Unified Alignment of Protein-Protein Interaction Networks

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others

Simple Topological Features Reflect Dynamics and Modularity in Protein Interaction Networks

<div><p>The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the <i>S. cerevisiae</i> interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, <i>S. cerevisiae</i>, <i>H. sapiens</i>, <i>D. melanogaster</i>, <i>A. thaliana</i>, and <i>E. coli</i>, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes.</p></div

Computational analysis of genetic interaction network structures and gene properties

University of Minnesota Ph.D. dissertation. July 2017. Major: Computer Science. Advisor: Chad Myers. 1 computer file (PDF); viii, 155 pages.Cellular systems are responsible for many complex tasks, such as carrying out cell cycle phases, responding to intra- and extra-cellular conditions, and resolving errors. Through analysis of biological networks, researchers have begun to describe how cells coordinate these processes by means of modularity and between-process connections. However, descriptions of this network-based cellular organization often do not incorporate the diverse characteristics and individual behaviors of the genes that compose it. Knowledge of gene properties and their relationships with biological network evolution is crucial for a complete understanding of cellular function, and investigation in this area can lead to general principles of biology that apply to many species. This dissertation will describe analyses of the Saccharomyces cerevisiae (baker’s yeast) genetic interaction network that connect gene topological behavior with various physical, functional, and evolutionary properties of genes. Genetic interactions occur between paired genes whose simultaneous mutations produce unexpected double-mutant phenotypes, which are indicative of a range of functional relationships. Because genetic interactions can be identified genome-wide in high-throughput experiments, their networks are comprehensive and unbiased representations of function to which we can apply computational methods that search for structure-function relationships. We begin by exploring the association between a set of gene properties and gene genetic interaction (GI) degree. Here, we build a decision tree model that sorts genes based on a set of properties, each of which has a correlation with GI degree, and accurately predicts GI degree. We show that our model, trained on S. cerevisiae, is also accurate for a very distant yeast species, Schizosaccharomyces pombe, demonstrating that the rules governing gene connectivity are well conserved. Finally, we used predictions from the model to identify gene modules that differ between the two yeast species. Next, we further characterize hub genes through an investigation of pleiotropy, the phenomenon of a single genetic locus with multiple phenotypic effects. Pleiotropy has typically been described by counting organism-level phenotypes, but a characterization based on genetic interactions can capture details about cellular processes that are buffered by the cell and never manifest in single mutant cellular phenotypes. For this analysis, we use frequent item set mining to discover GI modules, which we annotate with high-level processes, and use entropy to measure the functional diversity of each gene’s set of containing modules, thus distinguishing between genes whose functional influence is limited to very few bioprocesses and those whose roles are important for varied cellular functions. We identified a number of gene and protein characteristics that differed between genes with high and low pleiotropy and discuss the implications of these results regarding the nature and evolution of pleiotropy