title: Search Databases and Statistics: Pitfalls and Best Practices in Phosphoproteomics.
abstract: Advances in mass spectrometric instrumentation in the past 15 years have resulted in an explosion in the raw data yield from typical phosphoproteomics workflows. This poses the challenge of confidently identifying peptide sequences, localizing phosphosites to proteins and quantifying these from the vast amounts of raw data. This task is tackled by computational tools implementing algorithms that match the experimental data to databases, providing the user with lists for downstream analysis. Several platforms for such automated interpretation of mass spectrometric data have been developed, each having strengths and weaknesses that must be considered for the individual needs. These are reviewed in this chapter. Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here.
authors.affiliation: Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark.|Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark.
authors.forename: Jan C
authors.initials: JC
authors.lastname: Refsgaard
authors.affiliation: Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Munk
authors.affiliation: Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark. lars.juhl.jensen@cpr.ku.dk.
authors.forename: Lars J
authors.initials: LJ
authors.lastname: Jensen
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000706:statistics & numerical data; D057225:Data Mining / Q000706:statistics & numerical data; D030562:Databases, Protein / Q000706:statistics & numerical data; D006801:Humans; D013058:Mass Spectrometry; D015233:Models, Statistical; D010750:Phosphoproteins / Q000737:chemistry* / Q000378:metabolism; D010766:Phosphorylation; D011499:Protein Processing, Post-Translational; D040901:Proteomics / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software; D057188:Workflow
chemical_list: D010750:Phosphoproteins
keywords: Database Search; False Discovery Rate; MaxQuant; Phosphoproteomics; Quantitation; Statistics