Vaja Liluashvili

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

Visualizations of biomolecular networks assist in systems-level data exploration in many cellular processes. Data generated from high-throughput experiments increasingly inform these networks, yet current tools do not adequately scale with concomitant increase in their size and complexity. We present an open source software platform, interactome-CAVE (iCAVE), for visualizing large and complex biomolecular interaction networks in 3D. Users can explore networks (i) in 3D using a desktop, (ii) in stereoscopic 3D using 3D-vision glasses and a desktop, or (iii) in immersive 3D within a CAVE environment. iCAVE introduces 3D extensions of known 2D network layout, clustering, and edge-bundling algorithms, as well as new 3D network layout algorithms. Furthermore, users can simultaneously query several built-in databases within iCAVE for network generation or visualize their own networks (e.g., disease, drug, protein, metabolite). iCAVE has modular structure that allows rapid development by addition of algorithms, datasets, or features without affecting other parts of the code. Overall, iCAVE is the first freely available open source tool that enables 3D (optionally stereoscopic or immersive) visualizations of complex, dense, or multi-layered biomolecular networks. While primarily designed for researchers utilizing biomolecular networks, iCAVE can assist researchers in any field.

Abstract Visualizations of biomolecular networks assist in systems-level data exploration in myriad cellular processes in health and disease. While these networks are increasingly informed by data generated from high-throughout (HT) experiments, current tools do not adequately scale with concomitant increase in their size and complexity. We present an open-source software platform, interactome-CAVE, (iCAVE), that leverages stereoscopic (3D) immersive display technologies for visualizing complex biomolecular interaction networks. Users can explore networks (i) in 3D in any computer and (ii) in immersive 3D in any computer with an appropriate graphics card as well as in CAVE environments. iCAVE includes new 3D network layout algorithms in addition to extensions of known 2D network layout, clustering and edge-bundling algorithms to the 3D space, to assist in understanding the underlying structures in large, dense, layered or clustered networks. Users can perform simultaneous queries of several databases within iCAVE or visualize their own networks (e.g. disease, drug, protein, metabolite, phenotype, genotype) utilizing directionality, weight or other properties by using different property settings. iCAVE has modular structure to allow rapid development by the addition of algorithms, datasets or features without affecting other parts of the code. Overall, iCAVE is a freely available open source tool to help gain novel insights from complex HT datasets.

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.