Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation

By Peng Lu, Christine Vogel, Rong Wang, Xin Yao, Edward M. Marcotte. Nature Biotechnology, 2007

1,190 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/770a434f16248dd02ca2c3f2cdc059ee0e7d3543

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Citations in the corpus

Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, methodology): “APEX has been shown to successfully produce biologically meaningful data when used to assess differentially expressed proteins from yeast grown in either rich or minimal media and has demonstrated estimations of protein abundance for proteins over 3–4 orders of magnitude using an ion trap mass spect”
Analysis and validation of proteomic data generated by tandem mass spectrometry (2007, methodology): “Still, the practical utility of this method has been demonstrated in a number of application”
Mass Spectrometry-Based Label-Free Quantitative Proteomics (2009, background): “Recently, a modified spectral counting strategy termed absolute protein expression (APEX) profiling was developed to measure the absolute protein concentration per cell from the proportionality between the protein abundance and the number of peptides observed [39].”
Large‐scale image‐based screening and profiling of cellular phenotypes (2017, background): “For example in the budding yeast, it was estimated that (cid:4) 4,000–6,000 protein molecules are translated per mRNA molecule (10,11).”
Moderated statistical tests for assessing differences in tag abundance (2007, background): “EB represents the small sample test of Robinson and Smyth (2007) with the moderated dispersion estimates of this article.”
Insights into the regulation of protein abundance from proteomic and transcriptomic analyses (2012, neutral): “Both techniques enable the absolute quantification of proteins in the sample, providing that the resulting intensities or counts are suitably calibrated to molecular concentration”
Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial (2018, neutral): “…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 et al , 2007).”
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].”
Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line (2010, methodology): “Mechanism (iv) is accounted for by the quantitative proteomics method (Lu et al, 2007) (Supplementary Section 1.2; Supplementary Figures S6 and S7).”