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Visualizing the agreement of peptide assignments between different search engines
Author(s) -
Agten Annelies,
Van Houtven Joris,
Askenazi Manor,
Burzykowski Tomasz,
Laukens Kris,
Valkenborg Dirk
Publication year - 2020
Publication title -
journal of mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 121
eISSN - 1096-9888
pISSN - 1076-5174
DOI - 10.1002/jms.4471
Subject(s) - complementarity (molecular biology) , search engine , visualization , benchmark (surveying) , database search engine , computer science , information retrieval , data mining , genetics , geodesy , biology , geography
There is a trend in the analysis of shotgun proteomics data that aims to combine information from multiple search engines to increase the number of peptide annotations in an experiment. Typically, the degree of search engine complementarity and search engine agreement is visually illustrated by means of Venn diagrams that present the findings of a database search on the level of the nonredundant peptide annotations. We argue this practice to be not fit‐for‐purpose since the diagrams do not take into account and often conceal the information on complementarity and agreement at the level of the spectrum identification. We promote a new type of visualization that provides insight on the peptide sequence agreement at the level of the peptide‐spectrum match (PSM) as a measure of consensus between two search engines with nominal outcomes. We applied the visualizations and percentage sequence agreement to an in‐house data set of our benchmark organism, Caenorhabditis elegans , and illustrated that when assessing the agreement between search engine, one should disentangle the notion of PSM confidence and PSM identity. The visualizations presented in this manuscript provide a more informative assessment of pairs of search engines and are made available as an R function in the Supporting Information.