ArticleEURASIP journal on bioinformatics & systems biology2014
Modeling and systematic analysis of biomarker validation using selected reaction monitoring.
Article in EURASIP journal on bioinformatics & systems biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Bayesian Classification of Proteomics Biomarkers from Selected Reaction Monitoring Data using an Approximate Bayesian Computation-Markov Chain Monte Carlo Approach.Cancer informatics · 2018Review
- Identification of temperature regulated factors ofAIMS microbiology · 2017Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiscovery and validation of protein biomarkers with high specificity is the main challenge of current proteomics studies. Different mass spectrometry models are used as shotgun tools for the discovery of biomarkers. Validation of a set of selected biomarkers from a list of candidates is an important stage in the biomarker identification pipeline. Validation is typically done by triple quadrupole (QQQ) mass spectrometry (MS) running in selected reaction monitoring (SRM) mode. Although the individual modules of this pipeline have been studied, there is little work on integrating the components from a systematic point of view.
resultsThis paper analyzes the SRM experiment pipeline in a systematic fashion, by modeling the main stages of the biomarker validation process. The proposed models for SRM and protein mixture are then used to study the effect of different parameters on the final performance of biomarker validation. Sample complexity, purification, peptide ionization, and peptide specificity are among the parameters of the SRM experiment that are studied. We focus on the sensitivity of the SRM pipeline to the working parameters, in order to identify the bottlenecks where time and energy should be spent in designing the experiment.
conclusionsThe model presented in this paper can be utilized to observe the effect of different instrument and experimental settings on biomarker validation by SRM. On the other hand, the model would be beneficial for optimization of the work flow as well as identification of the bottlenecks of the pipeline. Also, it creates the required infrastructure for predicting the performance of the SRM pipeline for a specific setting of the parameters.
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