ArticleMolecular & cellular proteomics : MCP2012
Protein significance analysis in selected reaction monitoring (SRM) measurements.
Article in Molecular & cellular proteomics : MCP, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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77 citing papers in PubMed, 141 citations in OpenAlex.
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- Harmonization of quality metrics and power calculation in multi-omic studies.Nature communications · 2020Article
- High-Throughput Assessment of Kinome-wide Activation States.Cell systems · 2019Article
- Proteomics advances for precision therapy in ovarian cancer.Expert review of proteomics · 2019Review
- Unraveling the functional role of the orphan solute carrier, SLC22A24 in the transport of steroid conjugates through metabolomic and genome-wide association studies.PLoS genetics · 2019Article
- Improving Precursor Selectivity in Data-Independent Acquisition Using Overlapping Windows.Journal of the American Society for Mass Spectrometry · 2019Article
- Analysis of significant protein abundance from multiple reaction-monitoring data.BMC systems biology · 2018Article
- Clinical Features of Psoriatic Arthritis: a Comprehensive Review of Unmet Clinical Needs.Clinical reviews in allergy & immunology · 2018Review
- Application of targeted mass spectrometry in bottom-up proteomics for systems biology research.Journal of proteomics · 2018Review
- Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial.Molecular systems biology · 2018Review
- MS analysis of a dilution series of bacteria:phytoplankton to improve detection of low abundance bacterial peptides.Scientific reports · 2018Article
- Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry.BMC bioinformatics · 2018Article
- Comparison of the Whole Cell Proteome and Secretome of EpidemicFrontiers in microbiology · 2018Article
- A mass spectrometry-based proteomic analysis of Homer2-interacting proteins in the mouse brain.Journal of proteomics · 2017Article
- Definition of a RACK1 Interaction Network inG3 (Bethesda, Md.) · 2017Article
17 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that provides sensitive and accurate protein detection and quantification in complex biological mixtures. Statistical and computational tools are essential for the design and analysis of SRM experiments, particularly in studies with large sample throughput. Currently, most such tools focus on the selection of optimized transitions and on processing signals from SRM assays. Little attention is devoted to protein significance analysis, which combines the quantitative measurements for a protein across isotopic labels, peptides, charge states, transitions, samples, and conditions, and detects proteins that change in abundance between conditions while controlling the false discovery rate. We propose a statistical modeling framework for protein significance analysis. It is based on linear mixed-effects models and is applicable to most experimental designs for both isotope label-based and label-free SRM workflows. We illustrate the utility of the framework in two studies: one with a group comparison experimental design and the other with a time course experimental design. We further verify the accuracy of the framework in two controlled data sets, one from the NCI-CPTAC reproducibility investigation and the other from an in-house spike-in study. The proposed framework is sensitive and specific, produces accurate results in broad experimental circumstances, and helps to optimally design future SRM experiments. The statistical framework is implemented in an open-source R-based software package SRMstats, and can be used by researchers with a limited statistics background as a stand-alone tool or in integration with the existing computational pipelines.
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