MARS (MetAbolomics ReSearch) is a vendor neutral desktop application software endowed with a Graphical User Interface (GUI) specifically developed for untargeted and semi-targeted LC-MS-based metabolomics and exposomics.

Differently form Lipostar, which was specifically designed for LC-MS based lipidomics with dedicated tools and workflows, MARS provides more general algorithms and investigation tools.

MARS fully covers all the steps required in LC-MS based untargeted and semitargeted metabolomics and exposomics analysis: instrument data conversion and processing, peak detection, statistical analysis, automated MS and/or MS/MS-based metabolite annotation, quantification, and biopathway analysis. Unique features have been developed in the software to improve annotation accuracy, including customizable identification of multiple adducts, automated in-source fragmentation detection, and in-silico MS/MS spectrum validation. Additionally, two MARS databases for exposomics (nitrosamines) and phytomics applications are available upon request.

Key features

Database generation

  • The MARS DB Manager module allows to generate customized databases based on internal data as well as automatically import data from The Human Metabolome Database (HMDB), MassBank of North America (MoNA), and Microbial Metabolites Database (MiMe). As already mentioned, two MARS databases for exposomics (nitrosamines) and phytomics applications are available upon request.


Data processing

Specific data processing algorithm:

  • Baseline and noise reduction
  • Peak extraction
  • Peak smoothing (Statistical Deconvolution Algorithm or Savitzky-Golay)
  • Signal-to-noise ratio
  • Retention time (RT) correction
  • Alignment
  • Deisotoping
  • Gap-filler (optional algorithm to reduce missing values in the data matrix)

A new peak detection algorithm for the processing of ion mobility spectrometry (IMS) data (IMS data are currently supported for Agilent, Waters, and Bruker).


Data matrix refinement

Several tools for data matrix refinement:

  • Filters (e.g., blank subtraction, frequency filter, etc)
  • Normalization by metadata (e.g., cell count, volume, weight)
  • Normalization by analysis-related data (e.g., standards, total Area, QC, etc)
  • Averaging over all replicates
  • Merging of positive and negative data matrices
  • Adduct clustering


Statistical analysis tool

MARS provides different analysis to investigate your data:

  • Fold-change analysis
  • Univariate statistical analysis (e.g., ANOVA)
  • Principal Component Analysis (PCA)
  • Consensus PCA
  • Partial Least Squares regression (PLS)
  • Partial Least Squares-Discriminant Analysis (PLS-DA)
  • Orthogonal Partial Least Squares (O-PLS)
  • Orthogonal Partial Least Squares-Discriminant Analysis (O-PLS-DA)
  • Linear Discriminant Analysis (LDA)


Trend Analysis

An hypothesis-driven approach based on Pearson correlation coefficient or hypothesis-free cluster analysis (K-means and Bisecting K-means) are supported in MARS to extract trends of interest among samples.


Metabolite Identification

A flexible approach for metabolite identification is provided in MARS. It includes:

  • A spectral matching approach for species included in the database (RT or CCS values, when available, can be used to improve the annotation accuracy)
  • High-throughput approaches to detect other adducts and in-source fragmentations
  • A MS/MS validator tool to re-check spectral matching assignation
  • Clustering algorithm for adducts and in-source fragments of a same metabolite
  • Tool for stable isotope labelling studies
  • A score and a level-based classification as index of identification accuracy
  • Preliminary search of xenobiotic metabolites



Specific functionalities are provided in MARS for relative and absolute quantification using internal and/or external standards.


Pathway Analysis

MARS includes a collection of 20 metabolic pathways obtained by integrating data from different reference sources (KEGG metabolic network and PathBank linked to HMDB) and literature. The software also supports the projection of the identification results on metabolic pathways for functional analysis. The metabolics pathways available in MARS are:

  • AAA biosynthesis
  • Alanine aspartate and glutamate metabolism
  • Arginine and proline metabolism
  • Arginine biosynthesis
  • Cysteine and methionine metabolism
  • Glycolysis and gluconeogenesis
  • GSH metabolism
  • Histidine metabolism
  • Lysine biosynthesis
  • Lysine degradation
  • N-glycan biosynthesis
  • Pentose phosphate pathway
  • Phenylalanine metabolism
  • Purine pathway
  • Pyrimidine metabolism
  • TCA cycle
  • Tryptophan metabolism
  • Tyrosine metabolism
  • Valine, leucine and isoleucine biosynthesis
  • Valine, leucine and isoleucine degradation


Data support
  • MARS supports the import of LC-MS and LC-MS/MS data from the following mass-spec vendors:
    • Agilent(*.d): AutoMS and full scan at multiple energies of collision (All Ions).
    • Waters(*.raw): MSe, HDMSe, DDA, and MSMS, SONAR.
    • Thermo(*.RAW): Ion-Trap and Orbitrap, Exactive, Q-Exactive, DDA and AIF.
    • Sciex(*.wiff): SWATH and IDA.
    • Bruker(*.d): QTof, FT-ICR, TIMS-TOF data dependent scan.
    • Shimadzu(*.lcd): QTof.
  • Ion mobility spectrometry (IMS) data are supported for Agilent(*.d), Waters(*.raw), and Bruker(*.d).
  • Agilent(*.d), Waters(*.raw), and Shimadzu(*.lcd) files can be directly imported.
  • Thermo(*.RAW), Bruker(*.d), and Sciex(*.swiff) files require the use of a converter downloadable from the instrument site.



Thermo requirements:

  • MSFileReader 3.1 SP3
  • MSFileReader 3.1 SP4

Bruker requirements:

  • CompassXtract package

Sciex requirements:

  • MMS+Wiff+Access+Patch+2-win64.exe

Additional libraries required are listed in the software manual

System requirement and installation

MARS can be installed only on a 64bit Windows operating system.

MARS Training documents – Version 1.0.3


Database Information

  • File name: db_PHYTO_240531
  • Number of compounds: 29,750
  • Classification: 10 main classes and 70 sub-classes
  • Number of MS/MS spectra: 10,826
  • Type of MS/MS spectra: rule-based fragmentation (virtual)
  • Details:
    • The database contains the structure, formula, exact mass, MS1 of 29,750 phytochemicals and 10,826 MS2 information.
    • The dataset of 29,750 phytochemicals was collected from four databases (KEGG, LipidMaps, HMDB, and PhenolExplorer) and classified into 10 main classes and 70 subclasses.
    • The MS2 rule-based fragmentation was applied to different subclasses of phytochemicals. In particular, it has been adopted for the classes of flavonoids, alkaloids, and phenolic acids and derivatives.
  • Nomenclature assignation: An identification code (ID) consisting of an alphanumeric string of four and different numbers is assigned to each phytochemical in the database. In addition, a common name is associated with each compound based on the common nomenclature used in KEGG, LipidMaps, HMDB, and PhenolExplorer databases.
  • Fragmentation rules: Fragmentation rules were coded from experimental fragmentation of phytochemicals collected from literature and from in-house acquired data.

Database Information

  • File name: db_nitrosamines_20240531
  • Number of compounds: 28,024
  • Classification: two classes (linear nitrosamines, cyclic nitrosamines)
  • Number of MS/MS spectra: 28,024
  • Type of MS/MS spectra: rule-based fragmentation (virtual)
  • Details:
    • The database contains the structure, formula, exact mass, MS1, and MS2 information for 28,024 nitrosamines. Both linear and cyclic nitrosamines are included in the database. In particular, the linear nitrosamines included in the database are 27,856, while the cyclic nitrosamines are 168.
    • Nitrosamines compounds derive from different data sources:
      • nitrosamines reported by Regulatory Agencies (e.g., EMA and FDA);
      • nitrosamines distributed by commercial suppliers;
      • nitrosamines generated in-silico.
    • Table 1. Number of entries included in the database from the different sources
      • Regulatory Agencies: 141
      • Commercial suppliers: 209
      • In silico generation: 27,674
      • Total number: 28,024
  • Nomenclature assignation:
    • An identification code (ID) consisting of an alphanumeric string of two letters and 7 numbers (i.e., NA0000001, NA0000002, NA0000003, etc.) is assigned to each nitrosamine in the database. In addition, a common name is associated with each compound.
    • The schematic common name for linear nitrosamines is NO(N-X/N-Y) where X and Y can represent:
      • aliphatic chains bonded to the N-nitroso group. Aliphatic chains are represented in the common name as “C:DB” where C is the number of carbon and DB is the number of double bonds in the chains. Example for N-nethylethylamine (NMEA), common name: NO(N-1:0/N-2:0).
      • substituent different from aliphatic chains bonded to the N-nitroso group. This kind of substituents are represented in the common name with an alphanumeric string. Example for N-nitrosodiphenylamine (NDPhA), common name: NO(N-Ph/N-Ph).
    • In contrast, the schematic common name for cyclic nitrosamines is NO(C-Z), where Z is an alpha-numeric string. Example for N-nitrosomorpholine (NMOR), common name: NO(C-MOR); N-nitrosopiperidine (NPIP), common name: NO(C-PIP); and N-nitrosopyrrolidine (NPYR), common name: NO(C-PYR).
  • Fragmentation rules: Fragmentation rules were coded from experimental fragmentation of nitrosamines collected from literature and from in-house acquired data.

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