PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows

By Eric W. Deutsch, Henry H. N. Lam, R. Aebersold. EMBO Reports, 2008

547 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/6516bca3166382d61851ffc03003f0eaf9191c49

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Citations in the corpus

Parallel Reaction Monitoring for High Resolution and High Mass Accuracy Quantitative, Targeted Proteomics* (2012, unknown): “Additionally, through community effort, several publicly available databases of tandem MS spectra (33-39) and validated SRM assays (40,41) are available to provide empirical guides to transition selection and assay development.”
Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics (2009, methodology): “Proteins that could not be detected by previous proteomic (Deutsch et al., 2008) or affinity-based (Ghaemmaghami et al., 2003) techniques were added to the list.”
The ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics (2019, background): “Additionally, PeptideAtlas (5) participated as a resource that re-analyzed public submitted datasets.”
A Guided Tour of the Trans-Proteomic Pipeline (2010, methodology): “The raw and processed data are available in the PeptideAtlas [21] raw data repository as PAe001337.”
The Proteomics Identifications (PRIDE) database and associated tools: status in 2013 (2012, background): “Unlike other MS proteomics resources, such as PeptideAtlas (9) and the Global Proteome Machine Database (GPMDB) (10), no reprocessing of the data is performed because PRIDE aims to reﬂect the author’s analysis view on the experimental data.”
Target-Decoy Search Strategy for Mass Spectrometry-Based Proteomics (2010, background): “Alternatively, several public sources of MS/MS spectra are freely available on the internet, including PeptideAtlas (6) and the Open Proteomics Database (7).”
The PRIDE database and related tools and resources in 2019: improving support for quantification data (2018, background): “The evolution in the percentage of research articles supported by PRIDE datasets (in three different proteomics journals: Molecular and Cellular Proteomics, Journal of Pro-teome Research and Proteomics ) is explained in Supplementary Note 4.”
2016 update of the PRIDE database and its related tools (2015, background): “Resources such as GPMDB and PeptideAtlas have been reanalyzing PRIDE data sets for many years already, for instance in the context of the Human Proteome Project (49).”
Widespread Protein Aggregation as an Inherent Part of Aging in C. elegans (2010, background): “elegans proteins detected in mass spectrometry experiments available in PeptideAtlas [66].”
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].”
The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences (2021, background): “This is taking place in the context of the ‘PTMeXchange’ project, in collaboration with the PeptideAtlas team and the University of Liverpool.”
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.”
Systems medicine: the future of medical genomics and healthcare (2009, background): “Recent results indicate that the description of complete reference proteomes is now within reach in advanced centers, using multiple reaction monitoring combined with mass spectrometry; this combination is the most powerful and rapid targeted approach currently available [60-62].”
PaxDb, a Database of Protein Abundance Averages Across All Three Domains of Life (2012, methodology): “The PeptideAtlas Builds are the result of aggregations over multiple proteomics data sets stored in PeptideAltas, and the data has been reprocessed there via standardized database searches and peptide scoring (for some organisms, we average over two available builds to strike a balance between cover”