DBParser: Web-Based Software for Shotgun Proteomic Data Analyses
Author(s) -
Xiaoyu Yang,
Vijay Dondeti,
Rebecca Dezube,
Dawn Maynard,
Lewis Y. Geer,
Jonathan A. Epstein,
Xiongfong Chen,
Sanford P. Markey,
Jeffrey A. Kowalak
Publication year - 2004
Publication title -
journal of proteome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.644
H-Index - 161
eISSN - 1535-3907
pISSN - 1535-3893
DOI - 10.1021/pr049920x
Subject(s) - computer science , set (abstract data type) , shotgun proteomics , data set , data mining , software , information retrieval , computational biology , proteomics , artificial intelligence , biology , biochemistry , gene , programming language
We describe a web-based program called 'DBParser' for rapidly culling, merging, and comparing sequence search engine results from multiple LC-MS/MS peptide analyses. DBParser employs the principle of parsimony to consolidate redundant protein assignments and derive the most concise set of proteins consistent with all of the assigned peptide sequences observed in an experiment or series of experiments. The resulting reports summarize peptide and protein identifications from multidimensional experiments that may contain a single data set or combine data from a group of data sets, all related to a single analytical sample. Additionally, the results of multiple experiments, each of which may contain several data sets, can be compared in reports that identify features that are common or different. DBParser actively links to the primary mass spectral data and to public online databases such as NCBI, GO, and Swiss-Prot in order to structure contextually specific reports for biologists and biochemists.
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