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Biology (Basel). 2014 Jun 5;3(2):383-402. doi: 10.3390/biology3020383.

Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments.

Author information

1
Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA. lisa.chung@yale.edu.
2
Keck Foundation Biotechnology Resource Laboratory, Yale School of Medicine, New Haven, CT 06510, USA. christopher.colangelo@yale.edu.
3
Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA. hongyu.zhao@yale.edu.

Abstract

Multiple Reaction Monitoring (MRM) conducted on a triple quadrupole mass spectrometer allows researchers to quantify the expression levels of a set of target proteins. Each protein is often characterized by several unique peptides that can be detected by monitoring predetermined fragment ions, called transitions, for each peptide. Concatenating large numbers of MRM transitions into a single assay enables simultaneous quantification of hundreds of peptides and proteins. In recognition of the important role that MRM can play in hypothesis-driven research and its increasing impact on clinical proteomics, targeted proteomics such as MRM was recently selected as the Nature Method of the Year. However, there are many challenges in MRM applications, especially data pre‑processing where many steps still rely on manual inspection of each observation in practice. In this paper, we discuss an analysis pipeline to automate MRM data pre‑processing. This pipeline includes data quality assessment across replicated samples, outlier detection, identification of inaccurate transitions, and data normalization. We demonstrate the utility of our pipeline through its applications to several real MRM data sets.

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