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 6 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 protein-protein 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 several combinations of kidney anatomies and/or disease models and iv) the extrapolation of both expression data and network interactions across different diseases, anatomies but also different species facilitating the quick formulation and screening of biological hypotheses and the design of new experiments.

The molecule and interaction visual representations in KUPNetViz have been inspired by previous work in the field 16 with several additions and significant modifications to fit for use. To demonstrate its value and necessity in kidney research studies and its ability to complete the aforementioned tasks, we present two usage examples that exemplify that the KUPNetViz clearly extends the functionalities of the iKUP. Specifically, we found substantial additional evidence for a role of calreticulin in renal disease in humans 8 by showing that calreticulin is involved in a larger interaction network in renal graft rejection. In addition, we propose a novel association of the inflammatory axis interleukin-6 (IL6)/IL6 receptor with the progression of polycystic kidney disease.

To demonstrate the added value of the KUPNetViz we present two case studies, revealing insights that would require significant effort to mine using the iKUP only. Specifically, we explored the role of calreticulin, a protein involved in renal disease in animals, in a larger interaction network in renal graft rejection (interstitial fibrosis and tubular atrophy) and its association with other functional and pathway elements and the association of the inflammatory axis interleukin-6 (IL6)/IL6 receptor with the progression of polycystic kidney disease.

The wealth of biological information regarding kidney disease that can be extracted from the current version of the KUPKB is currently presented in tabular format. Given the possibly long returning lists of molecules, the nature of the knowledge base and the fact that it contains hierarchical and protein interaction data, we sought to visualize these interactions through a gene network visualization module. Prior to its development we tried to map experimental data to some of the numerous existing software packages that have been developed for biological network visualization and exploration and at the same time suited at least partially our needs (mapping of possible multi-omics data, integrated background databases, customizable data sources). These tools included EGAN, 16 and Biological Networks, 41 . In the case of EGAN, the feedback included the excessive amount of displayed information, the view complexity derived from the multiple data sources, the inability to simultaneously display expression data from multiple experiments and the moderate performance of the application regarding big networks. Another major issue with EGAN is the lack of directionality in gene-to-gene edges. On the other hand, in the case of Biological Networks, the users were overwhelmed mostly by the amount of functionalities, the complex interface and the multiple background knowledge data sources, features that might fascinate a bioinformatician but often frustrates a bench biologist seeking clear-cut specific answers instead of heavy and complex multi-functionalities. In addition, Biological Networks requires a local installation which is often a disadvantage, mostly in terms of maintenance.

The above tools comprise only a small example out of a nowadays large pool of tools allowing the combined view of protein-protein interaction networks and abundance data 14 . Leaving aside the domain-specific nature of KUPNetViz, most of the packages presented in the first table of 14 meet the criteria of combined views of interaction and abundance data (e.g. VANTED, 42 , GENeVis, 43 ) and are equipped with high-level functionalities (e.g. network clustering algorithms, 44 dimensionality reduction methods, 45 , visualization of extremely large networks, 46 , and network module detection, 47 ). However, although the above tools excel in network-related functionalities and visualization quality, none of them comes with a complete and yet simple set of bundled background databases (protein-protein interaction, element annotation, functional and pathway associations) in one place, a fact which is essential for the researcher. Other solutions including Pajek, 48 , and yEd ( http://www.yworks.com/en/products_yed_about.html) comprise also excellent network analysis tools but are very generic and not focused to biological problems which may also confuse the biologist.

Another drawback with many current applications is that they are desktop-based while there is a current trend in software development to switch from desktop to web-based applications for a variety of reasons including flexibility, maintenance and platform independence. Thus, although there are certain web-based tools, based on Java^TM webstart technology (VANTED, 42 ) or related technologies (VisANT, 13 ), they still have limitations similar to the ones described for certain desktop applications, and mainly the lack of a small but adequate set of bundled background databases and a relatively simplistic interface which does not require prior training. The KUPNetViz application, although simplistic in its basic concepts, aspires to initiate a set of domain-specific integrative visualization tools that focus in comprehensive interfaces. These interfaces could be used to answer biological questions from the first usages, without demanding from the user to go through long manuals, time-consuming database and literature searches to annotate the various entities in the network and understand basic mathematical concepts of graph theory.

Even though the developers of several biological network visualization and data integration packages have put substantial effort in the simplicity and clarity of the outcome, some of them can only be fully exploited by trained bioinformaticians while others require prior training in the form of workshops or seminars in order to be used at their full potential. The development of the KUPNetViz application was a continuous interaction between computer scientists and renal biologists, where several features were added, removed or re-implemented in the basis of the arguments that the application should remain simple to use, self explanatory and it should provide simple biological network snapshots. As a result, the tool manages to keep the network display and the functionalities as simple as possible by hiding and/or automating several technical aspects and mathematical properties of the biological graph. At the same time, it does not restrain more advanced users from exploring other possible mathematical properties of the resulting networks as it allows their export in text formats that can be imported to numerous existing graph analysis packages for further analysis. Additionally, taking into account a recent trend in the development of bioinformatics tools 49 , which focuses to the application of user-centered design methods, the KUPNetViz comprises a good example of such a strategy, given the time that was required for the case studies to be completed and the feedback from biologists that used the application.

To our knowledge, one of the novelties of the KUPNetViz, rare among other biological network visualization tools, is the ability to visualize gene-to-gene relationships in a multispecies manner, while at the same time maintaining the simple minimalistic network display. The majority of current packages focuses either on the alignment of known biochemical networks among different species (e.g. Osprey, 50 or VANLO, 51 ), allowing the user to visualize but not automatically extrapolate possible interactions that may apply across species, or on the manual building of such inter-species interaction networks based on multiple available data sources (e.g. Biological Networks, 41 ). In addition, the multiple network alignment or the manual network building is usually performed with a certain cost in application and visualization simplicity, often discouraging the simple or the hurried user.

The KUPNetViz goes one step further by allowing the possibility of extrapolation of biomolecular relationships from one species to another while at the same time maintaining its straightforward visual interface and graph views (Additional file 1). This creates the unique possibility to combine –omics data from different animal models and/or human disease and quickly screen for reasonable hypotheses. As the KUPNetViz supported organisms are not equally extensively annotated in the literature (e.g. mouse is better annotated for developmental functions than human 52 ), the multi-species functionality allows the extrapolation of gene-to-gene relationships and gene/protein expression data from one organism to another. While some relationships between molecules may differ between species, most relationships and biological pathways can be considered evolutionary conserved and thus allow the easy formulation of extrapolated hypotheses, regarding both biomolecular relationships and gene/protein expression under certain pathological conditions across species. The researcher can then judge the fundamental validity of these hypotheses and proceed either by rejecting a hypothesis and formulate another, or by creating a list of plausible hypothesis which can ultimately be verified in the lab.

Furthermore, as the whole application can be easily built and deployed in several machines, this modularity renders it easily adaptable to other data sources (e.g. another organ). Thus, with minor modifications, the KUPNetViz may evolve to a more general framework, suitable for organ or tissue specific knowledge bases, which at the same time is not as generic as Cytoscape Web itself but includes the basic bundled knowledge to function as an independent biological network visualization application. Although such an application is not alone in its kind, it manages to keep simplicity, visual complexity and speed at acceptable levels, as compared with the two aforementioned applications ( 16 41 ). A schematic representation of the KUPNetViz modularization is depicted in Figure  4.

The reasoning behind the construction of a MySQL backend database to support the application instead of using the KUPKB semantic web repository includes three main arguments: firstly, during the development we sought to maintain software modularity and re-usability even though in its current version, the visualization application is adjusted to kidney. Thus, we considered that an RDB model (especially regarding the background knowledge part) can be adjusted easier to other similar experimental data sources (e.g. a different organ) or data sources of different structure (e.g. multi-omics data from different types of cancer), requiring minor modifications only in the user interface part. Secondly, not all the background knowledge data sources that are used by the KUPNetViz were initially incorporated in the semantic web model (e.g. the protein-protein interactions or the reference pathways). This does not suggest a KUPKB model design problem but rather highlights the different initial purpose of the model which was not network visualization, but the detailed knowledge representation of kidney anatomies and disease models and the experimental data mapping and annotation to this knowledge representation model. Finally, during the design process, we realized that not all of the knowledge stored in the semantic web repository (e.g. the highly detailed localization and hierarchization of cell types and kidney tissues) was required for the minimalistic approach promoted by KUPNetViz. Thus, we used an RDB model to reduce query complexities and running speeds. In addition to the latter, the backend database building time, including the download and parsing of the background knowledge files and the re-parsing of the experimental data requires less than three hours while the rebuild of the semantic web repository requires over a day (the times were measured on the same computer). This allows the easier maintenance of the KUPNetViz and the more frequent synchronization of the backend database with the latest releases of the incorporated background knowledge sources.

KUPNetViz is a user-friendly biological network visualization tool dedicated to renal research. This tool uses data gathered from multiple resources on several renal pathological states and background knowledge elements regarding biomolecular interactions as well as functional and biochemical pathway associations and creates biological network snapshots that can shed light on mechanisms involved in renal disease. It provides renal biologists with an alternative network data representation, complementary to the functionality of the iKUP exploration tool with a small cost regarding the display complexity. Moreover, maybe its most important feature is the biomolecular interaction extrapolation across species which allows the researcher to quickly formulate and screen several hypotheses in a simple manner. We have demonstrated the value of KUPNetViz in two case studies, further investigating the role of calreticulin as a key player in a gene network mostly up-regulated in renal graft rejection and newly investigating the potential involvement of IL6 and the IL6 receptor in the progression of polycystic kidney disease.

The value of the KUPNetViz in kidney research will be increased in parallel with the number of related public multi-omics datasets which will be also available in the KUPKB. The expertise of the KUPNetViz hosting group in renal biology guarantees its constant curation and addition of related datasets, as these are getting published. This will potentially lead to increased confidence in the observed networks as additional biological evidence will eventually pile up from those constantly added datasets. The addition of datasets is easy and straightforward, since data are entered in ready-to-fill Excel sheets by biologists and incorporated by a single command line. We are aiming to the inclusion of the latest omics data sets in the KUPKB at least once a year from which the KUPNetViz will directly benefit from. Future extensions include the incorporation of additional biomolecule annotation and interaction resources (KEGG compounds) and the addition of a module that will identify highly enriched and over-represented biological functions and biochemical pathways using the StRAnGER algorithm 53 .

Home page: Accessible through the website of KUPKB and the iKUP browser (tab “KUPKB Network Visualizer”) at http://www.kupkb.org or directly at http://www.kupkb.org/vis/index.php.

Operating system: KUPNetViz is a web-based application thus it is platform independent.

Requirements: KUPNetViz is best used and viewed under Internet Explorer 8 or higher, Mozilla Firefox, Google Chrome, Apple Safari and Opera. The use of Internet Explorer 7 and lower is not recommended.

License: KUPNetViz is free for academic use but requires a license from the authors for any commercial purposes. The software is available without user registration.

Further information: An analytical user’s guide coupled with usage examples is available at the application’s homepage and as supplementary material online (Additional file 2).

KUP: Kidney and urinary pathways; KUPKB: Kidney and urinary knowledge base; KUPNetViz: Kidney and urinary pathway network visualizer; GO: Gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; PKD: Polycystic kidney disease; CKD: Chronic kidney disease; IL6: Interleukin 6; IL6R: Interleukin 6 receptor; RDB: Relational database.

The authors do not declare any conflicts of interest.

PM conceived, designed and implemented the software as a whole, tested the software and drafted the manuscript. JK and JPS tested the software, suggested interface modifications, performed the case studies and contributed in drafting the manuscript. SJ and RS contributed to the database design and the integration of KUPNetViz to the iKUP browser. JB contributed in drafting the manuscript and data management. All authors have read, revised and approved the manuscript.