Program download

Main program is available from here. MRMPROBS program.

The file converter is available from here. File converter.

FAQ for ABF file converter.

MRM database is provided. MRM database.

Demonstration files and the reference library can be downloaded from here. MRM, data independent MS/MS acqusition (DIA), GCMS scan data.

MRMPROBS tutorial is downloadable from here. MRMPROBS tutorial.

MRM-DIFF tutorial is downloadable from here. MRM-DIFF tutorial.

Demonstration files and the reference library for MRM-DIFF can be downloaded from here. MRM-DIFF demo data.


MRMPROBS is launched as a universal program for targeted metabolomics using not only multiple reaction monitoring (MRM)- or selected reaction monitoring (SRM) but also SCAN and data independent MS/MS acquisition (DIA) data. Our objective is to develop a data processing tool for widely targeted metabolomics by means of mass spectrometers such as QqQ-MS, DIA-MS, and just single Q-MS. Although the strategy became increasingly popular for the simultaneous analysis of up to several hundred metabolites at high sensitivity, selectivity, and quantitative capability, software development for the data analysis of MRM transitions lags behind in metabolomics: data assessment usually relies on manual evaluation due to the lack of automated probabilistic measures. Here, we developed a software program, MRMPROBS (Multiple Reaction Monitoring based PROBabilistic System for widely targeted metabolomics), written in C# language for widely targeted metabolomics. It evaluates the metabolite peaks by posterior probability, defined as the odds ratio by means of a newly optimized multivariate logistic regression model, and visualizes large-scale MS data sets with user-friendly graphical user interfaces to allow data curation and statistical analyses.

Please cite one of the following papers

  • MRM-DIFF: Data Processing Strategy for Differential Analysis in Large Scale MRM-based Lipidomics Studies. Frontiers in Genetics, 5:471, 2014 [PubMed]
  • MRMPROBS Suite for metablomics using large-scale MRM assays. Bioinformatics, 30, 2379-2380, 2014 [PubMed]
  • MRMPROBS: A Data Assessment and Metabolite Identification Tool for Large-Scale Multiple Reaction Monitoring Based Widely Targeted Metabolomics. Analytical Chemistry, 85, 5191-5199, 2013 [PubMed]

Overview of MRMPROBS software

File converter
We currently utilize a freely available file format converter (offered from Reifycs Inc.) that converts raw data from each instrument to the Reifycs Analysis Base File (ABF) format.
We also support mzML file format converted by an open source file translator ProteoWizard . Data import from Waters(.raw) became possible indirectly via mzML.

Import .abf files(or .mzML files) and MRM transition library (compound library)
MRMPROBS imports ABF format files (.abf) from measured samples and the reference (i.e. MRM transition) library in the tab-delimited text format that contains the target compound name, retention time, target or qualifier, QT ratio (optional), and precursor and product m/z.
Edited in 14/3/31: The library format to deal with mzML files is slightly different from the original one. Please see our MRMPROBS manual.

Data processing
After ABF files and MRM transition library are imported, MRMPROBS detects and constructs the peak groups and calculates five scores (intensity-, retention time-, QT ratio-, shape-, and co-elution score) for each peak group by comparing the detected peak intensity, qualifier transition records, and reference information. Our peak detection method is based on the local maximum and local minimum of raw data points. Our software also accepts a peak group consisting of only one transition record. While this is not recommended, in such cases only two scores (the intensity- and the retention time score) are calculated. Then the probability is computed for each peak group by a multivariate regression model. The odds ratio is calculated as a posterior probability of a true peak given the five scores. A peak group of the highest probability is selected as a quantification value in the resulting data matrix. This process is repeated for all transition group records of all samples. The result can be manually confirmed and corrected as shown the below figure.

This program supports interpolation methods for missing values, normalization methods (internal standard, LOESS/Cubic-spline etc.), and principal component analysis.
Please also see our excel macro based statistical analysis tool page.
Back to Top