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The need to revisit published data: A concept and framework for complementary proteomics
Author(s) -
Zhou Tao,
Sha Jiahao,
Guo Xuejiang
Publication year - 2016
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201500170
Subject(s) - proteomics , computer science , data science , raw data , computational biology , information retrieval , data mining , bioinformatics , biology , gene , programming language , biochemistry
Tandem proteomic strategies based on large‐scale and high‐resolution mass spectrometry have been widely applied in various biomedical studies. However, protein sequence databases and proteomic software are continuously updated. Proteomic studies should not be ended with a stable list of proteins. It is necessary and beneficial to regularly revise the results. Besides, the original proteomic studies usually focused on a limited aspect of protein information and valuable information may remain undiscovered in the raw spectra. Several studies have reported novel findings by reanalyzing previously published raw data. However, there are still no standard guidelines for comprehensive reanalysis. In the present study, we proposed the concept and draft framework for complementary proteomics, which are aimed to revise protein list or mine new discoveries by revisiting published data.