Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA
Author(s) -
Atanas Kamburov,
Rachel Cavill,
Timothy M. D. Ebbels,
Ralf Herwig,
Hector C. Keun
Publication year - 2011
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btr499
Subject(s) - computer science , application programming interface , interface (matter) , identification (biology) , computational biology , metabolomics , transcriptome , world wide web , bioinformatics , biology , gene , genetics , gene expression , bubble , maximum bubble pressure method , parallel computing , programming language , botany
Pathway-level analysis is a powerful approach enabling interpretation of post-genomic data at a higher level than that of individual biomolecules. Yet, it is currently hard to integrate more than one type of omics data in such an approach. Here, we present a web tool 'IMPaLA' for the joint pathway analysis of transcriptomics or proteomics and metabolomics data. It performs over-representation or enrichment analysis with user-specified lists of metabolites and genes using over 3000 pre-annotated pathways from 11 databases. As a result, pathways can be identified that may be disregulated on the transcriptional level, the metabolic level or both. Evidence of pathway disregulation is combined, allowing for the identification of additional pathways with changed activity that would not be highlighted when analysis is applied to any of the functional levels alone. The tool has been implemented both as an interactive website and as a web service to allow a programming interface.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom