6% for IG references and 94.7% for TR references. We also collected 920 epitope sequences bound with IG/TR. The CIG-DB is equipped with search engines for amino acid sequences and MEDLINE references, sequence analysis tools, and a 3D viewer. This database is accessible without charge or registration at http://www.scchr-cigdb.jp/, and the search results are freely downloadable. The CIG-DB serves as a bridge between immunological gene data and cancer studies, presenting annotation on IG, TR, and their epitopes. This database contains IG and TR data classified into two cancer-related groups and is able to automatically classify accumulating entries into these groups. The entries in Group I are particularly crucial for cancer immunotherapy, providing supportive information for genetic engineering of novel antibody medicines, tumor-specific TR, and peptide vaccines.

Community reviews

Data
quality & quantity
Content organization & presentation
System accessibility & reliability
Reviewed by

Word cloud (embeddable)

Database Commons - Word Cloud

Accessibility

Rate of accessibility:
HTTP status codeDate requested
-1 Failed2018-11-20
-1 Failed2018-11-16
-1 Failed2018-11-13
-1 Failed2018-11-09
-1 Failed2018-11-06
-1 Failed2018-11-02
-1 Failed2018-10-30
-1 Failed2018-10-26
-1 Failed2018-10-23
-1 Failed2018-10-19
-1 Failed2018-10-16
-1 Failed2018-10-12
-1 Failed2018-10-09
-1 Failed2018-10-05
-1 Failed2018-10-02
-1 Failed2018-09-28
-1 Failed2018-09-25
-1 Failed2018-09-21
-1 Failed2018-09-18
-1 Failed2018-09-14
-1 Failed2018-09-11
-1 Failed2018-09-07
-1 Failed2018-09-04
-1 Failed2018-08-31
-1 Failed2018-08-28
-1 Failed2018-08-24
-1 Failed2018-08-21
-1 Failed2018-08-17
-1 Failed2018-08-14
-1 Failed2018-08-10
-1 Failed2018-08-07
-1 Failed2018-08-03
-1 Failed2018-07-31
-1 Failed2018-07-27
-1 Failed2018-07-24
-1 Failed2018-07-20
-1 Failed2018-07-17
-1 Failed2018-07-13
-1 Failed2018-07-10
-1 Failed2018-07-06
-1 Failed2018-07-03
-1 Failed2018-06-29
-1 Failed2018-06-26
-1 Failed2018-06-22
-1 Failed2018-06-19
-1 Failed2018-06-15
-1 Failed2018-06-12
-1 Failed2018-06-08
-1 Failed2018-06-05
-1 Failed2018-06-01
-1 Failed2018-05-29
-1 Failed2018-05-25
-1 Failed2018-05-22
-1 Failed2018-05-18
-1 Failed2018-05-15
-1 Failed2018-05-11
-1 Failed2018-05-08
-1 Failed2018-05-04
-1 Failed2018-05-01
-1 Failed2018-04-27
-1 Failed2018-04-24
-1 Failed2018-04-20
-1 Failed2018-04-17
-1 Failed2018-04-13
-1 Failed2018-04-10
-1 Failed2018-04-06
-1 Failed2018-04-03
-1 Failed2018-02-27
-1 Failed2018-02-23
-1 Failed2018-02-20
-1 Failed2018-02-16
-1 Failed2018-02-13
-1 Failed2018-02-09
-1 Failed2018-02-06
-1 Failed2018-02-02
-1 Failed2018-01-30
-1 Failed2018-01-26
-1 Failed2018-01-23
-1 Failed2018-01-19
-1 Failed2018-01-16
-1 Failed2018-01-12
-1 Failed2018-01-09
-1 Failed2018-01-05
-1 Failed2018-01-02
-1 Failed2017-12-29
-1 Failed2017-12-26
-1 Failed2017-12-22
-1 Failed2017-12-19
-1 Failed2017-12-15
-1 Failed2017-12-12
-1 Failed2017-12-08
-1 Failed2017-12-05
-1 Failed2017-12-01
-1 Failed2017-11-28
-1 Failed2017-11-24
-1 Failed2017-11-21
-1 Failed2017-11-17
-1 Failed2017-11-14
-1 Failed2017-11-10
-1 Failed2017-11-07
-1 Failed2017-11-03
-1 Failed2017-10-31
-1 Failed2017-10-27
-1 Failed2017-10-24
-1 Failed2017-10-20
-1 Failed2017-10-17
-1 Failed2017-10-13
-1 Failed2017-10-10
-1 Failed2017-10-06
-1 Failed2017-10-03
-1 Failed2017-09-29
-1 Failed2017-09-26
-1 Failed2017-09-22
-1 Failed2017-09-19
-1 Failed2017-09-15
-1 Failed2017-09-12
-1 Failed2017-09-08
-1 Failed2017-09-05
200 OK2017-09-01
200 OK2017-08-29
200 OK2017-08-25
-1 Failed2017-08-22
-1 Failed2017-08-18
-1 Failed2017-08-15
-1 Failed2017-08-11
-1 Failed2017-08-08
-1 Failed2017-08-04
-1 Failed2017-08-01
-1 Failed2017-07-28
-1 Failed2017-07-25
-1 Failed2017-07-21
-1 Failed2017-07-18
-1 Failed2017-07-14
-1 Failed2017-07-04
-1 Failed2017-06-30
-1 Failed2017-06-27
-1 Failed2017-06-23
-1 Failed2017-06-20
-1 Failed2017-06-16
-1 Failed2017-06-13
-1 Failed2017-06-09
-1 Failed2017-06-06
-1 Failed2017-06-02
-1 Failed2017-05-30
-1 Failed2017-05-26
-1 Failed2017-05-23
-1 Failed2017-05-19
-1 Failed2017-05-16
-1 Failed2017-05-12
-1 Failed2017-05-09
-1 Failed2017-05-05
-1 Failed2017-05-02
200 OK2017-04-28
200 OK2017-04-25
200 OK2017-04-21
200 OK2017-04-18
200 OK2017-04-14
200 OK2017-04-11
200 OK2017-04-07
200 OK2017-04-04
200 OK2017-03-31
200 OK2017-03-28
200 OK2017-03-24
200 OK2017-03-21
200 OK2017-03-17
200 OK2017-03-14
200 OK2017-03-10
200 OK2017-03-07
200 OK2017-03-03
200 OK2017-02-28
200 OK2017-02-24
200 OK2017-02-21
200 OK2017-02-17
200 OK2017-02-14
200 OK2017-02-10
200 OK2017-02-07
200 OK2017-02-03
200 OK2017-01-31
200 OK2017-01-27
200 OK2017-01-24
200 OK2017-01-20
200 OK2017-01-17
200 OK2017-01-13
200 OK2017-01-10
200 OK2017-01-06
200 OK2017-01-03
200 OK2016-12-30
200 OK2016-12-27
200 OK2016-12-23
200 OK2016-12-20
200 OK2016-12-16
200 OK2016-12-13
200 OK2016-12-09
200 OK2016-12-06
200 OK2016-12-02
200 OK2016-11-29
200 OK2016-11-25
200 OK2016-11-22
200 OK2016-11-18
200 OK2016-11-15
200 OK2016-11-11
200 OK2016-11-08
200 OK2016-11-04
200 OK2016-11-01
200 OK2016-10-28
200 OK2016-10-25
200 OK2016-10-21
200 OK2016-10-18
200 OK2016-10-14
200 OK2016-10-11
200 OK2016-10-07
200 OK2016-10-04
200 OK2016-09-30
200 OK2016-09-27
200 OK2016-09-23
200 OK2016-09-20
200 OK2016-09-16
200 OK2016-09-13
200 OK2016-09-09
200 OK2016-09-06
200 OK2016-09-02
200 OK2016-08-30
200 OK2016-08-26
200 OK2016-08-23
200 OK2016-08-19
200 OK2016-08-16
200 OK2016-08-12
200 OK2016-08-09
200 OK2016-08-05
200 OK2016-08-02
200 OK2016-07-29
200 OK2016-07-26
200 OK2016-07-22
200 OK2016-07-19
200 OK2016-07-15
200 OK2016-07-12
200 OK2016-07-08
200 OK2016-07-05
200 OK2016-07-01
200 OK2016-06-28
200 OK2016-06-24
200 OK2016-06-21
200 OK2016-06-17
200 OK2016-06-14
200 OK2016-06-10
200 OK2016-06-07
200 OK2016-06-03
200 OK2016-05-31
200 OK2016-05-27
200 OK2016-05-24
200 OK2016-05-20
200 OK2016-05-17
-1 Failed2016-05-13
-1 Failed2016-05-10
-1 Failed2016-05-06
-1 Failed2016-05-03
-1 Failed2016-04-29
-1 Failed2016-04-26
-1 Failed2016-04-22
-1 Failed2016-04-19
-1 Failed2016-04-15
-1 Failed2016-04-12
-1 Failed2016-04-08
-1 Failed2016-04-05
-1 Failed2016-04-01
-1 Failed2016-03-29
-1 Failed2016-03-28
-1 Failed2016-03-25
-1 Failed2016-03-23
-1 Failed2016-03-21
-1 Failed2016-03-18
-1 Failed2016-03-16
-1 Failed2016-03-14
-1 Failed2016-03-11
-1 Failed2016-03-09
-1 Failed2016-03-07
-1 Failed2016-03-04
-1 Failed2016-03-02
-1 Failed2016-02-29
-1 Failed2016-02-26
-1 Failed2016-02-24
-1 Failed2016-02-22
-1 Failed2016-02-19
-1 Failed2016-02-17
-1 Failed2016-02-15
-1 Failed2016-02-14
-1 Failed2016-02-12
-1 Failed2016-02-10
-1 Failed2016-02-08
-1 Failed2016-02-07
-1 Failed2016-02-05
-1 Failed2016-02-03
-1 Failed2016-02-01
-1 Failed2016-01-31
-1 Failed2016-01-29
-1 Failed2016-01-27
-1 Failed2016-01-25
-1 Failed2016-01-24
-1 Failed2016-01-22
-1 Failed2016-01-20
-1 Failed2016-01-18
-1 Failed2016-01-17
-1 Failed2016-01-15
-1 Failed2016-01-13
-1 Failed2016-01-11
-1 Failed2016-01-10
-1 Failed2016-01-08
-1 Failed2016-01-06
-1 Failed2016-01-04

Tags

Disease DNA
Homo sapiens Mus musculus
antibody T cell receptor

Record metadata

  • Created on: 2015-06-30
  • Curated by:
    • Mengwei Li [2016-03-31]
    • Mengwei Li [2016-02-20]
    • Mengwei Li [2015-12-02]
    • Lina Ma [2015-06-30]
Stats