Posterior error probabilities and false discovery rates: two sides of the same coin.

By Lukas Käll, John D. Storey, Michael J. MacCoss, William Stafford Noble. Journal of Proteome Research, 2008

326 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/560463880942d2ec1b760ec386d8c0c7c6cd8dbe

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Citations in the corpus

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics (2010, methodology): “[178] Kall L, Storey JD, MacCoss MJ, Noble WS.”
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences (2018, methodology): “Other methods have suggested using the cumulative average or the cumulative maximum of posterior probabilities for deﬁning the set of interesting features in high-throughput experiments include Leng et al. [10], Choi et al. [33], and Kall et al. [34].”
iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011, background): “Thus, additional modeling is necessary to calculate statistical measures more suitable for filtering of large collections of MS/MS database search results, such as FDR(48) and posterior peptide and protein probabilities (37, 49–51).”
Accurate and Sensitive Peptide Identification with Mascot Percolator (2009, methodology): “…score, the q -value metric, proposed by Storey and Tibshi-rani 20 in the ﬁeld of genomics, was applied to mass spectrometry by Ka¨ll et al. 19,21 The q -value can be understood as the minimal FDR at which a PSM is accepted, hence, enabling the association of speciﬁc q -values with any PSM in…”
A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets (2015, methodology): “This is achieved by calculating q-values or posterior error probabilities, e.g. using routines implemented in Maxquant (33) for Andromeda results and Percolator (24) or PeptideProphet (35), for Mascot results.”
A new FST‐based method to uncover local adaptation using environmental variables (2015, methodology): “2, BayeScan was less well calibrated, yielding higher FDRs than BayeScEnv under all scenarios and for both monogenic and polygenic selection.”
The UniProtKB guide to the human proteome (2016, background): “probability (PEP) of (lt)1% or a minimum PTM localization score (Ascore) of 19], only 26 497 unique peptides are validated, and 5197 phosphorylation sites are annotated in 4118 UniProtKB/Swiss-Prot entries (28, 29, 31).”