@inbook {doi:https://doi.org/10.1002/9783527833092.ch3,
	title = {Data-Processing Workflow for Relative Quantification from Label-Free and Isobaric Labeling-Based Untargeted Shotgun Proteomics: From Database Search to Differential Expression Analysis},
	booktitle = {Capillary Electrophoresis-Mass Spectrometry for Proteomics and Metabolomics},
	year = {2022},
	pages = {65-119},
	publisher = {John Wiley \& Sons, Ltd},
	organization = {John Wiley \& Sons, Ltd},
	chapter = {3},
	abstract = {Summary This chapter aims to introduce the principal bases of the commonly used data processing workflow for identification and quantification of proteins, toward the goal of improving relative quantification from untargeted label-free or isobaric labeling workflows for shotgun proteomics. Parameters for database search and considerations for quantification workflows, especially imputation and normalization, will be discussed with examples from literature. As most of these data processing methods are well established or reviewed within the LC-MS proteomics literature, these methods should also be transferred and adapted to proteomics research using CE-MS. While this chapter is targeted toward newcomers to proteomics, some of the points are also raised for experts in the field to consider.},
	keywords = {DATABASE SEARCH, imputation, isobaric labeling, label-free proteomics, normalization, quantitative proteomics, relative quantification},
	isbn = {9783527833092},
	doi = {https://doi.org/10.1002/9783527833092.ch3},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9783527833092.ch3},
	author = {Zhong, Jenny J. and Morin, Gregg B. and Chen, David D. Y.}
}
@article {1238,
	title = {Statistical evaluation of electrospray tandem mass spectra for optimized peptide fragmentation},
	journal = {Journal of the American Society for Mass Spectrometry},
	volume = {16},
	number = {4},
	year = {2005},
	note = {ISI Document Delivery No.: 911RDTimes Cited: 6Cited Reference Count: 33},
	month = {Apr},
	pages = {505-514},
	type = {Article},
	abstract = {Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the method of choice for the analysis of complex peptide mixtures. It combines the separation power of nanoflow LC with highly specific sequence analysis, allowing automated peptide sequencing with high resolution and throughput. For peptide fragmentation, the current experimental setup uses predefined parameters based on the mass-to-charge ratio of the individual precursor. Suitable parameters are typically established by empirical evaluation of fragment spectra of individual peptides used as standards. As a result, nonoptimal fragment spectra are obtained if peptides show fragmentation behavior different from these standards, which often result in the loss of sequence-specific fragment ion information. Here we describe a statistical approach for the systematic evaluation of the quality of individual peptide fragment spectra based on the calculation of their arithmetic mean and standard deviation. The method utilizes the dependence of these parameters on the difference in electric potential across the collision cell to determine the value that results in maximum information content. We show that the method is applicable to fragment spectra generated from a variety of multiply-charged tryptic peptides, over a wide concentration range, and on different types of mass analyzers. We also show how this novel approach can be used to define optimized collision energy settings over a wide mass-to-charge range. (J Am Soc Mass Spectrom 2005,16,505-514) (c) 2005 American Society for Mass Spectrometry},
	keywords = {ALGORITHM, ASSISTED-LASER-DESORPTION/IONIZATION, COLLISION-INDUCED DISSOCIATION, DATABASE SEARCH, MIXTURES, PROTEIN IDENTIFICATION, PROTEOMICS, PROTONATED PEPTIDES, SPECTROMETRY},
	isbn = {1044-0305},
	url = {<Go to ISI>://000228021400010},
	author = {Rogalski, J. C. and Lin, M. S. and Sniatynski, M. J. and Taylor, R. J. and Youhnovski, N. and Przybylski, M. and Kast, J.}
}
