Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae.

By B. Zybailov, A. Mosley, Mihaela E. Sardiu, Michael K. Coleman, L. Florens, M. Washburn. Journal of Proteome Research, 2006

1,068 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/b6321e3a1734c1d18f78419ea30bff7f32fd5523

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Citations in the corpus

Who is who in litter decomposition? Metaproteomics reveals major microbial players and their biogeochemical functions (2012, methodology): “Thus, protein abundances were calculated based on the normalized spectral abundance factor (NSAF; Florens et al., 2006; Zybailov et al., 2006).”
Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “When using NSAF calculations, spectral count data become normally distributed after replacing zero SpC with a fraction of a spectral count and performing a log-transformation [41].”
High-fat diet alters gut microbiota physiology in mice (2013, methodology): “Protein abundances were estimated using NSAF (normalized spectral abundance factor) values calculated from the spectral counts of each individual identified protein (Zybailov et al., 2006).”
Label-free, normalized quantification of complex mass spectrometry data for proteomics analysis (2009, neutral): “NSAF is described by Zybailov et al 18 as where Spc is the spectral count for protein k and L is the length of protein k.”
Mass Spectrometry-Based Label-Free Quantitative Proteomics (2009, background): “Since large proteins tend to contribute more peptide/spectra than small ones, a normalized spectral abundance factor (NSAF) was defined to account for the effect of protein length on spectral count [28, 29].”
Comprehending crystalline β-carotene accumulation by comparing engineered cell models and the natural carotenoid-rich system of citrus (2011, neutral): “16 was added to every individual spectral count as practised before (Zybailov et al., 2006).”
Sorting Signals, N-Terminal Modifications and Abundance of the Chloroplast Proteome (2008, methodology): “The most promising methods of normalization have been protein length [41] or number of theoretical and relevant tryptic peptides, possibly corrected for their propensity to be observed [18,42].”
Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S (2008, methodology): “…spectral counts, including methods that explore weighted scoring by peptide match score (16), normalization by the number of potential peptide matches (17), peptide sequence length, overall experiment-wide abundance (18), or incorporation of the probability of identification into counting (19).”
Target identification with quantitative activity based protein profiling (ABPP) (2017, background): “In a label-free quantitative proteomics approach, protein abundance is correlated with either mass spectrometric signal intensities of peptides or the number of MS/MS spectra matches to peptides and proteins [86].”
Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial (2018, neutral): “…protein intensity values can be further normalized by a factor representative of the detectability of that protein, for example protein length (Zybailov et al , 2006), number of theoretical peptides generated (Schwanhausser et al , 2011) or a computed peptide detection probability score (Lu…”