Assembly of an Interactive Correlation Network for the Arabidopsis Genome Using a Novel Heuristic Clustering Algorithm

Oxford University Press (OUP) - Tập 152 Số 1 - Trang 29-43 - 2009
Marek Mutwil1,2,3, Björn Usadel1,2,3, Moritz Schuݶtte1,2,3, Ann E. Loraine1,2,3, Oliver Ebenhöh1,2,3, Staffan Persson1,2,3
1Department of Bioinformatics and Genomics, North Carolina Research Campus, University of North Carolina at Charlotte, Kannapolis, North Carolina 28081 (A.L.)
2Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom (O.E.)
3Max-Planck-Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (M.M., B.U., M.S., O.E., S.P.); Department of Bioinformatics and Genomics, North Carolina Research Campus, University of North Carolina at Charlotte, Kannapolis, North Carolina 28081 (A.L.); and Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom (O.E.)

Tóm tắt

Abstract A vital quest in biology is comprehensible visualization and interpretation of correlation relationships on a genome scale. Such relationships may be represented in the form of networks, which usually require disassembly into smaller manageable units, or clusters, to facilitate interpretation. Several graph-clustering algorithms that may be used to visualize biological networks are available. However, only some of these support weighted edges, and none provides good control of cluster sizes, which is crucial for comprehensible visualization of large networks. We constructed an interactive coexpression network for the Arabidopsis (Arabidopsis thaliana) genome using a novel Heuristic Cluster Chiseling Algorithm (HCCA) that supports weighted edges and that may control average cluster sizes. Comparative clustering analyses demonstrated that the HCCA performed as well as, or better than, the commonly used Markov, MCODE, and k-means clustering algorithms. We mapped MapMan ontology terms onto coexpressed node vicinities of the network, which revealed transcriptional organization of previously unrelated cellular processes. We further explored the predictive power of this network through mutant analyses and identified six new genes that are essential to plant growth. We show that the HCCA-partitioned network constitutes an ideal “cartographic” platform for visualization of correlation networks. This approach rapidly provides network partitions with relative uniform cluster sizes on a genome-scale level and may thus be used for correlation network layouts also for other species.

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Oxford University Press (OUP)

Tập 152 Số 1

29-43

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