Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

TAFFEL: Independent Enrichment Analysis of gene sets

Mitja I Kurki125, Jussi Paananen16, Markus Storvik23, Seppo Ylä-Herttuala4, Juha E Jääskeläinen5, Mikael von und zu Fraunberg5, Garry Wong12 and Petri Pehkonen2*

Author Affiliations

1 Laboratory of Functional Genomics and Bioinformatics, Department of Neurobiology, A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland

2 Department of Biosciences, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland

3 Department of Pharmacology and Toxicology, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland

4 Department of Biotechnology and Molecular Medicine, A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland

5 Department of Neurosurgery, Kuopio University Hospital, FIN-70211 Kuopio, Finland

6 Institute of Clinical Medicine, University of Eastern Finland, PO Box 1627, FIN-70211 Kuopio, Finland

For all author emails, please log on.

BMC Bioinformatics 2011, 12:171  doi:10.1186/1471-2105-12-171

Published: 19 May 2011

Abstract

Background

A major challenge in genomic research is identifying significant biological processes and generating new hypotheses from large gene sets. Gene sets often consist of multiple separate biological pathways, controlled by distinct regulatory mechanisms. Many of these pathways and the associated regulatory mechanisms might be obscured by a large number of other significant processes and thus not identified as significant by standard gene set enrichment analysis tools.

Results

We present a novel method called Independent Enrichment Analysis (IEA) and software TAFFEL that eases the task by clustering genes to subgroups using Gene Ontology categories and transcription regulators. IEA indicates transcriptional regulators putatively controlling biological functions in studied condition.

Conclusions

We demonstrate that the developed method and TAFFEL tool give new insight to the analysis of differentially expressed genes and can generate novel hypotheses. Our comparison to other popular methods showed that the IEA method implemented in TAFFEL can find important biological phenomena, which are not reported by other methods.