Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2019 Nov 13;10:1120.
doi: 10.3389/fgene.2019.01120. eCollection 2019.

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Affiliations
Free PMC article
Review

Leveraging User-Friendly Network Approaches to Extract Knowledge From High-Throughput Omics Datasets

Pablo Ivan Pereira Ramos et al. Front Genet. .
Free PMC article

Abstract

Recent technological advances for the acquisition of multi-omics data have allowed an unprecedented understanding of the complex intricacies of biological systems. In parallel, a myriad of computational analysis techniques and bioinformatics tools have been developed, with many efforts directed towards the creation and interpretation of networks from this data. In this review, we begin by examining key network concepts and terminology. Then, computational tools that allow for their construction and analysis from high-throughput omics datasets are presented. We focus on the study of functional relationships such as co-expression, protein-protein interactions, and regulatory interactions that are particularly amenable to modeling using the framework of networks. We envisage that many potential users of these analytical strategies may not be completely literate in programming languages and code adaptation, and for this reason, emphasis is given to tools' user-friendliness, including plugins for the widely adopted Cytoscape software, an open-source, cross-platform tool for network analysis, visualization, and data integration.

Keywords: correlation networks; graph; high-throughput sequencing; network analysis; omics; protein–protein interaction; regulatory networks; systems biology.

Figures

Figure 1
Figure 1
A roadmap to network concepts covered in this review. Three simple six-node graphs are shown in the upper panel. These graphs can be undirected (A), directed (B) or weighted directed (C). In the latter, the thickness of edges reflects the weights of the interactions. Various omics datasets can be analyzed using the language of networks, which are discussed in the following sections (D). (E) Once a network is attained, further analyses are warranted, which include disclosing modules or communities and calculating topological metrics such as node degree and betweenness centrality (BC), covered in A primer on network analysis and visualization. The size of a node is proportional to its degree, while the color reflects the community structure in this illustrative example where two modules are disclosed. For selected nodes, interpretations of node BC and degree are presented.
Figure 2
Figure 2
Different views on assessing correlations. (A) Classic scatter plot with correlation curve (straight black line). (B) Correlation matrix plot, designed with the corrplot package (Wei and Simko, 2017). (C) Circular layout correlation network, designed with Gephi (Bastian et al., 2009). (D) Complex correlation network with modularity coloring, designed with qgraph package (Espkamp et al., 2012).
Figure Box
Figure Box
Topological properties of a toy network. The modular aspect of the network is apparent in A, with two modules (or partitions) shown. The size of the nodes in BD are proportional to, respectively, the node degree, betweenness centrality, and closeness centrality.
Figure 3
Figure 3
A correlation network constructed using Cytoscape 3.2. The network was built using a bacterial expression dataset, and nodes represent annotated genes, with edges connecting nodes if they pass a correlation threshold calculated using Spearman’s rank correlation in the Cyni Toolbox. In the picture a pop-up menu with the calculated network metrics (using the NetworkAnalyzer plugin in Cytoscape) is shown. Besides the network zoom, the program also shows the whole network in the lower-right screen, as a miniature.
Figure 4
Figure 4
Different ways to represent gene regulatory networks. (A) Toy networks exemplifying bipartite and logical (Boolean) graphs. (B) A real example of the human gene regulatory network extracted from TRRUST database, and its graphical representation as a bipartite and a logical networks.
Figure 5
Figure 5
Typical network analyses performed using Cytoscape. A network of yeast protein interaction data is presented (A), with node size scaled with betweenness centrality, which help in straightforward identification of important nodes in this network. Nodes are colored according to its membership to a community as determined using the Girvan-Newman fast greedy algorithm implementation in the clusterMaker plugin (Morris et al., 2011). Colors for each community were chosen automatically using a color-generating function and a discrete mapping, with modules numbered sequentially in the left column shown in (B), and colors (in RGB and hex formats) on the right. Properties of nodes are shown below in (C), including some centrality measures. These can be downloaded in-whole as a table for downstream analyses. The network is arranged according to a force-directed layout algorithm.
Figure 6
Figure 6
Network methods on the rise. Searches in PubMed (http://ncbi.nlm.nih.gov/pubmed) were performed to identify the all-time use of co-expression networks (query: “co-expression network” OR “coexpression network”), gene regulatory networks (GRN; query: “gene regulatory network”), and protein–protein interaction networks (PPI; query: “protein–protein interaction network”). Data for 2019 is partial (up to March) and are displayed as open points.

Similar articles

See all similar articles

References

    1. Aittokallio T., Schwikowski B. (2006). Graph-based methods for analyzing networks in cell biology. Brief. Bioinform. 7, 243–255. 10.1093/bib/bbl022 - DOI - PubMed
    1. Ajorloo F., Vaezi M., Saadat A., Safaee S. R., Gharib B., Ghanei M., et al. (2017). A systems medicine approach for finding target proteins affecting treatment outcomes in patients with non-Hodgkin lymphoma. PloS One 12, e0183969. 10.1371/journal.pone.0183969 - DOI - PMC - PubMed
    1. Alonso-López D., Campos-Laborie F. J., Gutiérrez M. A., Lambourne L., Calderwood M. A., Vidal M., et al. (2019). APID database: redefining protein-protein interaction experimental evidences and binary interactomes. Database 2019, baz005. 10.1093/database/baz005 - DOI - PMC - PubMed
    1. Andrade R. F. S., Rocha-Neto I. C., Santos L. B. L., de Santana C. N., Diniz M. V. C., Lobão T. P., et al. (2011). Detecting network communities: an application to phylogenetic analysis. PloS Comput. Biol. 7, e1001131. 10.1371/journal.pcbi.1001131 - DOI - PMC - PubMed
    1. Aranda B., Blankenburg H., Kerrien S., Brinkman F. S. L., Ceol A., Chautard E., et al. (2011). PSICQUIC and PSISCORE: accessing and scoring molecular interactions. Nat. Methods 8, 528–529. 10.1038/nmeth.1637 - DOI - PMC - PubMed

LinkOut - more resources

Feedback