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Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA
Open accessAbstract
Summary: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. Availability:The web interface of IMPaLA is available at http://impala.molgen.mpg.de. A web services programming interface is provided at http://impala.molgen.mpg.de/wsdoc. Contact: kamburov@molgen.mpg.de; r.cavill@imperial.ac.uk; h.keun@imperial.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online.
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Riedel, R. F., Porrello, A., Pontzer, E., Chenette, E. J., Hsu, D. S., Balakumaran, B. S., Potti, A., Nevins, J. R., & Febbo, P. G. (2008) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-08-0642 · Cited by 72
RetractedControlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
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Scherf, U., Ross, D. T., Waltham, M., Smith, L. H., Lee, J. K., Tanabe, L., Kohn, K. W., Reinhold, W. C., Myers, T. G., Andrews, D. T., et al. (2000) · Nature Genetics · doi:10.1038/73439 · Cited by 1,450
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Kamburov, A., Pentchev, K., Galicka, H., Wierling, C., Lehrach, H., & Herwig, R. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1156 · Cited by 671
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Sabatine, M. S., Liu, E., Morrow, D. A., Heller, E., McCarroll, R., Wiegand, R., Berriz, G. F., Roth, F. P., & Gerszten, R. E. (2005) · Circulation · doi:10.1161/circulationaha.105.569137 · Cited by 461
Adjaye, J., Huntriss, J., Herwig, R., BenKahla, A., Brink, T. C., Wierling, C., Hultschig, C., Groth, D., Yaspo, M., Picton, H. M., et al. (2005) · STEM CELLS · doi:10.1634/stemcells.2005-0113 · Cited by 213
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Chagoyen, M., & Pazos, F. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr001 · Cited by 147
Cavill, R., Kamburov, A., Ellis, J. K., Athersuch, T. J., Blagrove, M. S. C., Herwig, R., Ebbels, T. M. D., & Keun, H. C. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001113 · Cited by 106
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Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723
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Metadata
- Authors
- Kamburov, A., Cavill, R., Ebbels, T. M. D., Herwig, R., & Keun, H. C.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-09-04
- Volume
- 27
- Issue
- 20
- Pages
- 2917-2918
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2155158051
- Cited by
- 403
- References
- 10
- FWCI
- 3.41
- Type
- journal article
- Language
- en
- Open access
- bronze
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