English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Decision tree supported substructure prediction of metabolites from GC-MS profiles

Hummel, J., Strehmel, N., Selbig, J., Walther, D., & Kopka, J. (2010). Decision tree supported substructure prediction of metabolites from GC-MS profiles. Metabolomics, 6(2), 322-333. doi:10.1007/s11306-010-0198-7.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-23E9-B Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-23EA-9
Genre: Journal Article

Files

show Files
hide Files
:
Hummel-2010-Decision tree suppor.pdf (Any fulltext), 662KB
Name:
Hummel-2010-Decision tree suppor.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Hummel, J.1, 2, Author              
Strehmel, N.3, Author              
Selbig, J.1, Author              
Walther, D.2, Author              
Kopka, J.3, Author              
Affiliations:
1BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753315              
2BioinformaticsCIG, Infrastructure Groups and Service Units, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753303              
3Applied Metabolome Analysis, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753338              

Content

show
hide
Free keywords: metabolic markers gas chromatography (gc) mass spectrometry (ms) gc-ms mass spectral classification mass spectral matching metabolite fingerprinting metabolite profiling metabolomics metabonomics decision trees mass-spectrometry compound identification gas-chromatography libraries database
 Abstract: Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.

Details

show
hide
Language(s): eng - English
 Dates: 2010-06-082010
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: ISI: ISI:000277957200006
DOI: 10.1007/s11306-010-0198-7
ISSN: 1573-3890 (Electronic)
URI: ://000277957200006 http://www.springerlink.com/content/e238n21532031t45/fulltext.pdf
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Metabolomics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 6 (2) Sequence Number: - Start / End Page: 322 - 333 Identifier: -