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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

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Related Experiment Video

Updated: Jun 3, 2026

PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
08:43

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Published on: May 11, 2017

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

Lukas Reiter1, Oliver Rinner, Paola Picotti

  • 1Biognosys AG, Zurich, Switzerland.

Nature Methods
|March 23, 2011
PubMed
Summary
This summary is machine-generated.

Selected reaction monitoring (SRM) data analysis is often subjective. mProphet is a new automated system providing accurate error rates for targeted proteomics, improving data consistency and reliability.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Selected reaction monitoring (SRM) is a sensitive, reproducible, and accurate targeted mass spectrometry technique used in proteomics.
  • Current SRM data analysis relies on subjective manual inspection, hindering consistent analysis and objective error assessment.

Purpose of the Study:

  • To develop an automated system for accurate error rate computation in SRM data.
  • To enhance specificity and sensitivity in targeted proteomics by improving data analysis.

Main Methods:

  • Development of mProphet, a fully automated statistical system.
  • Integration of relevant data features into a statistical model for peptide identification.

Main Results:

  • mProphet computes accurate error rates for targeted peptide identification in SRM datasets.
  • The system maximizes specificity and sensitivity through objective data analysis.

Conclusions:

  • mProphet offers an objective and automated solution for SRM data analysis.
  • This approach improves the reliability and consistency of targeted proteomics studies.