title: Global quantitative proteomics using spectral counting: an inexpensive experimental and bioinformatics workflow for deep proteome coverage.
abstract: As the field of proteomics shifts from qualitative identification of protein "subfractions" to quantitative comparison of proteins from complex biological samples, it is apparent that the number of approaches for quantitation can be daunting for the result-oriented biologist. There have been many recent reviews on quantitative proteomic approaches, discussing the strengths and limitations of each. Unfortunately, there are few detailed methodological descriptions of any one of these quantitative approaches. Here we present a detailed bioinformatics workflow for one of the simplest, most pervasive quantitative approach-spectral counting. The informatics and statistical workflow detailed here includes newly available freeware, such as SePro and PatternLab which post-process data according to false discovery rate parameters, and statistically model the data to detect differences and trends.
authors.affiliation: Department of Plant Biology, Institute of Biology, University of Campinas, Campinas, SP, Brazil.
authors.forename: Tiago S
authors.initials: TS
authors.lastname: Balbuena
authors.forename: Diogo Ribeiro
authors.initials: DR
authors.lastname: Demartini
authors.forename: Jay J
authors.initials: JJ
authors.lastname: Thelen
mesh_terms: D000595:Amino Acid Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D013058:Mass Spectrometry; D010455:Peptides / Q000378:metabolism; D020543:Proteome / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D010455:Peptides; D020543:Proteome