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Open Access Highly Accessed Methodology article

The differential disease regulome

Geir K Sandve1, Sveinung Gundersen2, Halfdan Rydbeck134, Ingrid K Glad5, Lars Holden3, Marit Holden3, Knut Liestøl14, Trevor Clancy2, Finn Drabløs6, Egil Ferkingstad3, Morten Johansen7, Vegard Nygaard7, Eivind Tøstesen7, Arnoldo Frigessi38 and Eivind Hovig1237*

Author Affiliations

1 Department of Informatics, University of Oslo, Blindern, 0316 Oslo, Norway

2 Department of Tumor Biology, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway

3 Statistics For Innovation, Norwegian Computing Center, 0314 Oslo, Norway

4 Centre for Cancer Biomedicine, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway

5 Department of Mathematics, University of Oslo, Blindern, 0316 Oslo, Norway

6 Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway

7 Institute for Medical Informatics, The Norwegian Radium Hospital, Oslo University Hospital, Montebello, 0310 Oslo, Norway

8 Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Blindern, 0317 Oslo, Norway

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BMC Genomics 2011, 12:353  doi:10.1186/1471-2164-12-353

Published: 7 July 2011

Abstract

Background

Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information.

Results

We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format.

The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF.

The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available.

Conclusion

We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no webcite.