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Database Commons - dbDEPC

dbDEPC

Citations: 22

z-index 1.90

Short name dbDEPC
Full name Database of Differentially Expressed Proteins in Human Cancer
Description dbDEPC is a database that collects curated cancer proteomics data, provides a resource for information on protein-level expression changes, and explores protein profile differences among different cancers.
URL http://lifecenter.sgst.cn/dbdepc/index.do
Year founded 2009
Last update & version NA    v2.0
Availability Free to all users
University/Institution hosted Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences
Address Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China
City Shanghai
Province/State
Country/Region China
Contact name Lu Xie
Contact email xielu@scbit.org
Data type(s)
Major organism(s)
Keyword(s)
  • cancer proteomics
  • protein profile differences
  • protein-level expression changes
Publication(s)
  • dbDEPC 2.0: updated database of differentially expressed proteins in human cancers. [PMID: 22096234]

    Ying He, Menghuan Zhang, Yuanhu Ju, Zhonghao Yu, Daqing Lv, Han Sun, Weilan Yuan, Fei He, Jianshe Zhang, Hong Li, Jing Li, Rui Wang-Sattler, Yixue Li, Guoqing Zhang, Lu Xie
    Nucleic acids research 2012:40(Database issue)
    9 Citations (Google Scholar as of 2016-01-22)

    Abstract: A large amount of differentially expressed proteins (DEPs) have been identified in various cancer proteomics experiments, curation and annotation of these proteins are important in deciphering their roles in oncogenesis and tumor progression, and may further help to discover potential protein biomarkers for clinical applications. In 2009, we published the first database of DEPs in human cancers (dbDEPCs). In this updated version of 2011, dbDEPC 2.0 has more than doubly expanded to over 4000 protein entries, curated from 331 experiments across 20 types of human cancers. This resource allows researchers to search whether their interested proteins have been reported changing in certain cancers, to compare their own proteomic discovery with previous studies, to picture selected protein expression heatmap across multiple cancers and to relate protein expression changes with aberrance in other genetic level. New important developments include addition of experiment design information, advanced filter tools for customer-specified analysis and a network analysis tool. We expect dbDEPC 2.0 to be a much more powerful tool than it was in its first release and can serve as reference to both proteomics and cancer researchers. dbDEPC 2.0 is available at http://lifecenter.sgst.cn/dbdepc/index.do.

  • dbDEPC: a database of differentially expressed proteins in human cancers. [PMID: 19900968]

    Hong Li, Ying He, Guohui Ding, Chuan Wang, Lu Xie, Yixue Li
    Nucleic acids research 2010:38(Database issue)
    13 Citations (Google Scholar as of 2016-01-22)

    Abstract: Cancer-related investigations have long been in the limelight of biomedical research. Years of effort from scientists and doctors worldwide have generated large amounts of data at the genome, transcriptome, proteome and even metabolome level, and DNA and RNA cancer signature databases have been established. Here we present a database of differentially expressed proteins in human cancers (dbDEPC), with the goal of collecting curated cancer proteomics data, providing a resource for information on protein-level expression changes, and exploring protein profile differences among different cancers. dbDEPC currently contains 1803 proteins differentially expressed in 15 cancers, curated from 65 mass spectrometry (MS) experiments in peer-reviewed publications. In addition to MS experiments, low-throughput experiment data from the same literatures and cancer-associated genes from external databases were also integrated to provide some validation information. Furthermore, dbDEPC associates differential proteins with important structural variations in the human genome, such as copy number variations or single nucleotide polymorphisms, which might be helpful for explaining changes in protein expression at the DNA level. Data in dbDEPC can be queried by protein identifier, description or sequence; the retrieved protein entry provides the differential expression pattern seen in cancers, along with detailed annotations. dbDEPC is expected to be a reference database for cancer signatures at the protein level. This database is provided at http://dbdepc.biosino.org/index/.

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

Tags

Expression Protein
Homo sapiens
cancer proteomics protein profile differences protein-level expression changes

Record metadata

  • Created on: 2015-06-20
  • Curated by:
    • Jian Sang [2016-04-04]
    • Mengwei Li [2016-02-19]
    • Jian Sang [2015-12-01]
    • Jian Sang [2015-06-26]
Stats