Network-based functional enrichment

<p>Abstract</p> <p>Background</p> <p>Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account.</p> <p>Results</p> <p>Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i) determine which functions are enriched in a given network, ii) given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii) given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms.</p> <p>Conclusions</p> <p>We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are implemented in C++ and are freely available under the GNU General Public License at our supplementary website. Additionally, all our input data and results are available at <url>http://bioinformatics.cs.vt.edu/~murali/supplements/2011-incob-nbe/</url>.</p

Reverse engineering molecular hypergraphs

Analysis of molecular interaction networks is pervasive in sys-tems biology. This research relies almost entirely on graphs for modeling interactions. However, edges in graphs cannot represent multi-way interactions among molecules, which oc-cur very often within cells. Hypergraphs may be better rep-resentations for such interactions, since hyperedges can natu-rally represent relationships among multiple molecules. Here we propose using hypergraphs to capture the uncer-tainty that is inherent in reverse engineering gene-gene net-works from systems biology datasets. Some subsets of nodes may induce highly varying subgraphs across an ensemble of high-scoring networks inferred by a reverse engineering al-gorithm. We provide a novel formulation of hyperedges to capture this uncertainty in network topology. We propose a clustering-based approach to discover hyperedges. We show that our approach can recover hyperedges planted in synthetic datasets with high precision and recall. We ap-ply our techniques to a published dataset of pathway struc-tures inferred from quantitative genetic interaction data in S. cerevisiae related to the unfolded protein response in the en-doplasmic reticulum (ER). Our approach discovers several hy-peredges that capture the uncertain connectivity of genes in specific pathways and complexes related to the ER. Our work demonstrates that molecular interaction hyper-graphs are powerful representations for capturing uncertainty in network structure. The hyperedges we discover directly suggest groups of genes for which further experiments may be required in order to precisely discover interaction patterns

The Evolutionary Histories of Clinical and Environmental SHV β-Lactamases are Intertwined

The rise of antibiotic resistant pathogens focuses our attention on the source of antibiotic resistance genes, on the existence of these genes in environments exposed to little or no antibiotics, and on the relationship between resistance genes found in the clinic and those encountered in non-clinical settings. Here, we address the evolutionary history of a class of resistance genes, the SHV β-lactamases. We focus on bla(SHV) genes isolated both from clinical and non-clinical sources and show that clinically important resistance determinants arise repeatedly from within a diverse pool of bla(SHV) genes present in the environment. While our results argue against the notion of a single common origin for all clinically-derived bla(SHV) genes, we detect a characteristic selective signature shaping this protein in clinical environments. This clinical signature reveals the joint action of purifying and positive selection on specific residues, including those known to confer extended-spectrum activity. Surprisingly, antibiotic resistance genes isolated from non-clinical -- and presumably antibiotic-free -- settings also experience the joint action of purifying and positive selection. The picture that emerges undercuts the notion of a separate reservoir of antibiotic resistance genes confined only to clinical settings. Instead, we argue for the presence of a single extensive and variable pool of antibiotic resistance genes present in the environment