Computational Metabolomics Group

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About: The goal of this work is the development novel algorithms and cyberinfrastructure for the metabolomics community. This is a collaborative effort by the Computational Metabolomics Group at the College of Charleston, Dr. Michael Peterson at the University of Hawaii at Hilo, and Dr. Ruth Gates lab at the University of Hawaii at Manoa that includes Emilia (Maggie) Sogin and Dr. Hollie Putnam.




Metabolomics is the exhaustive characterization of metabolite concentrations in biofluids and tissues. The use of NMR and chromatography-linked mass spectrometry to assay metabolic profiles of tissue homogenates and biofluids has been increasingly recognized as a powerful tool for biological discovery. In recent years metabolomics techniques have been applied to a wide variety of diagnostic, preclinical, systems biology, and ecological studies. Working with collaborators in the Gates Laboratory at the University of Hawaii at Manoa and Dr. Nick Reo's NMR spectroscopy lab at Wright State University, we are developing standards-based tools and web services for the pre-processing, normalization/standardization, exploratory and comparative analysis, and visualization of NMR spectra from biofluids.

NMR-based metabolomics has been used to associate an organism’s health status to its metabolite profile measured in biofluids (e.g, urine, blood, fecal and tissue extracts) or tissue biopsies. Coupled with multivariate data analyses, the 1H NMR-based metabolomics approach is a fast, accurate and reproducible analytical technique for visualization of biochemical changes in biofluids or tissues. This methodology involves correlating observed changes in metabolite levels to the biological effects related to physiological stimuli or genetic modification, toxicological, pathophysiological or environmental conditions. Studies have highlighted its potential for the successful identification and characterization of toxicity, metabolic pathways perturbed in various cancers, disease-related stages in chronic lymphocytic leukemia, and other pathophysiological conditions.

Metabolomics Galaxy-based Portal (Galaxy-M)

We aim to design and deploy novel cyberinfrastructure for the metabolomics community. A platform deemed “Online bioinformatics analysis for everyone” has emerged as the leading open-source workflow platform for genomics and proteomics ( The field of metabolomics is considerably less mature, and not significantly integrated into bioinformatics workflow tools, such as Galaxy. Our goal is to develop the integrated pipeline necessary to incorporate metabolomics data along with genomics and proteomics data in the Galaxy platform. We will create a software platform with a single, user-friendly interface to tackle the data and analytical challenges of multi-scale studies of Hawaiian corals. We will leverage the existing efforts of the genomics and proteomics communities and expand the feature set of Galaxy to include web-based metabolomics data analysis. The first step will consist of adding a set of algorithms necessary for the preprocessing and analysis of metabolomics data. Some algorithms, including those for signal de-noising and peak detection will be implemented from algorithms described in the literature. We will also develop a set of filters and algorithms for the automatic detection of specific metabolites within a mixed experimental spectrum. Intuitive user interfaces for these algorithms will be built into the web portal. The second major component in the portal will be a user-annotated metabolite database. Researchers will be able to use our portal to identify specific metabolites within a spectrum using both automatic and user-based assignments. Those assignments will then be annotated within our database, together with other experimental parameters (NMR spectrograph frequency, pH, solvent, etc.). The database, as it grows, will provide further confidence in future assignments, thus significantly reducing both the time and expertise required to annotate the metabolites within experimental spectra.

This project is supported by ongoing efforts of two undergraduate research students:

  1. Declan Whitmyer - Data Science undergraduate at the College of Charleston
  2. Ryder Donahue - Computer Science undergraduate at the University of Hawaii, Hilo

The goals this semester include:

  1. Set up the software engineering deployment pipeline that includes a hosted repository on
    1. Linux system administration and configuration of dedicated virtual machine hosted on the Anderson Lab compute cluster
    2. Local development environments
    3. Deployment procedures and documentation
  2. Galaxy customization
    1. This project will require significant customization and enhancement to the Galaxy scientific workflow system
    2. Part of this will be developing and levering tools in the R programming language that were created and refined by Maggie Sogin and Paul Anderson.
  3. Incorporation of the metabolomics spectral browser and annotation system, MetaboScribe (developed by Edward Pharr)
  4. Produce a technical report describing the status of the ongoing deployment and development

Metabolomics Algorithms

We are currently developing novel algorithms for

  • Improving the accuracy of spectral quantification
  • Identifying and deconvolving NMR-based spectroscopic data
  • Characterizing metabolomic profiles between treatment groups


This work is part of ongoing collaborations with the Gates Laboratory at the University of Hawaii at Manoa and Dr. Nicholas Reo at Wright State University. Dr. Paul Anderson is the director and PI of the Computational Metabolomics Group.


2014 - present: Kellan Fluette, Edward Pharr

2013 - 2014: Edward Pharr

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