The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

Panagiotis Moulos 0 1 3 4 Julie Klein 2 Simon Jupp 2 Robert Stevens 2 Jean-Loup Bascands 0 1 3 4 Joost P Schanstra 0 1 3 4 0 Universite Toulouse III Paul-Sabatier , 118 route de Narbonne, 31062 Toulouse , France 1 Institut National de la Sante et de la Recherche Medicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease , 1 avenue Jean Poulhes, 31432 Toulouse , France 2 School of Computer Science, University of Manchester , Kilburn Building, Oxford Road, Manchester M13 9PL United Kingdom 3 Universite Toulouse III Paul-Sabatier , 118 route de Narbonne, 31062 Toulouse , France 4 Institut National de la Sante et de la Recherche Medicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease , 1 avenue Jean Poulhes, 31432 Toulouse , France Background: Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. Results: In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. Conclusions: The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner. - Background During the past decade major advances in biological research, mainly in the field of high throughput analysis (e.g. omics), has led to an exponential increase in available experimental data, produced through a variety of techniques including DNA, miRNA [1] and antibody arrays [2], next generation sequencing technologies [3] and mass spectrometry [4]. This switch in life sciences towards multi-omics approaches has created a gap in the provision of bioinformatics tools capable of combining this data. More importantly, there appears to be a shortage in efficient tools that would aid the bench biologist to i) simplify and categorize the results of a multi-omics approach which usually come into the form of long lists ii) visualize different information layers which can be mined from multi-omics approaches and aggregate useful pieces into biochemical pathways and/or biological function groups iii) combine steps (i) and (ii) in a repeatable and reusable fashion so as to extract meaningful outcomes regarding the biological system under investigation and iv) combine all the aforementioned steps to formulate plausible hypotheses, possibly applied to similar systems (e.g. systems functioning in the same tissue/organ/similar disease situation) and eventually design new experiments for hypothesis validation. The renal biology field is facing similar problems. Although kidney diseases have been extensively studied in different species (i.e. human, mouse, rat), this wealth of information remains hidden across several layers of public data and/or literature repositories. Although considerable effort has been devoted in aggregating data from several resources [5-7], the resulting databases fail to meet the multi-omics attribute and remain dispersed across the web. To address this problem, we developed the Kidney and Urinary Pathway Knowledge Base (KUPKB) [8], a publicly available repository which organizes an important amount of existing knowledge regarding renal tissue, cell and disease categorization, using Semantic Web technologies [9]. The KUPKB can be queried through the user-friendly iKUP browser, [8], accessible at http://www.kupkb.org. The iKUP comprises a powerful tool in terms of speed, selectivity and descriptive power. Nevertheless, its nature restricts the user to viewing the query results in tabular format, only skimming the surface of the rich interconnected data otherwise available in the KUPKB. In addition, although it succeeds in displaying findings hidden in scattered repositories, such as the expression of a set of genes under a very specific combination of kidney tissue, cellular type and disease, it remains unable to map this information to interaction networks available as background information and in a multiple species manner. The value of biological network representations has been extensively analyzed in bioinformatics and systems biology literature [10,11]. Some important aspects include the ability to capture fixed snapshots of cellular states [12] otherwise hidden in tables, to infer functional associations [13], to reduce complexity by combining protein interaction, gene expression and metabolic profiles in a single image [14], and perform pattern recognition in a network snapshot [15]. In this article, we describe KUPNetViz, an interactive biological network querying and visualization application. Its main purpose is to assist renal scientists to extend their research by providing an alternative KUPKB data image depicting interactions among the queried molecules and their neighbors, coupled with functional and biochemical pathway annotation in both a species dependent and independent manner. The main tasks supported and promoted by KUPNetViz are: i) the display, exploration and manipulation of general proteinprotein interaction as well as functional and biochemical pathway association networks for a number of widely studied mammalian species, ii) the transformation of these general networks to kidney specific networks through their association with related gene/protein/ miRNA expression datasets, iii) the mining of possibly hidden relationships among co-regulated and/or directly interacting (neighboring) entities under s (...truncated)