mProphet: automated data processing and statistical validation for large-scale SRM experiments

By Lukas Reiter, Oliver Rinner, Paola Picotti, Ruth Hüttenhain, Martin Beck, Mi Youn Kim Brusniak, Michael O. Hengartner, R. Aebersold. Nature Methods, 2011

512 citations recorded by the source corpus.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/8b97b0061f3de4b87831edf38fcedb1baee1756d

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Citations in the corpus

DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics (2015, methodology): “For LDA model training, 50% of all matches in which S is a decoy spectrum are randomly selected and labeled as negative training data (the other 50% are held away from the training; these can be used at the final stage to assess the quality of the model fitted with the mixture modeling algo-rithm de”
The Skyline Ecosystem: Informatics for Quantitative Mass Spectrometry Proteomics (2017, methodology): “Many of these scoring features used in the Skyline default peak picking strategy are similar to those used in the mProphet method (Reiter et al. 2011).”
Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics (2015, unknown): “Statistical methods can provide conﬁdence metrics that the targeted peptide ion has truly been detected [1], as well as abundance uncertainty measures.”
Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues (2015, methodology): “The extracted ion currents of candidates can be directly visualized and manually validated like in SRM, i.e. a peptide from P2666 could be quantified down to 100 attomol on column (supplemental Fig.”
Parallel Reaction Monitoring for High Resolution and High Mass Accuracy Quantitative, Targeted Proteomics* (2012, background): “Through use of theoretical calculations, the high specificity of high mass accuracy data lays the groundwork for a generalized and statistically sound detection algorithm for reaction monitoring experiments that incorporates probability of correctness measures, as well as obviates the need for manua”
Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* (2012, methodology): “Data analysis in targeted proteomics essentially consists of computing the likelihood that a group of transition signal traces are derived from the targeted peptide (9).”
Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial (2018, unknown): “During this scoring process, a variety of individual scores is computed (Reiter et al , 2011).”
Machine learning in cell biology – teaching computers to recognize phenotypes (2013, background): “…Murphy, 2011), DNA sequence analysis (Castelo and Guigo´, 2004; Ben-Hur et al., 2008) and proteomics (Yang and Chou, 2004; Datta and Pihur, 2010; Reiter et al., 2011), as well as in many other fields outside of biology, such as speech (Rabiner, 1989) and face recognition (Viola and Jones,…”
A multi-center study benchmarks software tools for label-free proteome quantification (2016, background): “More than 99% of the 6,826 peptides in this library were detectable by at least two library-based tools (Supplementary Figure 20), which picked the same respective peaks as DIA-Umpire in 98% of the cases (Supplementary Figure 21).”
IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs (2020, methodology): “Then, we reintegrated the peaks by training a model with the built-in mProphet (44).”