Knowledge-based technologies in proteomics

被引:1
|
作者
Ponomarenko, E. A. [1 ]
Ilgisonis, E. V. [2 ]
Lisitsa, A. V. [1 ]
机构
[1] Russian Acad Med Sci, Inst Biomed Chem, Moscow 119121, Russia
[2] Russian Acad Sci, Engelhardt Inst Mol Biol, Moscow 119991, Russia
关键词
knowledgebase; metabolic pathway; gene ontology; mass-spectrometry; PROTEINS; DATABASE;
D O I
10.1134/S1068162011020129
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Proteomic technologies enable one to identify thousands of proteins in biological samples. These data require appropriate means for storage, dissemination and analytical processing to decipher the new knowledge. Automatic processing of high-efficient experimental results is powered by the controlled vocabularies, such as Medical Subject Headings and GeneOntology. While ontology and vocabularies undergo constant evolution, it is necessary to provide centralized storage of proteomic data for further revision in accordance with the updated knowledge domain. Proteomic repositories like PRIDE, The Global Proteome Machine, PeptideAtlas, etc., are available to harbor the wealth of mass spectral data and appropriate protein identifications. The existing repositories facilitate the development of knowledge extraction technologies to compare the list of identified proteins with the GeneOntology annotations, Medical Subject Headings, metabolic and regulatory pathways. This paper reviews modern analytical tools that exploit the knowledge-based technologies for proteome research.
引用
收藏
页码:168 / 175
页数:8
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