A model for random sampling and estimation of relative protein abundance in shotgun proteomics.

By Hongbin Liu, Rovshan G. Sadygov, John R. Yates III. Analytical Chemistry, 2004

2,523 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/496d1a7ffd07e4d6ffe0067cac5329cf0a6b25ea

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Citations in the corpus

Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, background): “Repeated sequencing and sequential extraction in numerous buffer conditions can alleviate this problem [9, 70, 71].”
A tissue-specific atlas of mouse protein phosphorylation and expression. (2010, positive): “Total spectral counts (TSCs) were used to approximate each site’s abundance within each tissue (Liu et al., 2004).”
Exponentially Modified Protein Abundance Index (emPAI) for Estimation of Absolute Protein Amount in Proteomics by the Number of Sequenced Peptides per Protein*S (2005, background): “Recently two groups independently presented similar curves relating the number of peptides to the concentration of proteins (19, 27).”
Mass Spectrometry-Based Label-Free Quantitative Proteomics (2009, background): “9997) with a dynamic range over 2 orders of magnitude [25].”
Sorting Signals, N-Terminal Modifications and Abundance of the Chloroplast Proteome (2008, background): “It has been shown in DDA LC-MS-based analyses of digested protein mixtures that the number of MS/MS of spectral counts (SPC) correlates with protein abundance [15–18].”
Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis (2010, background): “Among the more than 50 serine hydrolases detected in this analysis (Tables S1-3), two enzymes, KIAA1363 and MAGL, were found to be consistently elevated in aggressive cancer cells relative to their non-aggressive counterparts, as judged by spectral counting (Jessani et al., 2005; Liu et al., 2004).”
Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry (2005, background): “Ad-ditionally, due to the biased under-sampling nature of MS/MS ion selection (15), it is impossible to sequence every peptide in a sample in a single-pass analysis.”
Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (2009, background): “(41), and targeted proteomics approaches based on the”
Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* (2012, background): “It does, however, have limited quantification capabilities on large sample sets because of stochastic and irreproducible precursor ion selection (11) and under-sampling (12).”
A Protein Complex Network of Drosophila melanogaster (2011, background): “The number of TSCs correlates roughly with protein abundance in a sample (Liu et al., 2004) and, thus, increases the sensitivity of our approach by providing a semiquantitative dimension to the score.”
Analysis of the myosinII-responsive focal adhesion proteome reveals a role for β-Pix in negative regulation of focal adhesion maturation (2011, background): “These may be undiscovered, weakly associated or transient focal adhesion proteins, or cytosolic and/or cytoskeletal contaminants, and were classified according to biological function: endomembrane systems/trafficking (129), uncharacterized (84), cytoskeleton (81), metabolism (70), plasma membrane ch”
Interpretation of Shotgun Proteomic Data (2005, methodology): “It should also be mentioned that although the discussion here is centered on the quantitative proteomic approach based on isotopic labeling, it applies equally to semiquantitative methods based on simple peptide counts (70, 71) or on peptide ion current profiling (72, 73).”