An integrated software suite for
streamlining GC-MS metabolomic
data analysis (http://miolite2.iceht.forth.gr)
Dr. Maria I. Klapa (email@example.com; @mklapa10) ,
Head , Metabolic Engineering & Systems Biology Laboratory,
Institute of Chemical Engineering Sciences,
Foundation for Research & Technology – Hellas
(FORTH/ICE-HT), Patras, Greece
Citation: Ch. Maga-Nteve & M. I. Klapa. 2016. Streamlining
GC-MS metabolomic analysis using the M-IOLITE
software suite. IFAC-PapersOnline 49: 286-288
From the M-IOLITE initial window (Fig. 1), the user can select to upload
his/her raw or normalized GC-MS metabolic profile based on a
standardized template , along with details about the experimental design,
biological system and profiling method used (Fig. 2). During uploading,
the user is questioned for first encountered metabolite peak names, given
the option to select from expected names (in case of typos) or introduce
new unknown peaks tothe MESBL peak library, stored initially as
The MESBL metabolite peak library comprises (Fig.3):
(a)an in-house peak dataset of >900 peaks (429 reviewed) collected from
runs of standard compounds and multiple experiments in various
systems, based on the metabolite-centric profile analysis of 6 different
analysts, and annotated using commercial and publicly available
metabolite peak databases , extended by
(b)a revised and appropriately filtered by our group version of the public
GOLM peak dataset (http://gmd.mpimp- golm.mpg.de/).
Standardized GC-MS metabolic profile repository
based on validated metabolite peak library
M-IOLITE is a software suite for streamlining GC-MS metabolomic
data analysis, developed by the Metabolic Engineering & Systems
Biology Laboratory (MESBL), FORTH/ICE-HT, Patras, Greece and
supported by ELIXIR-GR structural funds.
a) a standardized GC-MS metabolomic profile repository based on a
validated metabolite derivative peak library of >900 peaks;
b) a module for specialized GC-MS metabolomic data validation,
normalization and filtering;
c) a module for unknown metabolite peak identification, based on
the MESBL peak library, reconstructed metabolic networks of
model organisms, meta-analysis of the M-IOLITE data repository
and pattern recognition analysis methods.
M-IOLITE is available as an executable file, freely provided to
academic users. Its third module will contribute to the ELIXIR
metabolomics case-study for unknown metabolite identification.
We acknowledge support of this work by the project “ELIXIR-GR: The Greek Research Infrastructure for Data Management &
Analysis in Life Sciences” (MIS 5002780) implemented under the “Action for Strengthening Research & Innovation
Infrastructures”, funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020)
and co-financed by Greece and the European Union (European Regional Development Fund).
EC FP7 STREPSYNTH project
no 613877 @ FORTH/ICE-HT
Fig. 1 :
M-IOLITE employs specialized GC-MS metabolic profile dataset
validation (QC), normalization & filtering methods (Fig.4), developed by
our group (Kanani & Klapa (2007) Metab. Eng. 9:39-51; Kanani et. al.
(2008). J Chromatogr. B 871: 191-201; Papadimitropoulos et. al. (2018)
Methods Mol. Biol. 1738: 133-147). It is noted that the employed QC
method is the only available that does not require the availability of QC
samples and can be applied a posteriori given the availability of at least
three technical replicates for some samples. The user can subsequently
upload the normalized dataset to the M-IOLITE profile repository.
The position of the known metabolites in a profile dataset within the
KEGG-annotated metabolic network of the selected organism can be
visualized in KEGG Atlas. Moreover, the user can get directly connected
to the TM4/MeV omic data analysis software (http://mev.tm4.org) and
use the normalized profile dataset in its stored format for further
multivariate statistical analysis.
Specialized GC-MS metabolomic data validation,
normalization & filtering methods
Fig. 2 : The metabolic profile dataset
Unknown metabolite identification module based on
profile pattern recognition and network analysis
M-IOLITE contains a module for unknown metabolite peak identification,
based on the educated integration of information from the extended
MESBL metabolite peak library, reconstructed metabolic networks of
model organisms, meta-analysis of the M-IOLITE data repository and
pattern recognition analysis methods (Fig. 5). Currently, the reconstructed
metabolic network in human (https://vmh.uni.lutia) is added, the
incorporation of additional model organisms is ongoing. The optimization
of this module is part of the ELIXIR metabolomics case-study.
Fig. 3 : The MESBL metabolite peak library.
Fig. 5 :
The workflow of the
identification module in
Fig. 4 : The M-IOLITE data