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Cited by 34

The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.

Reverse engineering and analysis of large genome-scale gene networks

Aluru, Maneesha, Zola, Jaroslaw, Nettleton, Dan, Aluru, Srinivas·2013·Nucleic Acids Research
DOI 10.1093/nar/gks904PMC PMC3592423
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Abstract

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web.

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Figures

Figure 1. The network to be inferred is represented as an adjacency matrix D, where n is the number of genes. The matrix is partitioned into blocks of submatrices as shown. Each processor is assigned a row of submatrices. The number inside a submatrix indicates the stage at which the submatrix is computed. Only half the matrix is computed as it is symmetric.
Figure 1. The network to be inferred is represented as an adjacency matrix D, where n is the number of genes. The matrix is partitioned into blocks of submatrices as shown. Each processor is assigned a row of submatrices. The number inside a submatrix indicates the stage at which the submatrix is computed. Only half the matrix is computed as it is symmetric.CC BY · PubMed Central
Figure 2. Comparison of TINGe with ARACNe and GeneNet on synthetic data using SynTReN.
Figure 2. Comparison of TINGe with ARACNe and GeneNet on synthetic data using SynTReN.CC BY · PubMed Central
Figure 3. Scalability of TINGe for datasets with different numbers of genes n, and different numbers of expression observations m.
Figure 3. Scalability of TINGe for datasets with different numbers of genes n, and different numbers of expression observations m.CC BY · PubMed Central
Figure 4. A partial rendering of the Arabidopsis whole-genome network. The illustrated network represents a union of all the shortest paths between each pair of the top 5% of the hubs in the whole-genome network (Supplementary Table S4). It contains 1556 genes and 22 073 interactions. The network topology is displayed using Cytoscape with the size of a node proportional to its degree and the intensity of its color proportional to its betweenness centrality. The largest and the darkest node in the bottom right-hand corner of the figure is PMDH2, a gene involved in photorespiration.
Figure 4. A partial rendering of the Arabidopsis whole-genome network. The illustrated network represents a union of all the shortest paths between each pair of the top 5% of the hubs in the whole-genome network (Supplementary Table S4). It contains 1556 genes and 22 073 interactions. The network topology is displayed using Cytoscape with the size of a node proportional to its degree and the intensity of its color proportional to its betweenness centrality. The largest and the darkest node in the bottom right-hand corner of the figure is PMDH2, a gene involved in photorespiration.CC BY · PubMed Central
Figure 5. Scale-free nature of the Arabidopsis whole-genome network. Node degree distribution in the network. X-axis is the node degree and Y-axis represents the probability of a node with a given degree.
Figure 5. Scale-free nature of the Arabidopsis whole-genome network. Node degree distribution in the network. X-axis is the node degree and Y-axis represents the probability of a node with a given degree.CC BY · PubMed Central
Figure 6. Cellulose subnetwork. Red—seed genes; green—genes sharing the same GO category as the seed genes; blue—genes with associated functions; pink—genes of interacting pathways and yellow—unclassified genes.
Figure 6. Cellulose subnetwork. Red—seed genes; green—genes sharing the same GO category as the seed genes; blue—genes with associated functions; pink—genes of interacting pathways and yellow—unclassified genes.CC BY · PubMed Central
Figure 7. Carotenoid subnetwork. Color coding as given in Figure 6.
Figure 7. Carotenoid subnetwork. Color coding as given in Figure 6.CC BY · PubMed Central
Figure 8. Aerobic respiration subnetwork. Color coding as given in Figure 6.
Figure 8. Aerobic respiration subnetwork. Color coding as given in Figure 6.CC BY · PubMed Central

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Metadata

Authors
Aluru, M., Zola, J., Nettleton, D., & Aluru, S.
Affiliations
Department of Genetics, Development, and Cell Biology, 2Department of Electrical and Computer Engineering, 3Department of Statistics, Iowa State University, Ames, IA 50011, USA and 4Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2012-10-05
Volume
41
Issue
1
Pages
e24-e24
ISSN
  • 1362-4962
  • 0305-1048
DOI
10.1093/nar/gks904
PMC
PMC3592423
Cited by
34
References
65
Language
en
Licence
creativecommons.org/licenses/by/3.0 (vor, 1461 d)

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