Metabolite Differentiation and Discovery Lab (MeDDL): A New Tool for Biomarker Discovery and Mass Spectral Visualization

The goal of this work was to design and implement a prototype software tool for the visualization and analysis of small molecule metabolite GC−MS and LC−MS data for biomarker discovery. The key features of the Metabolite Differentiation and Discovery Lab (MeDDL) software platform include support for...

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Bibliographic Details
Published inAnalytical chemistry (Washington) Vol. 82; no. 11; pp. 4386 - 4395
Main Authors Grigsby, Claude C, Rizki, Mateen M, Tamburino, Louis A, Pitsch, Rhonda L, Shiyanov, Pavel A, Cool, David. R
Format Journal Article
LanguageEnglish
Published Washington, DC American Chemical Society 01.06.2010
Subjects
Algorithms
Analytical chemistry
Animals
Biomarkers
Biomarkers - analysis
Chemistry
Chromatographic methods and physical methods associated with chromatography
Chromatography, Gas
Chromatography, Liquid
Computer Graphics
Exact sciences and technology
Gas chromatographic methods
Male
Mass spectrometry
Mass Spectrometry - methods
Metabolism
Molecules
Other chromatographic methods
Principal Component Analysis
Rats
Software
Spectrometric and optical methods
Visualization
Data analysis
Prototype
Support
Metabolite
Use
Biological marker
Liquid chromatography
Time
Algorithm
Design
Gas chromatography
Molecules
Alignment
Mass spectrometry MS/MS
Software
Peak
Differentiation
File
Mass spectrometry
Tool
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Summary:The goal of this work was to design and implement a prototype software tool for the visualization and analysis of small molecule metabolite GC−MS and LC−MS data for biomarker discovery. The key features of the Metabolite Differentiation and Discovery Lab (MeDDL) software platform include support for the manipulation of large data sets, tools to provide a multifaceted view of the individual experimental results, and a software architecture amenable to modification and addition of new algorithms and software components. The MeDDL tool, through its emphasis on visualization, provides unique opportunities by combining the following: easy use of both GC−MS and LC−MS data; use of both manufacturer specific data files as well as netCDF (network Common Data Form); preprocessing (peak registration and alignment in both time and mass); powerful visualization tools; and built in data analysis functionality.
ISSN:0003-2700
1520-6882
DOI:10.1021/ac100034u