ArticleResearch in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- )2017
Progressive calibration and averaging for tandem mass spectrometry statistical confidence estimation: Why settle for a single decoy?
Article in Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search.Journal of proteome research · 2023Article
- The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data.Journal of proteome research · 2023Article
- Machine Learning Strategy That Leverages Large Data sets to Boost Statistical Power in Small-Scale Experiments.Journal of proteome research · 2020Article
- Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate.Journal of proteome research · 2019Article
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Authors and funding
2 authors.
Funding
Abstract
Estimating the false discovery rate (FDR) among a list of tandem mass spectrum identifications is mostly done through target-decoy competition (TDC). Here we offer two new methods that can use an arbitrarily small number of additional randomly drawn decoy databases to improve TDC. Specifically, "Partial Calibration" utilizes a new meta-scoring scheme that allows us to gradually benefit from the increase in the number of identifications calibration yields and "Averaged TDC" (a-TDC) reduces the liberal bias of TDC for small FDR values and its variability throughout. Combining a-TDC with "Progressive Calibration" (PC), which attempts to find the "right" number of decoys required for calibration we see substantial impact in real datasets: when analyzing the
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