Open source system for analyzing, validating, and storing protein identification data.

By R. Craig, J. Cortens, Ronald C. Beavis. Journal of Proteome Research, 2004

682 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/01343635ef32c175320dd54dcf33f534fd67b0ec

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Citations in the corpus

Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics (2015, methodology): “When this is performed, the data can be reused by other researchers and reanalyzed as part of concerted efforts to define MS-observable proteomes, such as PeptideAtlas [37, 38], PRIDE Cluster [39], and GPMdb [40].”
PaxDb, a Database of Protein Abundance Averages Across All Three Domains of Life (2012, methodology): “Finally, GPMDB, PeptideAtlas and MOPED are repositories that have the additional aim of re-processing submitted raw data in a consistent way (14, 15, 17).”
Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database (2012, background): “The Global Proteome Machine (9) and PeptideAtlas (10, 11) are two of the earliest such collections, with the primary goal of providing a collection of peptide identifications.”
The ProteomeXchange consortium at 10 years: 2023 update (2022, neutral): “Additionally, GPMDB (36) has been providing to the community re-analysed peptide, protein and PTM identification data for > 15 years.”
The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition (2016, background): “There are other resources outside PX dedicated to the reanalysis of MS proteomics data, for instance GPMDB (7) and proteomics DB (8).”
DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics (2015, neutral): “Tandem 20 , Comet 21 and MSGF+ (ref.”
The PRIDE database and related tools and resources in 2019: improving support for quantification data (2018, background): “In addition, using the previously mentioned resource OmicsDI, it is now possible to trace the number of re-analyses of PRIDE datasets performed by PeptideAtlas and GPMDB and the number of direct citations of PRIDE datasets in the literature (BioRxiv: https://doi.org/10.1101/282517).”
Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* (2009, background): “Data repositories have been created to store the vast amount of mass spectrometric data (Craig, Cortens et al. 2004; Desiere, Deutsch et al. 2005; Martens, Hermjakob et al. 2005; King, Deutsch et al. 2006).”
Interpretation of Shotgun Proteomic Data (2005, methodology): “ent experiments (41, 51, 91), this would allow new uses of”
Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures (2009, methodology): “These include the crosscorrelative approaches of SEQUEST [18] and Global Proteome Machine [19] and the probability-based strategies used by MASCOT [20] and ProteinLynx GlobalSERVER [21].”
Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell‐lines (2015, methodology): “The same set of criteria were then applied to ﬁlter recent datasets stored in three large proteomics data repositories: PRIDE [9], GPMDB [12], and PeptideAtlas [10].”