Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments
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, calle...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-06-01
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| Series: | Biology |
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| Online Access: | http://www.mdpi.com/2079-7737/3/2/383 |
| _version_ | 1797727032198561792 |
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| author | Lisa M. Chung Christopher M. Colangelo Hongyu Zhao |
| author_facet | Lisa M. Chung Christopher M. Colangelo Hongyu Zhao |
| author_sort | Lisa M. Chung |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T10:54:02Z |
| format | Article |
| id | doaj.art-f36bf9c75ffd45c1b8e0a47551ca0914 |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-12T10:54:02Z |
| publishDate | 2014-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-f36bf9c75ffd45c1b8e0a47551ca09142023-09-02T06:41:01ZengMDPI AGBiology2079-77372014-06-013238340210.3390/biology3020383biology3020383Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) ExperimentsLisa M. Chung0Christopher M. Colangelo1Hongyu Zhao2Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USAKeck Foundation Biotechnology Resource Laboratory, Yale School of Medicine, New Haven, CT 06510, USADepartment of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USAMultiple 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.http://www.mdpi.com/2079-7737/3/2/383multiple reaction monitoringlabel-freequality assessmentdata normalizationproteomicspeptidetransition |
| spellingShingle | Lisa M. Chung Christopher M. Colangelo Hongyu Zhao Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments Biology multiple reaction monitoring label-free quality assessment data normalization proteomics peptide transition |
| title | Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments |
| title_full | Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments |
| title_fullStr | Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments |
| title_full_unstemmed | Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments |
| title_short | Data Pre-Processing for Label-Free Multiple Reaction Monitoring (MRM) Experiments |
| title_sort | data pre processing for label free multiple reaction monitoring mrm experiments |
| topic | multiple reaction monitoring label-free quality assessment data normalization proteomics peptide transition |
| url | http://www.mdpi.com/2079-7737/3/2/383 |
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