Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations

By Joshua E. Elias, W. Haas, Brendan K. Faherty, Steven P. Gygi. Nature Methods, 2005

684 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/42ba2eab39183277d4de47fe8083169abe3bb02a

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Citations in the corpus

Analysis and validation of proteomic data generated by tandem mass spectrometry (2007, background): “Although there is currently no theory on the optimality of the score, empirical evidence suggests that some scores perform better than others in different setting”
A probability-based approach for high-throughput protein phosphorylation analysis and site localization (2006, background): “Balancing precision and sensitivity is often a major consideration in large-scale proteomics experiment”
Analysis of the myosinII-responsive focal adhesion proteome reveals a role for β-Pix in negative regulation of focal adhesion maturation (2011, background): “To be included in a 'detectable list' required the presence of one or more sequence counts and a false positive protein identification rate of less than 1% (ref”
A HUPO test sample study reveals common problems in mass spectrometry-based proteomics (2009, neutral): “We identified discrepancies between the submitted results (Supplementary Table 12) and the centrally reprocessed results (Supplementary Table 15) for labs 2, 4, 5, 8, 10, 11, 16, 19, 20, 21, 22R, 24 and CR, largely due to the different data analysis strategies that these labs used.”
Molecular anatomy of a trafficking organelle. (2006, methodology): “To determine the falsediscovery rate (Elias et al., 2005), the data (PKL files) were searched against a randomized NCBInr database.”
Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures (2009, methodology): “The search parameters were kept as similar as possible for all algorithms, and a reversed database was used to estimate the rate of false positive identifications [26, 38].”
A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics (2010, methodology): “depending on the type of instrument used to generate the data) [22-24].”
The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins (2006, methodology): “To test experimentally the false-positive rate in our dataset, we performed a decoy database search [29].”
Sorting Signals, N-Terminal Modifications and Abundance of the Chloroplast Proteome (2008, background): “[67], and also underscores the reproducibility of the stromal preparations.”
Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry (2007, background): “dtd" [ ]> One of the most daunting tasks researchers face when conducting mass spectrometry–based proteomics investigations is the correct selection of the 10–50% of spectrum assignments generated in LC-MS/MS experiments that are actually correc”
Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. (2006, methodology): “01), we performed a MASCOT search against a concatenated target/decoy database (Elias et al., 2005) consisting of a combined forward and reverse version of the IPI human database.”