title: Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics.
abstract: How to process and analyze MS data to quantify and statistically compare protein abundances in bottom-up proteomics has been an open debate for nearly fifteen years. Two main approaches are generally used: the first is based on spectral data generated during the process of identification (e.g. peptide counting, spectral counting), while the second makes use of extracted ion currents to quantify chromatographic peaks and infer protein abundances based on peptide quantification. These two approaches actually refer to multiple methods which have been developed during the last decade, but were submitted to deep evaluations only recently. In this paper, we compiled these different methods as exhaustively as possible. We also summarized the way they address the different problems raised by bottom-up protein quantification such as normalization, the presence of shared peptides, unequal peptide measurability and missing data. This article is part of a Special Issue entitled: Plant Proteomics--a bridge between fundamental processes and crop production, edited by Dr. Hans-Peter Mock.
authors.affiliation: GQE-Le Moulon, INRA, Univ Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, F-91190 Gif-sur-Yvette, France.
authors.forename: Mélisande
authors.initials: M
authors.lastname: Blein-Nicolas
authors.affiliation: GQE-Le Moulon, INRA, Univ Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, F-91190 Gif-sur-Yvette, France. Electronic address: zivy@moulon.inra.fr.
authors.forename: Michel
authors.initials: M
authors.lastname: Zivy
mesh_terms: D018556:Crops, Agricultural / Q000737:chemistry / Q000378:metabolism; D013058:Mass Spectrometry / Q000379:methods*; D010455:Peptides / Q000737:chemistry / Q000378:metabolism; D010940:Plant Proteins / Q000032:analysis / Q000378:metabolism; D040901:Proteomics / Q000379:methods*
chemical_list: D010455:Peptides; D010940:Plant Proteins
keywords: Data processing; Mass spectrometry; Peptide; Statistics