Could correlation-based methods be used to derive genetic association networks?

In recent years a number of methods have been proposed for reverse engineering of genetic networks from gene expression data. These methods work well on small genetic networks with very few connections between genes, but for larger networks and networks with higher connectivity, the computational cost increases dramatically and the performance of these methods is insufficient. In real systems, however, it is known that the networks are large and that genes typically have many interactions. In addition, the methods require abundant expression data for derivation of the networks. A method that can derive networks irrespective of these obstacles and have a low computational cost will be of importance. In this paper, three correlation-based methods are investigated as alternatives. Using correlation-based methods means that the computational cost is reduced, since only N/2 correlations have to be computed for a data set of N expression profiles. The presented methods are not limited by any maximum size of the network, nor by the connectivity of the network, or the amount of expression data.

[1]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[2]  G. Ramsay.  DNA chips: State-of-the art , 1998, Nature Biotechnology.

[3]  Michael E. Cusick,et al.  The Yeast Proteome Database (YPD) and Caenorhabditis elegans Proteome Database (WormPD): comprehensive resources for the organization and comparison of model organism protein information , 2000, Nucleic Acids Res..

[4]  D Thieffry,et al.  Qualitative analysis of gene networks. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[5]  Adam P. Arkin,et al.  Statistical Construction of Chemical Reaction Mechanisms from Measured Time-Series , 1995 .

[6]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[7]  Laurie J. Heyer,et al.  Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.

[8]  G S Michaels,et al.  Cluster analysis and data visualization of large-scale gene expression data. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[9]  V. Thorsson,et al.  Discovery of regulatory interactions through perturbation: inference and experimental design. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[10]  Satoru Miyano,et al.  Identification of Genetic Networks from a Small Number of Gene Expression Patterns Under the Boolean Network Model , 1998, Pacific Symposium on Biocomputing.

[11]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[12]  Jérôme Euzenat,et al.  Grasping at molecular interactions and genetic networks in Drosophila melanogaster using FlyNets, an Internet database , 1999, Nucleic Acids Res..

[13]  Kei-Hoi Cheung,et al.  TRIPLES: a database of gene function in Saccharomyces cerevisiae , 2000, Nucleic Acids Res..

[14]  Ronald W. Davis,et al.  A genome-wide transcriptional analysis of the mitotic cell cycle. , 1998, Molecular cell.

[15]  Zoltan Szallasi,et al.  Genetic Network Analysis in Light of Massively Parallel Biological Data Acquisition , 1998, Pacific Symposium on Biocomputing.

[16]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[17]  Gary D. Stormo,et al.  Modeling Regulatory Networks with Weight Matrices , 1998, Pacific Symposium on Biocomputing.

[18]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..

[19]  Roland Somogyi,et al.  Making sense of gene-expression data , 1999 .

[20]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.