אסיף מאגר המחקר החקלאי

Construction, visualisation, and clustering of transcription networks from microarray expression data

Year:

2007

Source of publication :

Authors :

Volume :

3

Co-Authors:

Freeman, T.C., Division of Pathway Medicine, University of Edinburgh Medical School, Edinburgh, United Kingdom, Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
Goldovsky, L., European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom, Computational Biology Unit, Center for Research and Technology Hellas, Thessaloniki, Greece
Brosch, M., Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
Van Dongen, S., Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
Mazière, P., Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
Grocock, R.J., Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom
Freilich, S., European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom, Computational Biology Unit, Center for Research and Technology Hellas, Thessaloniki, Greece
Thornton, J., European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom
Enright, A.J., Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom

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Abstract:

Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express3D. © 2007 Freeman et al.

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