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MetDAT: A modular and workflow-based free online pipeline for metabolite data processing, analysis and interpretation

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Ambarish Biswas at AgResearch
Ambarish Biswas
Kalyan Mynampati at National University of Singapore
Kalyan Mynampati
Shivshankar Umashankar at Singapore Centre on Environmental Life Sciences Engineering
Shivshankar Umashankar
Sheela Reuben at National University of Singapore
Sheela Reuben
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Analysis of high throughput metabolomics experiments is a resource-intensive process that includes pre-processing, pre-treatment and post-processing at each level of experimental hierarchy. We developed an interactive user-friendly online software called Metabolite Data Analysis Tool (MetDAT) for mass spectrometry data. It offers a pipeline of tools for file handling, data pre-processing, univariate and multivariate statistical analyses, database searching and pathway mapping. Outputs are produced in the form of text and high-quality images in real-time. MetDAT allows users to combine data management and experiment-centric workflows for optimization of metabolomics methods and metabolite analysis. MetDAT is available free for academic use from http://smbl.nus.edu.sg/METDAT2/. sanjay@nus.edu.sg
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[18:24 20/9/2010 Bioinformatics-btq436.tex] Page: 2639 2639–2640
BIOINFORMATICS APPLICATIONS NOTE Vol. 26 no. 20 2010, pages 2639–2640
doi:10.1093/bioinformatics/btq436
Systems biology Advance Access publication August 11, 2010
MetDAT: a modular and workflow-based free online pipeline for
mass spectrometry data processing, analysis and interpretation
Ambarish Biswas1, Kalyan C. Mynampati1, Shivshankar Umashankar1, Sheela Reuben1,
Gauri Parab2, Raghuraj Rao1, Velayutham S. Kannan1and Sanjay Swarup1,2,3,∗
1Singapore-Delft Water Alliance, National University of Singapore, Singapore 117576, 2Small Molecules Biology
Laboratory, Department of Biological Sciences, National University of Singapore, Singapore 117543 and 3NUS
Environmental Research Institute (NERI), #02-01, T-Lab Building (TL), 5A Engineering Drive 1, Singapore 117411
Associate Editor: Trey Ideker
ABSTRACT
Summary: Analysis of high throughput metabolomics experiments
is a resource-intensive process that includes pre-processing, pre-
treatment and post-processing at each level of experimental
hierarchy. We developed an interactive user-friendly online software
called Metabolite Data Analysis Tool (MetDAT) for mass spectrometry
data. It offers a pipeline of tools for file handling, data pre-processing,
univariate and multivariate statistical analyses, database searching
and pathway mapping. Outputs are produced in the form of text and
high-quality images in real-time. MetDAT allows users to combine
data management and experiment-centric workflows for optimization
of metabolomics methods and metabolite analysis.
Availability: MetDAT is available free for academic use from http://
smbl.nus.edu.sg/METDAT2/.
Contact: sanjay@nus.edu.sg
Received on May 3, 2010; revised on July 20, 2010; accepted on
July 25, 2010
1 INTRODUCTION
Metabolomics experiments conducted using mass spectrometry
produce spectral outputs amounting to gigabytes of data. This is
a result of elaborate experimental set-ups with several replicates,
time-series studies and different types of treatment parameters
for a single sample. Datasets from such experimental set-up can
be very complex. Querying information from several subsets of
complex datasets and deriving meaningful biological interpretation
is very challenging. To overcome this problem in data handling and
analysis, the metabolomics research community has recommended
a minimal set of reporting standards and general guidelines for data
analysis, interpretation and exchange of metabolomics experiments
(Fiehn et al., 2008; Goodacre et al., 2007; Sansone et al., 2007).
Data pre-processing generally involves removal of noise and
reduction of baseline of the raw data. The resulting data are
normalized and scaled for univariate and multivariate statistical
analysis. Visualization of the resultant output helps to understand and
interpret experimental results. A number of standalone data analysis
tools for preprocessing, statistical analysis and pathway mapping
(mzMine, XCMS, MassTrix, Metexplore, Bioconductor) are freely
available on the web (Jourdan et al., 2010; Katajamaa et al., 2007;
Lommen, 2009). Currently, these available data analysis tools do
∗To whom correspondence should be addressed.
not have the means to include information from experiment such
as hierarchical data structure. As a result, meaningful inferences
based on comparisons at the various levels of data structure are not
straightforward and their associated effects are not easily interpreted.
Most of the available tools require continuous interaction with
users in the programs leading to delayed output. Here, we present
Metabolite Data Analysis Tool (MetDAT) to address gaps in the
currently available software for mass spectrometry data analysis
in a systems-centric approach. MetDAT is a web-based tool with
an open and integrated system that performs data preprocessing,
analysis followed by database searches with filters to refine the
output. MetDAT also provides interactive and customizable modules
and user-driven analysis of data at hierarchical levels.
2 METHODS AND IMPLEMENTATION
2.1 Computational model and user interface
MetDAT is a platform-independent web application, which works well
with most widely used web browsers such as Mozilla Firefox (above 3.2),
Internet Explorer (above 6), Google Chrome and Safari. The web interface
of MetDAT is developed using HTML and CGI-Perl scripts. Computational
functions of MetDAT are written in Perl and R languages, while graphics
are generated using Gnuplot (Williams et al., 1993) and R packages (R
Development Core Team, 2008). The software is hosted on a server running
Red Hat Enterprise Linux Server release 5.4.
MetDAT accepts input data as tab-delimited text files in a single zipped
file which is then converted into a matrix by ‘Prepare Dataset’ module.
The standalone modules run seamlessly on uploaded data and show the
results instantaneously but these results are not saved on MetDAT’s server.
All MetDAT programs can be used without any log-in. However, logged-
in users have the benefit of creating custom workflows or using a default
one. The workflows are designed such that the data can be cleaned up,
analyzed, visualized and interpreted all in one smooth pipeline. Two default
workflows—one for differential metabolite analysis and another for method
optimization are provided in MetDAT. The uploaded datasets, workflows and
results are automatically stored on MetDAT server and can be retrieved at a
later time.
MetDAT has no restrictions on the number of files to be processed.
However, larger datasets would require longer processing time. This software
has a combination of 23 most commonly used programs by metabolomics
researchers for pre-processing, pre-treatment, data analysis, visualization,
metabolite database search and pathway mapping. Other programs like self
organizing map, Random forest and support vector machine are not available
in this version of the software. These programs have been streamlined such
that they can be used readily by beginners while providing ample controls
© The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org 2639
at National University of Singapore on October 26, 2010bioinformatics.oxfordjournals.orgDownloaded from
[18:24 20/9/2010 Bioinformatics-btq436.tex] Page: 2640 2639–2640
A.Biswas et al.
Fig. 1. Screen shot from MetDAT v2.0 software. Individual programs are
accessible by left panel. Important functions such as workflow creation,
data management, account details and visualization can be accessed via
dashboard. Graphical outputs from the various programs in MetDAT are
shown at the bottom.
for customization by advanced users. User interface provides a dashboard for
easy access to the programs and results (Fig. 1). A standalone visualization
module that is accessible from the dashboard has two types of interactive
plots—X–Y plot and barchart. These require Adobe Flash plug-in to be
installed in the browser. Algorithms for programs, references, user guide
and examples are included in the manual at the website for easy reference.
2.2 Data processing, analysis and output
The pre-processing and pre-treatment modules in MetDAT include removing
noise, correction of base lines and normalizing or scaling the data to increase
signal-to-noise ratio. Data analysis includes a range of statistical techniques
such as analysis of variance, principal component analysis, partial least
square (PLS), partial least square-discriminant analysis (PLS-DA), linear
discriminant analysis and t-test. The major strength of this software is
the power to analyze data at various hierarchical levels such as samples,
treatments, time points, biological repeats and technical replicates. Datasets
for the different hierarchical levels can be assigned at data preparation.
It provides users the option to perform intensive statistical analysis at any
of these levels. Differentially expressed metabolites are listed based on fold
change and t-test analysis. The listed metabolites can further be searched
against four databases—Aracyc (Mueller et al., 2003), HMDB (Wishart
et al., 2009), Plantcyc and KEGG (Kanehisa and Goto, 2000) along with
multiple filters for better and targeted results. The database search includes
a feature for metabolic pathway identification. Key differential metabolites
are highlighted for users to focus on selected parts of metabolic networks.
3 CONCLUSIONS
Metabolic perturbation studies involve understanding effects of
variation in a statistically robust fashion. This software is created
with the vision of biologists for data analysis and interpretation.
MetDAT platform simplifies and standardizes data analysis for
usage with minimal training. MetDAT provides data processing
and analysis, database search for metabolite identification, pathway
mapping and a rich palette of visualization tools. The combination of
data management and customizable workflow makes data analysis
faster thereby saving researchers time.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the support and contributions of
the Singapore-Delft Water Alliance (SDWA). The research presented
in this work was carried out as part of the SDWA’s Aquatic Science
Centre at Ulu Pandan programme.
Funding: Singapore-Delft Water Alliance (WBS No: R-264-001-
002-272).
Conflict of Interest: none declared.
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Mueller, L.A., Zhang, P. & Rhee, S.Y. AraCyc: A biochemical pathway database for Arabidopsis. Plant Physiol. 132, 453-460
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  • ANAL CHEM
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  • Aug 2007
  • J CHROMATOGR A
Modern analytical technologies afford comprehensive and quantitative investigation of a multitude of different metabolites. Typical metabolomic experiments can therefore produce large amounts of data. Handling such complex datasets is an important step that has big impact on extent and quality at which the metabolite identification and quantification can be made, and thus on the ultimate biological interpretation of results. Increasing interest in metabolomics thus led to resurgence of interest in related data processing. A wide variety of methods and software tools have been developed for metabolomics during recent years, and this trend is likely to continue. In this paper we overview the key steps of metabolomic data processing and focus on reviewing recent literature related to this topic, particularly on methods for handling data from liquid chromatography mass spectrometry (LC-MS) experiments.
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Quality control for plant metabolomics: Reporting MSI-compliant studies
Article
  • Mar 2008
  • PLANT J
The Metabolomics Standards Initiative (MSI) has recently released documents describing minimum parameters for reporting metabolomics experiments, in order to validate metabolomic studies and to facilitate data exchange. The reporting parameters encompassed by MSI include the biological study design, sample preparation, data acquisition, data processing, data analysis and interpretation relative to the biological hypotheses being evaluated. Herein we exemplify how such metadata can be reported by using a small case study - the metabolite profiling by GC-TOF mass spectrometry of Arabidopsis thaliana leaves from a knockout allele of the gene At1g08510 in the Wassilewskija ecotype. Pitfalls in quality control are highlighted that can invalidate results even if MSI reporting standards are fulfilled, including reliable compound identification and integration of unknown metabolites. Standardized data processing methods are proposed for consistent data storage and dissemination via databases.
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R: a language and environment for statistical computing. R Foundaton for Statistical Computing
  • Jan 2008
  • R Development
  • Core Team
R Development Core Team (2008) R: a language and environment for statistical computing. R Foundaton for Statistical Computing, Vienna, Austria. Available at http://www.R-project.org.