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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

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MRMPROBS suite for metabolomics using large-scale MRM assays.

Hiroshi Tsugawa¹, Mitsuhiro Kanazawa², Atsushi Ogiwara²

¹RIKEN Center for Sustainable Resource Science, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan Department of Biotechnology, Graduate School of Engineering, Osaka University, Suita, Osaka 565-0871, Japan Reifycs Incorporated, 1-6-12 Nishishinbashi, Minato-ku, Tokyo 105-0003, Japan and National Institute of Genetics, Mishima, Shizuoka 411-8540, JapanRIKEN Center for Sustainable Resource Science, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan Department of Biotechnology, Graduate School of Engineering, Osaka University, Suita, Osaka 565-0871, Japan Reifycs Incorporated, 1-6-12 Nishishinbashi, Minato-ku, Tokyo 105-0003, Japan and National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan.

Bioinformatics (Oxford, England)

|April 23, 2014

Summary

This study introduces new software for analyzing large-scale metabolome data from multiple reaction monitoring (MRM) assays. The tool offers interactive visualization and a standard library for reliable metabolite identification.

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Area of Science:

Biochemistry
Bioinformatics
Analytical Chemistry

Background:

Metabolome analysis of large-scale multiple reaction monitoring (MRM) assays presents computational challenges.
Standardized software environments are needed for efficient data processing and analysis.

Purpose of the Study:

To develop a novel software environment for comprehensive metabolome analysis of large-scale MRM assays.
To provide an integrated pipeline from raw data import to high-dimensional statistical analysis.

Main Methods:

Developed a software environment supporting major mass spectrometer vendor data formats and mzML.
Implemented a graphical user interface for interactive peak quantification, missing value interpolation, and normalization.
Integrated an MRM standard library with 301 metabolites and 775 transitions for reliable peak identification.

Main Results:

The software facilitates processing of large-scale MRM assay data.
Interactive visualization and quality control sample-based normalization enhance data reliability.
The MRM standard library aids in accurate peak identification using retention time and ion abundances.

Conclusions:

The developed software provides an efficient and reliable platform for metabolome analysis.
The integrated approach, including a standard library, improves the accuracy and ease of MRM data interpretation.