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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Decoy methods for assessing false positives and false discovery rates in shotgun proteomics
Guanghui Wang1, Wells W Wu, Zheng Zhang
1Proteomics Core Facility, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
Analytical Chemistry
|December 9, 2008
Summary
Understanding false positives (FPs) in proteomics is key. This study reveals how decoy database methods, FP calculation, and search strategies impact FP and false discovery rate (FDR) estimations in peptide-spectrum matches (PSMs).
Area of Science:
- Proteomics
- Bioinformatics
- Mass Spectrometry
Background:
- False positives (FPs) in peptide-spectrum matches (PSMs) are a recognized challenge in proteomic database searching.
- The target-decoy approach is widely used to control FPs and false discovery rate (FDR).
- Variations exist in decoy construction, rate calculation, and search strategies within the target-decoy approach.
Purpose of the Study:
- To evaluate the impact of different target-decoy strategy implementations on FP and FDR estimations.
- To compare decoy construction methods (reversing vs. stochastic), FP calculation (total vs. unique PSMs), and search strategies (separate vs. composite).
- To provide insights for optimizing proteomic biomarker discovery.
Main Methods:
- Utilized a rat kidney protein sample and the SEQUEST search engine.
- Compared sequence reversing and stochastic decoy construction methods.
- Analyzed estimations using total versus unique PSMs for FDR calculation.
- Contrasted separate and composite database searches.
- Validated findings with a standard protein mixture.
Main Results:
- Stochastic decoy construction yielded higher FP/FDR estimates than sequence reversing, especially with single filters.
- Multiple filters diminished differences between decoy construction methods.
- Unique PSM-based FDR/FP estimations were nearly double those using total PSMs.
- Composite searches resulted in approximately threefold lower FDR estimates than separate searches.
- Separate searches could overestimate FPs, correctable by merging procedures.
Conclusions:
- Decoy construction, FP calculation, and search strategy significantly influence FP and FDR estimations in PSMs.
- Multiple filtering criteria reduce dependency on specific decoy construction methods.
- Composite searches offer a more accurate FDR estimation than separate searches.
- Understanding these implementation differences is crucial for reliable proteomic biomarker discovery.
