A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools. PLOS Computational Biology Software ArticleThis is a The total volume of computational pathway data mapped by biologists has entered a rapid growth phase. There are more than 100,000 biochemical reactions and more than a million molecular interactions in publicly available 325 on-line resources Despite these promising studies, the use of computable pathway information in biology is still in its infancy. Pathway data sources were originally developed to use their internal pathway representation, resulting in a heterogeneous set of resources that are difficult to combine and use. There are two key technical challenges we need to address to streamline the use of this rapidly expanding information : (i) standardizing and integrating pathway data from multiple heterogeneous sources and (ii) developing methods and tools that can help scientists easily access, map and analyze pathway information.BioPAX  The second key challenge is to build methods, algorithms and software tools to work with pathway data to answer biological questions At the core of Paxtools is a complete and consistent object-oriented implementation of the BioPAX specification. A BioPAX record includes pathways, interactions, reactions and their participants. These elements are implemented as Java beans that have methods to access and manipulate the properties as described in BioPAX specification. The Model class acts as a container that allows adding, removing and searching for elements.BioPAX uses OWL semantics that are not automatically covered by object oriented languages such as Java. Properties can be symmetric, transitive or subtyped into other properties. For example, a protein binding relationship is symmetric, if A binds to B, the inverse is also true. Also, the “standard name” property is a subtype of “name” property, so updating the standard name of the protein should also update its list of names. Paxtools implements these additional semantics and automatically updates object fields.In the BioPAX specification, properties are unidirectional for brevity. For example, the “participant” property links interactions to physical entities. Paxtools provides additional “inverse” links for key properties that allow efficient bidirectional navigation. These inverse links are also automatically updated when the forward link is updated.Paxtools includes two input/output handlers for reading and writing BioPAX files. Jena based IO can handle most OWL encodings but can be relatively slow and demands a significant amount of memory for very large files. The StaX based “Simple” IO handler class can only read BioPAX formatted as RDF/XML and does not have the flexibility of the Jena handler but it can read hundreds of thousands of elements within minutes with a very small memory footprint.On top of this foundation Paxtools implements methods, algorithms and tools to solve common tasks encountered while working with pathway data . Some coThe “Controller” package allows users to manipulate BioPAX models without actually hard coding property and class names, define XML Path Language-like queries and traverse the object graph efficiently. An example which implements an object inspector to display the properties of objects can be found in the user's guide (Text S1). This pattern considerably simplifies development of BioPAX exporters and other tools and makes it easier to extend and update them to support future changes in the BioPAX specification.Paxtools offers an array of graph search algorithms that are specifically targeted towards answering common biological questions. For example a mutually exclusive relationship between the mutations observed in two genes in a set of drug-resistant tumors can be explained by a common downstream pathway. Similarly, an observed correlation in gene expression between two genes can be caused by a signaling path that connects one gene to the control of the expression of the other. Starting from a set of entities, users can find common upstream and downstream events, feedback loops, connecting paths and networks of interest . A list Working with BioPAX often requires complex manipulations to merge models from different sources, extract subparts or reduce them to simpler formats . PaxtoolPaxtools can convert Proteomics Standards Initiative - Molecular Interaction (PSI-MI) The recently released BioPAX Level 3 introduced significant extensions to previous levels with some loss of backwards compatibility. Paxtools supports all three BioPAX levels  and provides facilities for upgrading older BioPAX models to Level 3.Each operation that modifies the model is internally validated by Paxtools to comply with BioPAX syntax, including RDF well-formedness, domain and range restrictions, bidirectional links, and redundancies. This is especially useful for tools that create BioPAX such as pathway editors, converters or exporters as it allows early detection of errors, increasing the quality of the produced BioPAX data. This syntactic validation does not include more detailed checking of semantics that is performed by the BioPAX validator.Paxtools comes with mappings for Java Persistence API that allows out-of-the-box persistence of BioPAX models into relational database systems, such as MySQL. This is an especially important feature for working with large biopax models, such as complete pathway database exports.Paxtools source code is currently distributed as a modular Maven project which allows developers to easily select just the parts of Paxtools they need in their application. The Paxtools core module, which provides a complete implementation of BioPAX and read in/write out functionality, is a very compact library (400 kB) that can be expanded as required with additional modules, such as those described above.Paxtools implements all of these features in a lightweight, extensible structure. Paxtools can be embedded in a software tool as a library, can be used for programmatic access to pathway data with Java or Jython or can be used to run most commonly used Paxtools algorithms and searches from the command line. Here, we briefly review several applications where Paxtools was used to answer biological questions or develop bioinformatics tools.paths-between query, implemented in the Query package that finds the paths in the network that connect the seed genes. Entities will be mapped using UniProt identifiers and the algorithm will return the connecting sub-network in BioPAX. This BioPAX network can be visualized or used for subsequent analysis. An implementation of this scenario can be found in cBio Cancer Genomics Portal When an experimental method identifies a set of genes associated with a phenotype, such as prostate cancer, cellular processes that connect these genes can offer a causal explanation. In this scenario, a user writes a small Java program that loads the cellular process network curated by a pathway database. Alternatively, instead of using BioPAX exports, the data can be queried dynamically from PC using the PC-client module. The program then runs a In this use case, a set of high throughput profiles are used in conjunction with pathway information to find significantly altered modules. Due to the nature of the experimental data, detailed BioPAX information, such as the phosphorylation states of interacting proteins is unnecessary in this case. To facilitate analysis, complex BioPAX pathways can be reduced to a simple interaction network using the SIF converter that effectively merges multiple states of the entity into a single node. Alteration profiles can be directly mapped onto these “entity” nodes using external references to Entrez Gene and UniProt databases as defined in BioPAX models. Existing clustering and module-finding algorithms Visualization is key for understanding complex relationships between biological entities. Paxtools significantly facilitates implementing Pathway visualizers and editors by handling the reading/writing and syntactic validation. Paxtools also provides additional common functionality for interactive tools such as undo/redo and object inspection. Finally, the Pathway Commons client enables pathway visualization tools to directly access and query Pathway Commons. Pathway Editors and visualizers that use Paxtools for these purposes include ChiBE The effect of post-translational modifications on protein activity can be useful for assessing the function of mutations in the vicinity of a modification site. Specifically, we would like to know if a given post-translational modification causes a protein to gain or lose a function. We can search pathways for processes that modify a protein and predict how this change will affect the controlled downstream processes. The algorithm also needs to consider processes that contain the protein as a part of molecular complex or as a part of a protein family. Since these inclusion relationships and the type and location of the modifications are stated explicitly in BioPAX, equivalent protein modifications can be matched to integrate findings from different pathways. A complete implementation of this algorithm can be found in the Paxtools code.http://lists.sourceforge.net/lists/listinfo/biopax-paxtools. Sample code that demonstrates basic tasks and operations can be found in the org.biopax.paxtools.examples package.Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in A rapidly growing open-source BioPAX software infrastructure, available as part of the Pathway Commons project, is directly built on top of Paxtools, including a state-of-the-art persistence and full-text search system cPath2, an advanced validator that allows checking complex rules and best practices, a pathway alignment tool and a framework for quickly mapping experimental data on BioPAX pathways. Since these tools use a common API, it is possible to combine and re-use software components across multiple applications. Detailed examples of using Paxtools for real-life applications can be found in these projects. We encourage users to contribute to the codebase and expand it to build a pathway informatics platform that addresses the growing needs of the community.One of our major future goals is to reach a wider community of researchers by implementing different language bindings and usage modes for Paxtools. We are planning to include bindings for the R statistical programming language and implement an interactive scripting mode for more efficient non-programmatic manipulation.Paxtools can significantly reduce the time spent parsing, normalizing and querying BioPAX information. Without removing these barriers, it is difficult to tap into the rapidly growing computable pathway information corpus produced by multiple pathway databases and curation groups. Paxtools allows researchers to shift their effort from data plumbing to answering biological questions.Protocol S1Paxtools User's Guide. This guide provides details on how to obtain and use Paxtools. It also provides examples and technical details for the key features as well as a frequently asked questions section.(PDF)Click here for additional data file.Software S1The complete source code for all Paxtools modules as of the 4.2.0 release as a single bundle. The latest sources and release bundles can be obtained from biopax.org/paxtools.(TAR)Click here for additional data file.


In silico resources are tools used to aid the understanding of cellular signaling pathways. Systems approaches have provided a deeper knowledge of diverse biochemical processes, including individual metabolic pathways, signaling networks and genome-scale metabolic networks. In the future, these tools will be enormously valuable, if they continue to be developed in parallel with growing biological knowledge. In this study, an overview of the bioinformatics resources that are currently available for the analysis of biological networks is provided.In the last several years, researchers have exhibited an intense interest in the evolutionarily conserved signaling pathways that have crucial roles during embryonic development. Interestingly, the malfunctioning of these signaling pathways leads to several human diseases, including cancer. The chemical and biophysical events that occur during cellular signaling, as well as the number of interactions within a signaling pathway, make these systems complex to study.  The term “systems biology” has emerged recently to describe the frontier of cross-disciplinary research in biology. The suffix “Omics” encompasses a variety of new technologies that can help explain both normal and abnormal cell pathways, networks, and processes via the simultaneous monitoring of thousands of molecular components. Omics studies encompass many technologies, including genomics , transcriptomics (the quantitative study of RNA and gene expression) , proteomRecent experimental and theoretical work has begun to supply lacking biological details. In a small but growing number of systems ,11, the The evasiveness of the gene concept has become fully apparent only in the last decade  with theEnormous progress has been made in understanding critical cellular processes, such as cell cycle regulation, DNA repair, apoptosis, transcription, cell migration, and matrix structure, which are essential to the understanding and treatment of cancer –20. HoweIn the past 10 years, new technologies have been developed that have generated extensive genomic and proteomic data, as well as other types of genome-wide information ,26. AddiThis review considers the chemical and physical types of complexity within cancer signaling pathways. Furthermore, the tools available to analyze signaling network data, which are accessible to specialized bioinformaticians and general research scientists, are detailed.The full complexity and the multidimensional nature of biochemical networks that take into account the genome, transcriptome, proteome, and metabolome data highlight the intricacy and interdependency of these systems . UnderstThe types of networks that are currently available include, but are not limited to, protein interaction networks, genetic interaction networks, transcription factor-target regulation networks, miRNA-target regulation networks, kinase-substrate phosphorylation networks, and metabolic pathway networks.Analogous to a sequence analysis, the development of a biological network has three similar stages: (1) the construction of the network by large-scale experimentation and computational predictions –30, (2) i.e., either the edges are undirected or directed but reversible). In this study, the term reversibility means that a reversed edge is biologically possible between a pair of nodes. Regulatory networks have irreversible edges . By this definition, transcription factor-target regulation networks, miRNA-target regulation networks, and kinase-substrate phosphorylation networks fall into the regulatory network group and protein interaction networks, genetic interaction networks, and metabolic networks fall into the collaborative network group [Biological networks are characterized by their functional relationships, such as the binding, expression, regulation, and phosphorylation of proteins. Biological networks could be categorized as collaborative or regulatory networks. Collaborative networks are the biological networks with reversible edges . Unknown relationships are accounted for by data mining or reverse-engineering approaches that extract patterns and relationships from high-throughput data. Both of these approaches rely on the development of experimental methods that analyze the interaction, expression, and localization of biological molecules in a quantitative manner.Along with the ability to generate a large amount of data per experiment, high-throughput technologies have also brought experimental challenges, such as translating large amounts of data into a better understanding of biological phenomena. Independent of the platform and the analysis methods used, in many cases, the results of a high-throughput experiment are lists of differentially expressed genes. Translating these lists into a better understanding of underlying biological phenomena is a common challenge faced by all researchers. In particular, placing high-throughput data into context within a whole organism remains a daunting challenge.Z-score. PathwayProcessor [Currently, the overrepresentation approach  and the rocessor , PathMAProcessor , Cytoscarocessor , and Patrocessor  use Fishrocessor  offers brocessor  and Pathrocessor  focus oni.e., the genome, transcriptome, proteome or metabolome).Pathway-level analysis is a powerful approach that enables the interpretation of post-genomic data at a higher level than the individual biomolecules . The eviThe field of bioinformatics has blossomed in the last 10 years, and as a result, a large, increasing number of researchers are generating computational tools to solve problems relevant to biology. While many systems biology approaches involve mathematical and computational modeling, the development, maintenance, and dissemination of tools for systems biology are significant challenges. Examples of these challenges include the development of data repositories, data standards and software tools for the simulation, analysis and visualization of system components, such as biochemical networks . AnotherThe practice of systems biology seeks to comprehend the complexity of organisms or organism subsystems by combining many different kinds of data, such as mRNA levels, protein levels, protein-protein interactions, protein-DNA interactions, protein modifications, and biochemistry data, to create predictive models . The comin silico knowledge is greatly needed because the integral complexity of most biological processes is beyond what is comprehensible to the human mind. Therefore, these “systems biology databases” often provide important foundations for the quantitative modeling of biological systems. In certain cases, these databases allow for the direct export of mathematical models. Additionally, the first collections of the mathematical models of biological processes have been developed (databases of models), which solely archive and curate the mathematical models in the Systems Biology Markup Language (SBML) for future use and refinement [As biological research accelerates through the development of new technologies and instrumentation, biological databases have become an indispensable part of scientific research. The construction and maintenance of primary databases, such as GenBank  and Protfinement . Tools ffinement   was to improve the field of bioinformatics by designing and implementing new, flexible information and communication technology tools. These tools should be able to support the analysis of biological data, partially distribute the computation burden of the large amounts of high-throughput data, and reduce the need to transfer large amounts of data. From this perspective, software agents can play a major role. The scope of the Working Group was to promote collaborations between software agents and the bioinformatics communities with the aim of creating synergies for the modeling of complex biological systems .The combination of software agents with bioinformatics presents a two-fold opportunity. First, the domain of bioinformatics, which contains extensive and growing database resources and analysis tools, provides an appropriate domain for the application of agent technologies. Second, the combination of agents with bioinformatics allows for the deployment and testing of agent systems in a real-world setting with the possibility of making substantial contributions to human society. Additionally, a distinct and identified need exists for good solutions to improve the performance of existing bioinformatics systems, and agents may be able to contribute to this needed improvement. In this sense, there is a notably strong synergy between the two domains. This picture is both enhanced and complicated by the introduction of relevant infrastructure technologies that facilitate both bioinformatics and agent-based computing. For example, the Grid has become increasingly important to both communities and suggests a convergence to a service-oriented vision of bioinformatics underpinned by Grid-based virtual organizations. However, significant challenges still exist. Researchers from both communities generally require education in the other domain, and work must be undertaken to ensure that any solutions developed across both areas satisfy the needs of both domains. In many cases, the language of discourse is so distinct that the discussion of key issues becomes problematic. Additionally, the introduction of new technologies, such as the Grid, requires further efforts to understand and adopt the new technology, as well as improve the immaturity of fully deployed systems. Thus, the maturity at the interface is a key challenge.Computational systems biology is a rather new science ; howeverThe cell cycle refers to a sequence of events that leads to the correct duplication of cells . A complSingle-cell measurements and other new technologies have enabled the development of detailed, quantitative models of cell-cycle regulation. Mass spectrometry has provided data on protein level fluctuations during the cell cycle, identified members of important protein complexes, and revealed the phosphorylation states of the Cdk-regulated proteins . Future Several modeling platforms have been used in cell-cycle research . These pSearching for missing values is an example of the types of jobs that computational tools can undertake; missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. Although gene expression microarrays have developed much during the past years, the technology is still rather error prone, resulting in datasets with compromised accuracy and coverage. In particular, the existence of missing values due to various experimental factors still remains a frequent problem, especially in cDNA microarray experiments. Experimental data can also be used to infer undiscovered molecular interactions and propose the existence of protein regulation. Specific useful tools can handle this type of network data , and metAnother layer of complexity in cell-cycle models is the spatial distribution of regulatory molecules. Many crucial events happen in the nucleus, and many molecules are moved in or out of the nucleus during the cell cycle. Despite these localization limits, only a few cell-cycle models have considered the compartmentalization of the cell .Recently, biological modeling has been enriched by new concepts that have helped to delineate cell-cycle models into sub-networks , find thPlacing the core cell-cycle machinery into the larger context of cell physiology, deciphering how a cell copes with checkpoint problems, determining how a cell responds to environmental changes, and understanding why some cells leave the cell cycle to commit suicide remain major challenges for future work. Understanding these issues will lead us to the future goal of understanding how perturbations of the human cell cycle machinery lead to tumor formation. Indeed, the mathematical modeling of cancer development is another active research field –83. VariThe Virtual Cell  is a uniThe Virtual Cell has been deployed as a distributed application that is used over the internet. Users can build complex models with a web-based Java interface to specify compartmental topology, compartmental geometry, molecular characteristics, and relevant interaction parameters. The Virtual Cell automatically converts biological descriptions into a corresponding mathematical system of ordinary and/or partial differential equations. Distinct biological and mathematical frameworks are encompassed within a single graphical interface. A user, competent in mathematics, may directly specify the complete mathematical description of the model by bypassing the schematic interface. The Virtual Cell will then solve the equations by applying numerical solvers and generate appropriate software code to perform and analyze simulations. The results can be displayed and analyzed online or downloaded to the user’s computer in a variety of formats. The software is freely accessible to all members of the scientific community.To understand how a cancer cell is functionally different from a normal cell, it is necessary to assess the changes in the dynamics of overall networks, rather than the changes in the individual pathways. As networks tend to be complex with extensive crosstalk between pathways and important feedback loops, representing networks in the form of a computational model that can be used for a rigorous analysis is crucial. As with most complex diseases, in the instance of cancer, an additional complication arises in that for many aspects of the diseases, several different scales must be integrated. Recognizing dynamic signatures can help improve the diagnosis of cancer, and an understanding of the mechanisms that lead to cancer can help with the design of sophisticated perturbations that can disrupt cancer progression.Despite significant advances in the understanding of tumor biology, improvements in patient outcomes in terms of cure rates and survival times have not met many expectations. In the past 50 years, major advances have been made in scientific knowledge and technology. Additionally, increasing amounts of money are being spent to meet the clinical challenges of cancer. However, further progress is certainly needed.Systems biology has emerged during the past decade as a powerful new paradigm for life-science research. This discipline is based on the premise that the properties of complex systems, which consist of many components that interact with each other in nonlinear, non-additive ways, cannot be understood solely by focusing on the components. The system as a whole has emergent properties that are not visible at the level of the individual parts. The availability of high-throughput data generation technologies, such as DNA microarrays, has made it possible to apply this paradigm in molecular biology and biomedicine. Large-scale transcriptional data allows the focus to shift from individual genes in linear pathways to large-scale networks of interacting genes. In many cases, the properties of the aggregate network, which possibly interact with the extracellular environment, are at the heart of pathological changes, such as cancer. Moreover, the system properties of interest are often embodied in its dynamics. For instance, changes in the structure of the network through mutations or epigenetic effects can lead to changes in the network dynamics that result in different physiological properties. In many cases, especially in the case of cancer, the phenomena at different scales are connected, such as the molecular- and tissue-level scales in tumor growth. The use of mathematical models is the natural framework for the study of the structure and dynamics of networks, which is perhaps the defining characteristic of the systems biology paradigm .A key feature of cancer development is encapsulated in the interactions between the intracellular networks and the extracellular environment. Ultimately, a comprehensive understanding of cancer must link events at the molecular level with events at the tissue-level, organism level, and environmental level. These relationships can only be found with multi-scale mathematical models that integrate quantitative information at different scales, which is precisely the approach of systems biology.To make progress in oncology research, more investigators and grant money have been added; however, these investments may not lead to more effective therapies. Further progress may require an entirely new approach, such as computational oncology. In the past, oncologists and oncology investigators viewed themselves as entirely separate from mathematicians and physicists. Bringing these once-disparate groups together to work on a common problem is long overdue and has spurred the development of the field of computational oncology.in silico models [The study of systems biology requires the modeling of vastly complex components, which interact with each other on many levels. These models are crucial to the understanding of complex biological systems and are referred to as o models . The dysin vitro experiments and animal models to develop and improve the mathematical models, the accuracy of the resulting models has been greatly improved.Using mathematical models to understand the behavior of cancer has generally proceeded in two directions: descriptive models and mechanistic models . DescripAlthough current medicine employs single genes as molecular markers, thousands of markers analyzed simultaneously will probably be used as molecular marker profiles in the future. These molecular-oriented diagnostic techniques will be linked to the prediction and prevention of disease on a molecular level . These din vivo tumor behavior can greatly aid in improving pharmaceutical drug development and create a more rational, predictive approach to the application of therapeutic strategies.The use of systems biology for the study of cancer involves addressing the complex nature of the disease in the human body. Much experimental progress is being made to address these issues; however, the power of computation and computational modeling is still needed to help bridge this gap. The development of combined systems biology computational and experimental approaches to improve the understanding of human High-throughput methodologies have allowed biological studies to move from a reductionist approach, such as the isolation of specific pathways and mechanisms, to a more integrative approach in which biological systems are seen as a network of interconnected components that provide specific outputs and functions in response to different stimuli.The integration of known cancer genes into protein and signaling networks reveals the characteristics of these cancer genes within the networks. This approach shows that cancer genes often function as network hub proteins, which are involved in many cellular processes and form focal nodes for the exchange of information between many signaling pathways.By providing a systematic and integrative framework for incorporating data and outputting predictions, systems biology has the potential to revolutionize the understanding of the mechanisms underlying cancer formation and to facilitate novel applications for cancer therapeutics.


The ever-increasing capacity of biological molecular data acquisition outpaces our ability to understand the meaningful relationships between molecules in a cell. Multiple databases were developed to store and organize these molecular data. However, emerging fundamental questions about concerted functions of these molecules in hierarchical cellular networks are poorly addressed. Here we review recent advances in the development of publically available databases that help us analyze the signal integration and processing by multilayered networks that specify biological responses in model organisms and human cells Eukaryotic cells respond to a myriad of external and internal cues via a multilayered signaling network. At the top layer of this network, there are plasma membrane receptors which sense changes in the surrounding environment and play important roles in the communication between cells and tissues. Upon activation, these receptors trigger multiple interweaved signaling pathways which operate via protein-protein interactions (PPI) and posttranslational protein modifications (PTMs), such as phosphorylation and ubiquitination, to generate specific biological responses. Many of these responses include changes in gene transcription, which are controlled throughthe modulation of transcription factor (TF) activities. Activated TFs instigate chromatin remodeling and regulate the production of messenger RNAs (mRNAs), which contain the protein coding regions of the genes. Subsequently, mRNAs are translated into protein molecules. The production, degradation, and translation of mRNAs is delicately regulated by a network of non-coding RNAs, which include micro RNAs (miRNAs) and small inhibitory RNAs (siRNAs). This hierarchical structure is intertwined by a plethora of crosstalks, feedback, and feedforward loops connecting signaling PPI and PTM with transcriptional and translational regulation E. coli, C. elegans, Drosophila, and others. For example, IntAct, STRING, HPRD, BioGRID, WI8, DroID, YEASTRACT, and SGD With recent, rapid advances in modern -omics techniques, our ability to acquire vast amounts of biological data increasingly exceeds our ability to interpret these data. However, the main advances were made in the identification and mapping of the components of signal transduction networks, and these repositories have not translated into understanding how interactions between the components generate network functions and specific outputs. It is still poorly understood how signals are processed and converted into physiological or pathological responses. The prolific output of the -omics technologies has been matched by an ever-increasing number of databases that organize data on biological molecules and their interactions in human cells and in model organisms, such as yeast, To understand the coordinate action of different types of interactions that form multilayered signaling networks, we need to systematically integrate heterogeneous interaction data from the literature and specialized databases. Pioneering efforts have brought us the KEGG http://cpdb.molgen.mpg.de/). In addition, ConsensusPathDB contains drug target interactions . The layout of the network is designed to group molecules together based on their subcellular localizations. These interactions can be downloaded in different formats, e.g., XML and GINML, for further analysis. The XML format enables the user to import downloaded data into Cytoscape One of the first databases that integrated transcriptional and posttranscriptional (mRNA-microRNA) interactions with other types of biochemical interactions was TranscriptomeBrowser D. melanogaster, and C. elegans. For this purpose, it implements a multilayered database architecture , it offers a search tool, which allows users to retrieve interactions involving a gene or protein of interest. The retrieved interactions are organized according to their signaling layers and are visualized as a network in the same page. In the download page , users can retrieve entire pathways and the crosstalk mechanisms between these pathways. To discover multilayered crosstalk between two signaling pathways, the user selects two pathways, an organism, and the signaling layers of interest, and the database retrieves the relevant interactions. Information regarding two additional pathways  where two separate user interfaces, customized for these pathways, are provided to facilitate data retrieval. On the same page , SignaLink2 also provides two additional tools, PathwayLinker and SignaLog. PathwayLinker retrieves the first neighbor interaction network of the queried proteins and visualizes the pathways that involve the proteins in the retrieved network. SignaLog predicts novel pathway components based on orthologue information.Despite the complex and multilayered architecture of its underlying database, SignaLink2 provides a simple and intuitively clear user interface to search and retrieve information. On the main page , PSI-MI (tab or xml), Cytoscape, and SBML. The data can also be exported to Boolean pathway simulators, such as CellNetOptimizer One of the main objectives behind integrating heterogeneous interaction data is to understand the mechanistic details of how different pathways modulate each other's activities via PPI, PTM, and transcriptional crosstalk In addition to the elimination of drugs from cells by export pumps, mechanisms emerging from network design features, such as robustness and adaptation, are now drifting into the limelight. The exact contribution of network-based mechanisms is unknown, but may be substantial given that the network negative feedback and crosstalk motifs, which can cause drug resistance, are common The above example of drug discovery is just one of many applications where truly integrated databases could be useful. While there are many more biological and biomedical questions which would greatly benefit, two grand challenges stand out. One is the functional interpretation of genetic and genomic alterations. Next-generation sequencing is now cheap and powerful enough to make the sequencing of human genomes a clinical routine test At the moment we are lacking systematic approaches to each of these grand challenges. Integrated databases will be a cornerstone of developing them. How can we achieve this goal? We will need not only more integration between more things, but primarily we will need more efficient integration. Instead of just linking data we will need to design semantics that, like in a language, instill meaning into a string of linked facts or words. Semantic web tools are finding their way into biology and hold great promise for accomplishing data linking As fully integrated databases have only started to be built, time will show how these databases will change the research and computational modeling landscape. To facilitate computational modeling, integrated databases need to provide dynamic linkage to specialized databases that store quantitative kinetic data on the time course of phosphorylation or other protein modifications for multiple different sites of signaling proteins and enzymes. Then, using semantic and other links between databases, mathematical models can be properly calibrated, and predictive computer simulations would allow us to find the routes and relative intensities of signal flows following a variety of external cues processed by cell surface receptors. This will help us understand cellular responses and phenotypic behavior. A largely understudied problem is the combinatorial complexity of signaling by multi-domain proteins and protein complexes Overall, integrated databases such as ConsensusPathDB, InteractomeBrowser, and SignaLink2 are noteworthy initiatives in reconstructing a global multilayered picture of cellular signaling systems by integrating heterogeneous interaction data from multiple sources. However, integrated databases have a long way to come from their current state, before we can effectively use them to develop a quantitative, mechanistic understanding of multilayered cellular networks at realistic complexity. In particular, integrated databases should include more of already available information. For example, none of the databases named above integrate epigenetic regulations (such as modulation of gene regulation via chromatin remodeling) and mutation data, although this information is increasingly available from such sources as the ENCODE


Structural biomolecular data are commonly stored in the PDB format. The PDB format is widely supported by software vendors because of its simplicity and readability. However, the PDB format cannot fully address many informatics challenges related to the growing amount of structural data. To overcome the limitations of the PDB format, a new textual format mmCIF was released in June 1997 in its version 1.0. mmCIF provides extra information which has the advantage of being in a computer readable form. However, this advantage becomes a disadvantage if a human must read and understand the stored data. While software tools exist to help to prepare mmCIF files, the number of available systems simplifying the comprehension and interpretation of the mmCIF files is limited.In this paper we present mmView - a cross-platform web-based application that allows to explore comfortably the structural data of biomacromolecules stored in the mmCIF format. The mmCIF categories can be easily browsed in a tree-like structure, and the corresponding data are presented in a well arranged tabular form. The application also allows to display and investigate biomolecular structures via an integrated Java application Jmol.http://sourceforge.net/projects/mmview) that can be installed on the user's computer, and as a publicly available web server.The mmView software system is primarily intended for educational purposes, but it can also serve as a useful research tool. The mmView application is offered in two flavors: as an open-source stand-alone application (available from  The Protein Data Bank (PDB)  is a pubThe PDB format  is stillTo overcome the limitations of the PDB format, a new textual format mmCIF was released in June 1997 ,6. mmCIFname-value pairs (a data item) defined by STAR, where the data name is preceded by a leading underscore (_). The name matches the entry in the mmCIF dictionary in which the characteristics of that data item are explicitly defined. Where multiple values for the same data item exist, the name of the data item is declared in a header and the associated values follow in rotation defined by a loop_ construct . The basic principles of the mmCIF syntax are illustrated in the trimmed representation of atomic coordinates  where category defines a natural grouping of data items contained within a single loop_. The values corresponding to the names from the header are given in individual lines . Thus, e.g. the value of the isotropic atomic displacement parameter (given by _atom_site.B_iso_or_equiv name) is the 15th value in each of the lines  and comThe importance of the mmCIF format is demonstrated by the fact, that it represents the data standard upon which the PDB is built . PDB useThe mmView application is implemented as a dynamic web system using open source scripting language Python ,18. Pythhttp://sourceforge.net/projects/mmview and installed locally on user's computer, and as the ready-to-use online version available at http://ich.vscht.cz/~cechp/mmcif. To simplify the installation of the standalone version, the SQLite3 database (version 3.6.10) [The mmView application is available in two flavors: as a standalone application that can be downloaded from  3.6.10) , which d 3.6.10) . However 3.6.10)  library.The View layer Figure  controlsThe workflow of the mmView application begins by typing a PDB code to the form field placed at the top of the mmView application Figure . mmView All information about structure is presented in two different ways. The leftside menu is divided in two parts, where the first part , non-polymer (e.g. ions), and water. Entities consist of chemical components - all monomers  found in the structure. Entities are summarized in the Entities topic, and components are displayed in the Chemical components topic ._entity_poly.type item  entity types are labeled as protein, DNA, RNA or polysacharide. For all other entities  code and description is displayed. The image of the chemical structure will appear on roll over the non-polymer entity code.The Entities topic gives general overview about all entities found in the structure. If the entity is of the polymer type, short description and sequence using one letter codes (codes closed in brackets correspond to non-standard residues) is displayed. Instead of original mmCIF terminology used in The Chemical components topic Figure  displaysExperimental details and conditions are summarized in the Experiment topic. The provided information depends on the type of experimental method. Two most common experimental approaches for biomolecular structure determination are X-ray crystallography, and nuclear magnetic resonance (NMR).For X-ray diffraction experiments the following attributes, if given in mmCIF, are displayed Figure :• residual factors R for all reflections that satisfy the given resolution limits• high and low resolution of interplanar spacings for the reflection data used in the refinement• number of all and observed reflections• ambient temperature• source of diffraction rays• crystal grow details• the pH at which the crystal was grown• the pH of the solution• software used for refinementFor NMR experiments the following attributes, if given in mmCIF, are displayed:• strength of the magnetic field• the pH at which the NMR data were collected• number of models• software used in structure modellingThe last topic of Aggregated view offers complex survey of the structure in 3D form Figure  utilizin• show/hide unit cell• show/hide axes• show/hide wireframe model of the structure• change size of the spacefill of atoms• change the background color• view the structure in a cartoon representation• explore the solvent accesible and Van der Waals surfaces• rotate the structure• zoom the structureThe expandable Category view Figure  lists almmView can display only the information that is available in the corresponding mmCIF file. However, if the data in the mmCIF file are not correct or the file contains errors, these are shown as they are. The number of requests that can be made on the PubMed server is currently limited to three requests every second (state of 19. 7. 2010). Thus, depending on the number of publications, generating the Citations topic can be slow. The dynamically generated leftside menu contains only categories present in the given mmCIF file. However, parsing large mmCIF files is a slow procedure, and thus it can produce additional delays in displaying leftside menu, especially for large structures. Another drawback of the mmView application is the fact, that at present it does not allow to investigate custom mmCIF files. Only structures from the PDB database can be studied, and their PDB IDs must be known in advance utilizing e.g. the search capability of the PDB web site.Future development directly relates to the above mentioned disadvantages of the mmView application. The delays can be avoided by deploying the relational database for storing critical data such as publication abstracts or lists of categories and topics of individual structures. The incorporation of simple form allowing to search PDB database directly from mmView, as well as the possibility to upload custom users' mmCIF files, are also planned for the next version of the application.The mmView application provides a simple but powerful tool for researchers in the fields of tructural biology, and structural bioinformatics. mmView is well suited as an educational tool , but can also serve as a research tool for exploring the details about biomolecular structures. Its online version does not require any installation and provides an intuitive and easy-to-use interface. In addition, the version that can be installed localy on the end-user's computer is also available.Project name: mmView• Project home page: http://ich.vscht.cz/~cechp/mmcif(ready-to-use online version), http://sourceforge.net/projects/mmview• Operating system: Platform independent, any modern web browser needed• Programming language: Python• Other requirements: Python 2.5.4, Django 1.0, NumPy 1.2.1, PymmLib 1.0, JRE 1.6• License: GNU General Public License (Version 2)• Any restrictions to use by non-academics: no restrictions• The authors declare that they have no competing interests.PČ designed and implemented the software and drafted the manuscript. DS instigated the study, participated in its design and coordination, and helped to draft the manuscript. Both authors read and approved the final manuscript.


Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression) with independent and highly curated structural interaction data (e.g protein interaction networks) or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study.We present an algorithm called DART (Denoising Algorithm based on Relevance network Topology) which filters out noise before estimating pathway activity. Using simulated and real multidimensional cancer genomic data and by comparing DART to other algorithms which do not assess the relevance of the prior pathway information, we here demonstrate that substantial improvement in pathway activity predictions can be made if prior pathway information is denoised before predictions are made. We also show that genes encoding hubs in expression correlation networks represent more reliable markers of pathway activity. Using the Netpath resource of signalling pathways in the context of breast cancer gene expression data we further demonstrate that DART leads to more robust inferences about pathway activity correlations. Finally, we show that DART identifies a hypothesized association between oestrogen signalling and mammographic density in ER+ breast cancer.Evaluating the consistency of prior information of pathway databases in molecular tumour profiles may substantially improve the subsequent inference of pathway activity in clinical tumour specimens. This de-noising strategy should be incorporated in approaches which attempt to infer pathway activity from prior pathway models. A key goal in cancer genomics is to map out the activation levels of cancer-relevant pathways across clinical tumour specimens . Obtainide-novo in relation to a phenotype of interest )The DART algorithm and strategy advocated here substantially improves unsupervised predictions of pathway activity that are based on a prior model which was learned from a different biological system or context. It will be fruitful to apply DART and further extensions of it in the context of multidimensional cancer ge-nomic data, where reliable and robust molecular pathway correlates of (epi)genomic abnormalities, clinical and imaging traits are urgently needed.ER+: (ER positive breast cancer); ER-: (ER negative breast cancer)The authors declare that they have no competing interests.YJ performed most of the statistical analyses and contributed to the writing of the manuscript. AET conceived the study, performed some of the analyses and wrote the manuscript. KL and AG helped with bioinformatics and data processing. GP and AJ obtained, analysed and scored the mammogram data. AP is the principal investigator at KCL for the Molecular Taxonomy of Breast Cancer International Consortium programme , which funded the gene array analysis. TN designed the original concept of correlating and integrating imaging with genomic analyses, obtained funding and contributed to final writing of manuscript. All authors read and approved the final manuscript.DART modules for Netpath pathways in ER+ breast cancer. Tables listing the pruned, consistent, maximally connected networks inferred from the application of DART to the transcriptional up and downregulated modules from Netpath in the ER+ subset of the Wang data set.Click here for fileDART modules for Netpath pathways in ER- breast cancer. Tables listing the pruned, consistent, maximally connected networks inferred from the application of DART to the transcriptional up and downregulated modules from Netpath in the ER- subset of the Wang data set.Click here for fileHub genes in DART modules in ER+ breast cancer. Genes in pruned Netpath pathway networks ranked according to their degree in the network.Click here for fileHub genes in DART modules in ER- breast cancer. Genes in pruned Netpath pathway networks ranked according to their degree in the network.Click here for fileDART ESR1 module in breast cancer. Boxplots comparing predicted pathway activities of the Doane ESR1 signature in ER+ versus ER- tumours in the six different breast cancer cohorts. P-values from a t-test are given.Click here for fileComparison of module genes between CORG and DART. Table comparing the number of genes in the CORG and DART predictors of pathway activity.Click here for fileGSEA table for DART ERBB2 module genes. Gene Set Enrichment Analysis table for the ERBB2 DART gene modules (relevance subnetworks). P-values are from a one-tailed hypergeometric test using the genes on the Affy array (Wang data set) as the null background.Click here for fileGSEA table for DART MYC module genes. Gene Set Enrichment Analysis table for the MYC DART gene modules (relevance subnetworks). P-values are from a one-tailed hypergeometric test using the genes on the Affy array (Wang data set) as the null background.Click here for fileGSEA table for DART TP53 module genes. Gene Set Enrichment Analysis table for the MYC DART gene modules (relevance subnetworks). P-values are from a one-tailed hypergeometric test using the genes on the Affy array (Wang data set) as the null background.Click here for file


In this letter, we present the concept of translational computational web robots that aims to improve the clinical knowledge utilization and microbiology test interpretation. This development promise to accelerate discovery by searching and will be challenging the position of observation and experimentation as the most productive means of discovery.With a shift in the 'wet lab' bottleneck from data generation to data analysis, there has been a reinvigoration of the rusty partnership between biomedical and computer scientists. One of the most appealing targets for collaboration has been whole genome analysis, especially the linkage of new sequencing data with the wealth of unstructured information captured in biomedical literature. This endeavour holds particular promise for translational medical microbiology, where a potentially unlimited number of sequenced microbial genomes might be coupled with the breadth of infectious diseases literature. However, generation of meaningful associations between literature and sequences demands novel analytical approaches. We argue that web robots, initially called 'intelligent web crawlers', could be invaluable to microbiologists for such analyses. Many business intelligence applications are already exploiting web robots or autonomous programs that methodically traverse the Internet by following hypertext links and then analyse the results to gain knowledge that enables fulfilment of new tasks. Today, microbiologists often spend more time looking at nucleic acid sequences than at colonies on Petri dishes. Therefore, it seems appropriate to consider robots that could combine the analysis of publicly available genomes of pathogens with the published body of knowledge wide analysis of infectious diseases. Figure http://bioinformatics.leeds.ac.uk/shark/[http://galaxy.psu.edu or CLoVR http://clover.igs.umaryland.edu. Locating online resources which are the most suitable for a specific analytic task in hand applied to a set of individual genes or whole genomes has become a challenge. Web robots with their ability for online service discovery are likely to markedly increase the effectiveness of analytic processes.While there has indeed been rapid growth in web portals and specialized datasets dedicated to the storage and molecular analysis of individual pathogens or microbial genera, we still lack comprehensive web-accessible resources that organise, analyse and predict host-pathogen interactions. Large databases with genome annotation and visualisation tools such as the European Bioinformatics Institute (EBI), the Integrated Microbial Genomes (IMG) system and Comprehensive Microbial Resource (CMR) have been developed to allow microbial genomes to be compared and examined from an evolutionary point of view -3. Otheruk/shark/. The funuk/shark/. Such 'hA true systems biology approach to interpreting genome-scale data requires the analysis of interactions among the genes, not just the genes in isolation . The intWhile there are several well-studied genes that have hundreds of indexed citations in the literature, that degree of functional annotation falls off steeply: almost 80% of Entrez Gene entries had five or fewer linked references in PubMed and almost 50% had zero linked references . The proUnstructured knowledge indexed by the National Library of Medicine and made available through PubMed has an annual growth rate of more than 4.8%. Structured knowledge in the form of molecular biology databases has also increased significantly. An annual compendium of molecular biology databases in 2008 contained 1, 078 databases. Platforms like caBIG , i2B2 (integrating biology and bedside) and GenGIS have taken on the challenge of the integration of different data types from genomes and associated phenomes in order to allow the study of whole genome associations and the identification of 'hot-spots' for new drugs .The capacity of web robots to generate new associational knowledge makes them far more powerful than currently used workflow engines, which intend to automate data analysis tasks -14. TranThe continuing strong growth (with a current rate of about 650 genomes per year) in genomic data is enabling new high-resolution surveillance of organisms as well as assisting the study of 'big' unanswered evolutionary questions. Coupled with the repeated sampling of pathogen populations, these approaches enable the assessment of genome phylodynamics, evolutionary scales and the richness of microbial communities ,16. GenoThe complexity of analysis of pathogen genomes has increased markedly, but there has been an even greater increase in opportunities for an immensely deeper understanding of the causes of disease and of better targeting interventions . Howeverhttp://www.roboearth.org, aiming to build a parallel World Wide Web for robots where they can share the information and learn from each other, are paving a new road for data analysis. The concept of web robots allows us to address the growing gap between the exponential growth of publicly available data and our capacity to leverage it. Robotic assistants can improve the knowledge utilization, accelerate discovery by searching and challenge the position of observation and experimentation as the most productive means of discovery. Computational data integration and analysis still has a long journey ahead of it, but the light is beginning to shine brightly on its path. We are another step closer to Norbert Wiener's prediction that machines might eventually devalue the brain as emphatically as the first Industrial Revolution did the hand.Science has been lagging behind other industries which have been heavily investing in business intelligence in the last decade, with an 11.2% growth of investment in these technologies since 2006 . Recent


Pathway analysis based on Genome-Wide Association Studies (GWAS) data has become popular as a secondary analysis strategy. Although many pathway analysis tools have been developed for case–control studies, there is no tool that can use all information from raw genotypes in general nuclear families. We developed Pathway-PDT, which uses the framework of Pedigree Disequilibrium Test (PDT) for general family data, to perform pathway analysis based on raw genotypes in family-based GWAS.Simulation results showed that Pathway-PDT is more powerful than the p-value based method, ALIGATOR. Pathway-PDT also can be more powerful than the PLINK set-based test when analyzing general nuclear families with multiple siblings or missing parents. Additionally, Pathway-PDT has a flexible and convenient user interface, which allows users to modify their analysis parameters as well as to apply various types of gene and pathway definitions.https://sourceforge.net/projects/pathway-pdt/.The Pathway-PDT method is implemented in C++ with POSIX threads and is computationally feasible for pathway analysis with large scale family GWAS datasets. The Windows binary along with Makefile and source codes for the Linux are available at  Genome-wide association studies (GWAS) have been successful in identifying single nucleotide polymorphisms (SNPs) associated with complex diseases ,2. AlmosStatistical pathway analysis based on GWAS data has become a popular secondary analysis strategy -6. Combihttp://research.agre.org). Moreover, for late-onset diseases such as Alzheimer disease, parental genotypes are often missing. Statistical power for the PLINK set-based test and the GenGen test can be reduced for analyzing such families.Most of the currently available software packages for pathway analysis are designed for case–control studies or are restricted to use trios (two parents and one affected sib) ,9,10. PLAnother way to perform pathway analysis for general nuclear families, such as families with multiple siblings or missing parents, is to use the p-value based methods. The first step for the analysis is to obtain p-values from existing family-based single-SNP association tests that can accommodate general nuclear family structures such as the Pedigree Disequilibrium Test (PDT)  and FBATHere, we integrated two well-established algorithms, the PDT and the modified gene set enrichment analysis (GSEA)  algorithThe Pathway-PDT algorithm combines the framework of the family-based association test, PDT , and theThere are several steps in the Pathway-PDT algorithm:k kb away from the gene. The parameter k is specified by the user. A commonly used k is 5 kb or 20 kb to account for SNPs in regulatory regions for the gene.(1) Assign SNPs to genes. SNPs are assigned to a gene if they are inside the gene or (2) Calculate PDT statistic for each SNP that has been assigned to a gene.(3) For each gene, select the largest PDT statistic  from the PDT statistics for all SNPs assigned to the gene as a score for the gene.N, where the jth gene, Gj, has a score rj. The N genes are sorted by their gene scores from largest to smallest. For each pathway P, calculate the weighted KS-like running sum statistic (referred to as the Enrichment Score of P or ES(P)) by the following [(4) Let the total number of genes in the dataset be ollowing :w is the weight for each gene and NH is the number of genes in P. The default weight w is 1 for Pathway-PDT as recommended in the GSEA algorithm [where lgorithm .(5) Permute the transmitted and untransmitted alleles from parents to siblings within each family and recalculate the PDT statistics for SNPs within genes.K times.(6) Repeat steps 3–5 for ES(P) is greater than the observed ES(P) in the K times. Based on our simulation results, Pathway-PDT maintained correct type I error rates when K was specified as 2,000. However, a larger number of K is required if a higher precision of p-value is needed. The null hypothesis is that the distribution of gene scores in P is the same as the distribution of gene scores for other genes in the genome.The p-value for the Pathway-PDT test is the proportion of times that the permuted Similar to Monks and Kaplan , it can Both Wang’s method  and PathSimulation studies were conducted to evaluate the type I error rates and to compare power for the Pathway-PDT with the p-value based tool, ALIGATOR, which uses p-values from GWAS and a bootstrap sampling approach to estimate empirical p-values, and the PLINK set-based test, which is a raw-genotype based test. The PLINK set-based test has been shown to be a powerful test for pathway analysis using simulations and real data applications ,16. The A forward-time population simulation program, genomeSIMLA  was usedThe null data simulations showed that type I error rates were controlled for Pathway-PDT at the 0.05 and 0.01 significance levels. Figure Pathway-PDT requires three types of input files: a gene file, which contains the locations of genes, a pathway file with pathway definitions, and the standard PLINK map and ped files. The analysis of pathways is performed in parallel using the POSIX threads (pthreads). The total run time of Pathway-PDT for analyzing 210 KEGG pathways for 1,000 permutations based on a GWAS dataset with 710 families genotyped on the Illumina 1 M chip platform was 42 minutes on 8 Intel ×86-64 processors. Therefore, Pathway-PDT can efficiently perform large-scale pathway analysis in a reasonable time frame.In conclusion, we implemented a family-based pathway algorithm, Pathway-PDT, in an efficient software package. The routine procedures of pathway analysis such as mapping SNPs to genes and mapping genes to pathways, the procedures of calculating the single-SNP and pathway statistics, and calculating the permutation adjusted p-values and FDR are automatically performed in the Pathway-PDT software in a single run. Moreover, threads are used to run the permutations in parallel to increase the efficiency of the tool. With convenient user interface and efficient performance, Pathway-PDT will be very useful for analyzing family-based GWAS datasets.Project name: Pathway-PDTProject home page:https://sourceforge.net/projects/pathway-pdt/Operating system(s): Windows and LinuxProgramming language: C++ with POSIX threads (pthreads)Restrictions on use by non-academics: no limitationsThe authors declare that they have no competing interests.YSP was the primary author on the manuscript. YSP, MS, and RHC developed the Pathway-PDT method and software, and tested the program intensively on simulated datasets. ERM and MPV provided input to study design. All authors contributed to writing of the manuscript. All authors read and approved the final manuscript.


The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at  Systems Biology aims to study the relationships and interactions between various parts of a biological system and to integrate this information in order to understand its functionality. In this process, the visualization and visual analysis of the network data plays an essential role in understanding complex biological processes and mechanisms. Many tools, which strongly differ in the way of presenting the data, have been developed for visually exploring biological networks In addition to the static pathway maps, many tools that dynamically visualize biological networks have been developed in the last decade. Most of these tools have been designed to fulfill the requirements of particular applications.The stand-alone visualizer Cytoscape Another visualization tool that has been designed specifically for the integrative visual data mining of biological pathway is VisANT VANTED A problem with visualizing biological data is the size of the networks, which can be very large, e.g., for protein-protein interaction data. OSPREY Since many users appreciate the classical layouts used in static biological maps like KEGG, some visualization tools try to dynamically generate similar layouts, which in addition have the advantage of being editable. KGML-ED A review A major challenge in visualizing biological networks is that the layouts have to account for additional constraints imposed by biological convention or additional data . The next step is the visualization of high-throughput data  in the context of these networks The aspect of incorporation of external data sources is another important point also addressed by Suderman et al. in Another major challenge is the improvement of the layouts since most tools do not take into account the underlying biology and drawing conventions known from biochemical textbooks but only the structural relations of the mathematical network. For specific applications, it is however necessary to extend and customize the general layouts to the special needs of the application. Most visualization tools focus on very specific aspects and cannot easily be extended.Thus, sophisticated layouts for biological networks in general, but also tailored for special applications, need to be developed and combined with existing drawing concepts. Furthermore, the integration of future tasks and applications should be easy. The key challenges in the area of visual analytics of biological networks remain, thus, to combine automated analysis with advanced visualization techniques, and to make the navigation and exploration of huge data sets interactive.A prior version of the software we present, has been published along with the BN++ data warehouse BiNA is a visual analytics tool for the interactive visualization, exploration, and analysis of biological network data. It uses advanced graph drawing techniques for visualizing the network data. The integrated connection to an R server supports custom pre-analysis of high-throughput (omics) data, which can then be projected onto these networks and visualized and analyzed in a number of ways.The core of BiNA is a sophisticated visualization concept, combining high-quality graphics, dynamic network visualizations, and very flexible data projections. BiNA provides different network representations and visual styles.In Similar to other tools, BiNA is able to project high-throughput data from various sources to the network. For this, we implemented a generic projection framework, supporting a large number of projection targets. These targets range from simple graph attributes, e.g., color, node size, line thickness, and visibility to complex ones, like sub-cellular compartment association or time series. Time series projections, for example, are realized using small charts, which are drawn instead of node shapes.The user can load omics data from various file formats, via drag and drop from a spreadsheet program, or online from the gene omnibus database (GEO) The imported data can be modified directly in BiNA. It is possible to transform the data based on simple mathematical expression. Access to a number of mathematical functions and operators is provided through an intuitive syntax. User defined functions can be mapped to R expressions normalize_vsn takes a matrix as an argument and passes it on to the normalizeVSN function of the vsn package of R GSM73386 normalized. This new dataset can then be stored or mapped onto the network data in BiNA.The mathematical expression editor  can thenFor a more complete overview of BiNA's features and a comparison to other tools, we refer to BiNA has been used in several application scenarios. For example, we are offering a web service called NetworkTrail Other applications where we successfully used BiNA for visually analyzing the algorithmic results are miRtrail In BiNA itself is a modular software tool built upon OSGi http://bina.unipax.info/development and contains JavaDocs and a comprehensive example plugin, which addresses different use cases.Each core bundle provides a API, which can be used by software developers to extend the functionality of BiNA. These bundles usually correspond to certain application scenarios, e.g., for using BiNA Webstart in an online workflow for visualizing results in a customized way. The developer documentation is available at The underlying OSGi 4.3 platform provides dynamic loading and unloading of bundles during runtime, automatic handling of native libraries, and a full-featured component/bundle system, which becomes more and more necessary in large software projects. The main application window (GUI) of BiNA is divided into several dynamic views using the Docking Frames window management library BiNA distinguishes between views and editors. While a view exists only once, editors can have multiple instances, e.g., for different visualizations.The core of the visualization is a sophisticated visualization model. From a set of universal base components, different visualization styles are realized, e.g., for metabolic and regulatory networks. The latter ones include gene regulatory, signaling, and protein-protein interaction networks.Base components of the visualization model like nodes, edges, groups, hyperedges, and stars combine the simplicity of graphs with the readability of hierarchically structured elements. During the visualization process these components will be translated into a hierarchic graph using the graph library interface of BiNA. An implementation of this interface is provided by the separate yFiles graph library Groups of the visualization model are realized using hierarchic nodes and can be opened and collapsed again using mouse gestures. They can contain their own graph layout, which supports multiple layouts in one visualization view and can be used for, e.g., laying out two biological pathways in a different way nearby.Every component has an additional graphical abstraction layer describing its visual appearance and available user interactions. This layer can be exchanged by a different visual style, for example, the KEGG visual style for the metabolic representation.Since groups can be collapsed and single components can be made invisible, single changes to the model can result in a large number of changes to the resulting graph. We meet this, by only updating those parts of the graph, which have been marked as modified by the changes to the model. We further distinguish between topological, visual, and layout changes to avoid unnecessary calculations, which heavily accelerate the rendering process in many cases.This is also the reason why we subdivide the rendering process into three steps: (1) the construction of the graph topology, (2) assignment of graphical attributes  to each node and edge, and (3) laying out the graphical elements on the screen.Data projection (see section below) can influence each of these steps. For example, the projection of omics data to node visibility can filter some nodes in the first step. The subdivision into these steps now improves the rendering speed, since not all nodes and edges have to be processed by step two and three, which becomes important for large networks. Every component of the visualization model has a set of properties, e.g., label text, shape, size, text font, color, line width. These properties have default values that can be changed globally with a predefined visual style. These properties can, however, be overridden by the user at any time. The resulting graphical representation can thus be customized in any way the user desires.The network visualizations can be saved using the BiNA specific file format (BML). The saved visualization contains also all data sets projected to the visualization and the visualization-wide properties.As discussed above, BiNA is able to directly access biological networks from a BNDB Analysis Project Explorer view for processing and projection onto network visualization.A generic projection framework supports the mapping of arbitrary external information, primarily omics data sets, onto the network data. As noted before, data projection can influence a large number of visualization attributes, like color, size, stroke, and more complex ones like reaction direction, or subcellular location. The projected data can be imported from suitable files , SOFT), dragged & dropped from text/spreadsheet editors, or downloaded from the GEO database The data itself is organized in a tabular fashion. Data columns can be of different types. Rows correspond to individual components of the network  and are identified by a single identifier or a set of identifiers . Columns can be grouped in order to express sample semantics . Identifiers will be mapped to the internal identifiers of the network nodes. If necessary, external identifier mapping services (UniProt) are employed to translate between different identifier systems.BiNA will use the selected sample of the series for data projection. If multiple samples are selected, BiNA will automatically try to compute a temporary sample consisting of their mean values, which will then be used for projection. It is also possible to enter a custom formula using an intuitive syntax to calculate the temporary sample. The user can choose from a large set of built-in functions or provide new user-defined functions, which will be passed on to and evaluated by R.R Console view is available for the user, which grants full callback access to the underlying R session.In the latter case, BiNA must be connected via Java Native Interface (JNI) to an R installation or via network to a server running the Rserve package. The connection to R also enables loading of arbitrary matrix data from R into a series in BiNA and vice versa, such that existing workflows in R can be reused. Derived samples can be exported again together with the series, either in the BiNA data format (BDF) or in comma/tab separated values format (CSV). If BiNA is connected to R using JNI, the Since, the network visualization of BiNA uses the concept of hierarchic graphs, it is not reasonable to apply standard graph algorithms on the resulting visualization graph, for example, a shortest paths search. Especially, the groupings of protein families, protein complexes, biochemical reactions, and pathways, and the multiple representation of entities used for visualization aspects, can conflict with graph theoretic interpretations. To meet this problem, the different network representations of BiNA provide a configurable way of flattening the visualization graph. For example, the hierarchical modeling of protein families can be flattened by splitting the node representing the family into its individual members. This feature can be used by developers to access the underlying biological network of BiNA. It is also used for exporting visualizations into simple graph formats .k shortest paths and a breadth-first search. The resulting paths and distances can be highlighted in the visualization using node and edge colorings.BiNA provides also a way to project the results, calculated on the simple graph, back to the visualization. We provide two example algorithms, a The default visual style of the metabolic network representation provides drawings known from biochemical textbooks. For this, reaction arrows are drawn using quadratic curves and instead of node shapes, the chemical structure of metabolites can be shown by painting MOL files from the KEGG database. The resulting network is a bi-partite graph, containing reaction nodes (enzymes) and metabolite nodes. As noted before, reactions are realized using groups containing the co-factors of a reaction. Since the reaction groups do not have their own layout style, co-factors and their edges are integrated into the parental layout, if the group is opened.The KEGG visual style is built upon the default one. It provides different properties for the visualization elements, and uses a special KEGG layout algorithm for pathways to imitate the KEGG maps. The layout information is provided in parts by the KGML files of KEGG. For example, a KGML file contains information about node positions, but lacks information about edge routes, thus, they must be computed by the layouter.The metabolic representation provides functionality for improving the readability and clarity of the visualization, like context-specific hiding of co-factors and aliasing of compounds and reactions. Several strategies for selecting co-factors are provided to the user, e.g., by information from the source data, editable pre-defined lists, heuristics, or manually. Furthermore, the integration of context-specific pathway validation strategies . These can be organized in complexes and families, which are realized using groups. Similar to the metabolic representation, the regulatory one supports aliasing of entities. This improves the readability, if multiple pathways, which are also realized as groups, contain the same entity. Different instances of an entity can be highlighted and it is possible to merge instances again. Another feature is the exploration of the underlying data source. For this, the user can explore the neighborhood of an entity, either upstream, downstream, or both, by adding these regulative interactions to the visualization.yFiles bundle for BiNA, which implements the graph library developer interface of the Network Analysis bundle. Using this interface, BiNA is able to access yFiles features indirectly, with respect to the 3-clause BSD. This bundle also provides a number of layout algorithms given by the yFiles library. Most notable for our purposes are the yFiles hierarchic layouter, the organic (forced-directed) layouter, and the orthogonal layouter. These layout algorithms have been extended to our specific needs, supporting the layout criteria of the metabolic and regulatory network representations, and their visual styles.For rendering and layout algorithms, BiNA uses the yFiles for Java graph library. We provide the In the metabolic representation, for example, the organic layouter is extended to support the readability by laying out dangling co-factors according to the reaction flow. For the regulatory representation, which supports changing the visualization background to visualize subcellular locations, we extended the hierarchic layouter of the yFiles graph library to support layer assignments in an easy way.BiNA, the Biological Network Analyzer, is a mature tool for the integrative analysis of omics data in a network context. In contrast to other tools, it permits not only the direct import of networks and omics data from flat files, but also features a direct connection to an integrated data warehouse. By supporting common file formats for transcriptomics, metabolomics, and proteomics as well as arbitrary tabular data, nearly any data set of interest can be projected onto the networks and explored interactively. A generic projection system supports users in creating visually concise and information-rich visualizations that can be directly exported in publication quality. The interface to the statistical programming language R furthermore enables a wealth of existing statistical methods. These can be easily applied to the datasets loaded in BiNA and results of the statistical analysis can be easily projected back onto the network visualization.The integration of advanced graph drawing techniques permits a pleasing graphical representation of the networks. Inclusion of well-established layouts  provides easily recognizable pathways while adding the benefits of interactive navigation and simultaneous display of multiple pathways. Where these layouts do not suffice, the built-in editing capabilities permit extension, correction, and curation of the representation as well as the underlying data.BiNA's standardized plugin interface permits developers to add custom extensions whenever needed. We expect that additional plugins will extend the functionality soon.http://bina.unipax.info/ and Sourceforge at http://sf.net/p/bina/. Since BiNA is written in Java, it runs on every platform supported by Oracle's JRE 7 or OpenJDK 7. We provide prepackaged versions for Windows, Mac OS X, and Linux containing an executable for starting BiNA. In addition, we provide a Java Webstart version at http://webstart.bina.unipax.info/2.4.1/.BiNA (version 2.4.1) is available free of charge under an open-source license (3-clause BSD license) with exception of the yFiles bundle, which is free but not open-source. Source code, documentation, and installable packages are available from the project web site at Figure S1Rendering time of protein-protein interaction networks of different sizes in BiNA. BiNA allows loading of networks having more than 100,000 edges using a standard desktop PC . In the Figure we give some testing results for rendering Protein-Protein Interaction networks from a SIF file containing 50 to 100,000 edges (interactions) using the organic layouter. The overall rendering time for 10,000 interactions took about 4.2 seconds and for 100,000 interactions about 53 seconds. Note that depending on the network size, we dynamically change the layout settings, to improve its quality in smaller (human readable) networks.(DOCX)Click here for additional data file.Table S1Comparison of major functionalities between BiNA, Cytoscape, VANTED, and CellDesigner. The table shows a comparison of major functionalities between BiNA, Cytoscape, VANTED, and CellDesigner. Since all tools are extendable by plug-ins, we compare the distributed versions and mark features fulfilled by an additional plugin with an asterisk. Major differences of the tools can be found in the data access methods, where the functionality of BiNA is heavily affected by the presence of the BN++ data warehouse, which provides access to a large number of integrated databases. Another essential difference is BiNA's network visualization model, which is based on hierarchical graphs and focuses comprehensive, dynamic, and interactive visualizations for metabolic, regulatory, and signaling networks of high quality. Although, CellDesigner has also a hierarchical visualization model supporting hierarchical groupings, it is more focused on metabolic network simulation and lacks explicit functionality for regulatory and signaling network visualization.(DOCX)Click here for additional data file.