Systematic Functional Annotation and Visualization of Biological Networks

By Anastasia Baryshnikova. 2016

152 citations recorded by the source corpus.

Large-scale biological networks map functional connections between most genes in the genome and can potentially uncover high level organizing principles governing cellular functions. These networks, however, are famously complex and often regarded as disordered masses of tangled interactions (“hairballs”) that are nearly impenetrable to biologists. As a result, our current understanding of network functional organization is very limited. To address this problem, I developed a systematic quantitative approach for annotating biological networks and examining their functional structure. This method, named Spatial Analysis of Functional Enrichment (SAFE), detects network regions that are statistically overrepresented for a functional group or a quantitative phenotype of interest, and provides an intuitive visual representation of their relative positioning within the network. By successfully annotating the Saccharomyces cerevisiae genetic interaction network with Gene Ontology terms, SAFE proved to be sensitive to functional signals and robust to noise. In addition, SAFE annotated the network with chemical genomic data and uncovered a new potential mechanism of resistance to the anti-cancer drug bortezomib. Finally, SAFE showed that protein-protein interactions, despite their apparent complexity, also have a high level functional structure. These results demonstrate that SAFE is a powerful new tool for examining biological networks and advancing our understanding of the functional organization of the cell.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

A global interaction network maps a wiring diagram of cellular function (2016, methodology): “1, A to C), we applied spatial analysis of functional enrichment (SAFE), which identifies dense network regions associated with specific functional attributes (15).”
neuromaps: structural and functional interpretation of brain maps (2022, methodology): “In adjacent fields, such as bioinformatics, there already exist multiple widely-used computational methods for functional profiling and pathway enrichment analysis of gene lists [6, 61].”
DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier (2017, methodology): “For this purpose, several methods use proteinprotein interaction networks to identify significant links between proteins that can be used to transfer functions, or significant network patterns that may be predictive of a function (Nguyen et al., 2011; Kirac and Ozsoyoglu, 2008; Jiang and McQuay, 201”
High-Density Proximity Mapping Reveals the Subcellular Organization of mRNA-Associated Granules and Bodies. (2018, methodology): “We first coupled PCC analysis (Figure 5) to the automated network annotation tool SAFE (Baryshnikova, 2016), resulting in ten prey groups enriched for specific GO cellular component (CC) terms that largely corroborated our manual annotation (Table S6).”