Pathos: A web facility that uses metabolic maps to display experimental changes in metabolites identified by mass spectrometry

This work describes a freely available web-based facility which can be used to analyse raw or processed mass spectrometric data from metabolomics experiments and display the metabolites identified – and changes in their experimental abundance – in the context of the metabolic pathways in which they occur. The facility, Pathos (http://motif.gla.ac.uk/Pathos/), employs Java servlets and is underpinned by a relational database populated from the Kyoto Encyclopaedia of Genes and Genomes (KEGG). Input files can contain either raw m/z values from experiments conducted in different modes, or KEGG or MetaCyc IDs assigned by the user on the basis of the m/z values and other criteria. The textual output lists the KEGG pathways on an XHTML page according to the number of metabolites or potential metabolites that they contain. Filtering by organism is also available. For metabolic pathways of interest, the user is able to retrieve a pathway map with identified metabolites highlighted. A particular feature of Pathos is its ability to process relative quantification data for metabolites identified under different experimental conditions, and to present this in an easily comprehensible manner. Results are colour-coded according to the degree of experimental change, and bar charts of the results can be generated interactively from either the text listings or the pathway maps. The visual presentation of the output from Pathos is designed to allow the rapid identification of metabolic areas of potential interest, after which particular results may be examined in detail. Copyright © 2011 John Wiley & Sons, Ltd.

[1]  Simon Rogers,et al.  Probabilistic assignment of formulas to mass peaks in metabolomics experiments , 2009, Bioinform..

[2]  Peer Bork,et al.  iPath2.0: interactive pathway explorer , 2011, Nucleic Acids Res..

[3]  Michael P. Barrett,et al.  Genetic characterization of glucose transporter function in Leishmania mexicana , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[4]  David R. Gilbert,et al.  MetaNetter: inference and visualization of high-resolution metabolomic networks , 2008, Bioinform..

[5]  S. Kanaya,et al.  Summary , 1940, Intellectual Property in the Conflict of Laws.

[6]  M. Barrett,et al.  Metabolomic profiling using Orbitrap Fourier transform mass spectrometry with hydrophilic interaction chromatography: a method with wide applicability to analysis of biomolecules. , 2008, Rapid communications in mass spectrometry : RCM.

[7]  Laurent Debrauwer,et al.  Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining , 2010, Metabolomics.

[8]  Oliver Fiehn,et al.  Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry , 2007, BMC Bioinformatics.

[9]  Matej Oresic,et al.  MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data , 2010, BMC Bioinformatics.

[10]  David S. Wishart,et al.  MetaboAnalyst: a web server for metabolomic data analysis and interpretation , 2009, Nucleic Acids Res..

[11]  Wanchang Lin,et al.  Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules' , 2009, BMC Bioinformatics.

[12]  Alexey V Antonov,et al.  TICL – a web tool for network‐based interpretation of compound lists inferred by high‐throughput metabolomics , 2009, The FEBS journal.

[13]  Oliver Fiehn,et al.  Metabolomic database annotations via query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm , 2006, BMC Bioinformatics.

[14]  Gabi Kastenmüller,et al.  metaP-Server: A Web-Based Metabolomics Data Analysis Tool , 2010, Journal of biomedicine & biotechnology.

[15]  F. Ausubel Metabolomics , 2012, Nature Biotechnology.

[16]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[17]  R. Breitling,et al.  PeakML/mzMatch: a file format, Java library, R library, and tool-chain for mass spectrometry data analysis. , 2011, Analytical chemistry.

[18]  Karsten Suhre,et al.  MassTRIX: mass translator into pathways , 2008, Nucleic Acids Res..

[19]  김삼묘,et al.  “Bioinformatics” 특집을 내면서 , 2000 .

[20]  Douglas B. Kell,et al.  Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets , 2011, Bioinform..

[21]  Laxman Yetukuri,et al.  Algorithms and tools for the preprocessing of LC–MS metabolomics data , 2011 .

[22]  Matej Oresic,et al.  MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data , 2006, Bioinform..

[23]  Michael P. Barrett,et al.  MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks , 2010, Nucleic Acids Res..