Analysis and validation of proteomic data generated by tandem mass spectrometry

By Alexey I. Nesvizhskii, Olga Vitek, R. Aebersold. Nature Methods, 2007

678 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/e7efccfabb3f21c9836c9be8ba397e547a01b078

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Citations in the corpus

Spectral Probabilities and Generating Functions of Tandem Mass Spectra: a Strike Against Decoy Databases (2008, methodology): “To avoid a confusion, we follow the terminology from the recent review 1 and use the term FPR (and the related term Spectral Probability deﬁned below) in the remainder of this paper.”
Correlation of mRNA and protein in complex biological samples (2009, neutral): “A large variety of MS data analysis software packages exist, also comprising statistical tools for data analysis (for an overview see [81,83–85]).”
Less label, more free: Approaches in label‐free quantitative mass spectrometry (2011, background): “algorithms aim to be as specific as possible, they cannot eliminate the possibility of false positives in the output data set so further analysis is required to minimise false discovery rates [77].”
Revisiting biomarker discovery by plasma proteomics (2017, background): “In this way, 2-h runs quantified an average of 786 mouse proteins, although it should be noted that proper FDR criteria for inferring peptide identities in the complex DIA MS/MS spectra are still being discussed (Nesvizhskii et al, 2007; Bruderer et al, 2017; Rosenberger et al, 2017).”
A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets (2015, methodology): “To achieve this, proteins are digested into peptides, analyzed by LC-MS/MS, and tandem mass spectra are used to interrogate protein sequence databases using search engines that match experimental data to data generated in silico (12, 13).”
Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (2009, neutral): “Tandem) (Nesvizhskii, Vitek et al. 2007).”
Sorting Signals, N-Terminal Modifications and Abundance of the Chloroplast Proteome (2008, background): “Additional (but far more costly and labor intensive) techniques, such as titration with known amounts of stable isotope labeled ‘proteotypic’ peptides uniquely representing each proteins will be needed to obtain precise quantifications (reviewed in [20]).”
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation (2010, background): “High-throughput genomic experiments often lead to the identification of large gene lists [1,2,3].”
iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011, methodology): “Without a uniformly applied confidence measure it was difficult and unreliable to combine and compare data sets (4, 5).”
IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs (2020, methodology): “Various approaches to LC-MS data acquisition (1-3) have been developed, among which data-dependent acquisition (DDA) remains the most commonly used strategy (4).”
Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S (2008, methodology): “MS-based shotgun proteomics is currently the most commonly used approach for the identification and quantification of proteins in large scale studies (1, 2).”