The Importance of Modularity in Bioinformatics Tools

In the last decade the amount of Bioinformatics tools has increased enormously. There are tools to store, analyse, visualize, edit or generate biological data and there are still more in development. Still, the demand for increased functionality in a single piece of software must be balanced by the need for modularity to keep the software maintainable. In complex systems, the conflicting demands of features and maintainability are often solved by plug-in systems.

For example Cytoscape, an open source platform for Complex-Network Analysis and Visualization, is using a plug-in system to allow the extension of the application without changing the core. This not only allows the integration of new functionality without a new release but offers the possibility for other developers to contribute plug-ins which are needed in their research.

Most tools have their own, individual plug-in system to meet the needs of the application. These are often very simple and easy to use. However, the increasing complexity of plug-ins demands more functionality of the plug-in system. We want to reuse components in different contexts, we want to have simple plug-in interfaces and we want to allow communication and dependencies between plug-ins. Many tools implemented in Java are facing these problems and there seems to be a common solution: the integration of an established modularity framework, like OSGi. To our knowledge, a number of developers of bioinformatics tools are already implementing, planning or thinking about the integration of OSGi into their applications, e.g. Cytoscape, Protege, PathVisio, ImageJ, Jalview or Chipster. The adoption of modularity frameworks in the development of bioinformatics applications is steadily increasing and should be considered in the design of new software.

By modularity in the traditional computer science sense, we mean the division of a software application into logical parts with separate concerns. To ease the development of software tools the application is separated into smaller logical parts, which are implemented individually. A set of modules can form a larger application but only if a proper glue is used, OSGi is an example of such a glue. OSGi allows to build an infrastructure into an application to add and use different modules. It provides mechanisms to allow the individual modules to rely on and interact with each other, opening the possibility to put together different modules to solve the problem at hand. Later, modules can be removed and new ones can be added to tackle another problem. As Katy Boerner in her article 'Plug-and-Play Macroscopes' writes, we should 'implement software frameworks that empower domain scientists to assemble their own continuously evolving macroscopes, adding and upgrading existing (and removing obsolete) plug-ins to arrive at a set that is truly relevant for their work'.

Some of these modules are going to be specific for one application but a lot of these modules can actually be reused by other tools. We are talking about general features like the import or export of different file formats, a layout algorithm that could be used by several visualization tools or the lookup in an external online database. Why should every tool implement its own parser or algorithm? Modularity can help to share functionality. There is no need to start from scratch and implement everything anew, thus developers can focus on new and important features.

Adding modularity, or better, a modularity framework to an existing software application is not a trivial task. The developers of Cytoscape are currently undertaking this challenge with the coming version 3. We are also working on the integration of OSGi into our pathway visualization tool PathVisio and we now want to share and compare our experiences, so others can benefit from our discoveries. This will not only help them in making a decision if OSGi is a suitable solution for them but also in the integration process itself

WikiPathways: building research communities on biological pathways.

Here, we describe the development of WikiPathways (http://www.wikipathways.org), a public wiki for pathway curation, since it was first published in 2008. New features are discussed, as well as developments in the community of contributors. New features include a zoomable pathway viewer, support for pathway ontology annotations, the ability to mark pathways as private for a limited time and the availability of stable hyperlinks to pathways and the elements therein. WikiPathways content is freely available in a variety of formats such as the BioPAX standard, and the content is increasingly adopted by external databases and tools, including Wikipedia. A recent development is the use of WikiPathways as a staging ground for centrally curated databases such as Reactome. WikiPathways is seeing steady growth in the number of users, page views and edits for each pathway. To assess whether the community curation experiment can be considered successful, here we analyze the relation between use and contribution, which gives results in line with other wiki projects. The novel use of pathway pages as supplementary material to publications, as well as the addition of tailored content for research domains, is expected to stimulate growth further

BridgeDb: standardized access to gene, protein and metabolite identifier mapping services

Many interesting problems in bioinformatics require integration of data from various sources. For example when combining microarray data with a pathway database, or merging co-citation networks with protein-protein interaction networks. Invariably this leads to an identifier mapping problem, where different datasets are annotated with identifiers that are related, but originate from different databases.

Solutions for the identifier mapping problem exist, such as Biomart, Synergizer, Cronos, PICR, HMS and many more. This creates an opportunity for bioinformatics tool developers. Tools can be made to flexibly support multiple mapping services or mapping services could be combined to get broader coverage. This approach requires an interface layer between tools and mapping services. BridgeDb provides such an interface layer, in the form of both a Java and REST API.

Because of the standardized interface layer, BridgeDb is not tied to a specific source of mapping information. You can switch easily between flat files, relational databases and several different web services. Mapping services can be combined to support multi-omics experiments or to integrate custom microarray annotations. BridgeDb isn't just yet another mapping service: it tries to build further on existing work, and integrate multiple partial solutions. The framework is intended for customization and adaptation to any identifier mapping service. 

BridgeDb makes it easy to add an important capability to existing tools. BridgeDb has already been integrated into several popular bioinformatics applications, such as Cytoscape, WikiPathways, PathVisio, Vanted and Taverna. To encourage tool developers to start using BridgeDb, we've created code examples, online documentation, and a mailinglist to ask questions. 

We believe that, to meet the challenges that are encountered in bioinformatics today, the software development process should follow a few essential principles: user friendliness, code reuse, modularity and open source. BridgeDb adheres to these principles, and can serve as a useful model for others to follow. BridgeDb can function to increase user-friendliness of graphical applications. It re-uses work from other projects such as BioMart and MIRIAM. BridgeDb consists of several small modules, integrated through a common interface (API). Components of BridgeDb can be left out or replaced, for maximum flexibility. BridgeDb was open source from the very beginning of the project. The philosophy of open source is closely aligned to academic values, of building on top of the work of giants. 

Many interesting problems in bioinformatics require integration of data from various sources. For example when combining microarray data with a pathway database, or merging co-citation networks with protein-protein interaction networks. Invariably this leads to an identifier mapping problem, where different datasets are annotated with identifiers that are related, but originate from different databases.

Solutions for the identifier mapping problem exist, such as Biomart, Synergizer, Cronos, PICR, HMS and many more. This creates an opportunity for bioinformatics tool developers. Tools can be made to flexibly support multiple mapping services or mapping services could be combined to get broader coverage. This approach requires an interface layer between tools and mapping services. BridgeDb provides such an interface layer, in the form of both a Java and REST API.

Because of the standardized interface layer, BridgeDb is not tied to a specific source of mapping information. You can switch easily between flat files, relational databases and several different web services. Mapping services can be combined to support multi-omics experiments or to integrate custom microarray annotations. BridgeDb isn't just yet another mapping service: it tries to build further on existing work, and integrate multiple partial solutions. The framework is intended for customization and adaptation to any identifier mapping service. 

BridgeDb makes it easy to add an important capability to existing tools. BridgeDb has already been integrated into several popular bioinformatics applications, such as Cytoscape, WikiPathways, PathVisio, Vanted and Taverna. To encourage tool developers to start using BridgeDb, we've created code examples, online documentation, and a mailinglist to ask questions. 

We believe that, to meet the challenges that are encountered in bioinformatics today, the software development process should follow a few essential principles: user friendliness, code reuse, modularity and open source. BridgeDb adheres to these principles, and can serve as a useful model for others to follow. BridgeDb can function to increase user-friendliness of graphical applications. It re-uses work from other projects such as BioMart and MIRIAM. BridgeDb consists of several small modules, integrated through a common interface (API). Components of BridgeDb can be left out or replaced, for maximum flexibility. BridgeDb was open source from the very beginning of the project. The philosophy of open source is closely aligned to academic values, of building on top of the work of giants. 

The BridgeDb library is available at "http://www.bridgedb.org":http://www.bridgedb.org.
A paper about BridgeDb was published in BMC _Bioinformatics_, 2010 Jan 4;11(1):5.

BridgeDb blog: "http://www.helixsoft.nl/blog/?tag=bridgedb":http://www.helixsoft.nl/blog/?tag=bridged

BBADIS-16-507-R1 1 Integrative network analysis reveals time-dependent molecular events underlying left ventricular remodeling in post-myocardial infarction patients

International audienceTo elucidate the time-resolved molecular events underlying the LV remodeling (LVR) process, we developed a large-scale network model that integrates the 24 molecular variables (plasma proteins and non-coding RNAs) collected in the REVE-2 study at four time points (baseline, 1month, 3months and 1year) after MI. The REVE-2 network model was built by extending the set of REVE-2 variables with their mechanistic context based on known molecular interactions (1310 nodes and 8639 edges). Changes in the molecular variables between the group of patients with high LVR (>20%) and low LVR (<20%) were used to identify active network modules within the clusters associated with progression of LVR, enabling assessment of time-resolved molecular changes. Although the majority of molecular changes occur at the baseline, two network modules specifically show an increasing number of active molecules throughout the post-MI follow up: one involved in muscle filament sliding, containing the major troponin forms and tropomyosin proteins, and the other associated with extracellular matrix disassembly, including matrix metalloproteinases, tissue inhibitors of metalloproteinases and laminin proteins. For the first time, integrative network analysis of molecular variables collected in REVE-2 patients with known molecular interactions allows insight into time-dependent mechanisms associated with LVR following MI, linking specific processes with LV structure alteration. In addition, the REVE-2 network model provides a shortlist of prioritized putative novel biomarker candidates for detection of LVR after MI event associated with a high risk of heart failure and is a valuable resource for further hypothesis generation

Exploring pathway interactions in insulin resistant mouse liver

<p>Abstract</p> <p>Background</p> <p>Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset.</p> <p>Results</p> <p>We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar.</p> <p>Conclusions</p> <p>Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well.</p

WikiPathways: Pathway Editing for the People

WikiPathways provides a collaborative platform for creating, updating, and sharing pathway diagrams and serves as an example of content curation by the biology community

Time-Resolved and Tissue-Specific Systems Analysis of the Pathogenesis of Insulin Resistance

BACKGROUND: The sequence of events leading to the development of insulin resistance (IR) as well as the underlying pathophysiological mechanisms are incompletely understood. As reductionist approaches have been largely unsuccessful in providing an understanding of the pathogenesis of IR, there is a need for an integrative, time-resolved approach to elucidate the development of the disease. METHODOLOGY/PRINCIPAL FINDINGS: Male ApoE3Leiden transgenic mice exhibiting a humanized lipid metabolism were fed a high-fat diet (HFD) for 0, 1, 6, 9, or 12 weeks. Development of IR was monitored in individual mice over time by performing glucose tolerance tests and measuring specific biomarkers in plasma, and hyperinsulinemic-euglycemic clamp analysis to assess IR in a tissue-specific manner. To elucidate the dynamics and tissue-specificity of metabolic and inflammatory processes key to IR development, a time-resolved systems analysis of gene expression and metabolite levels in liver, white adipose tissue (WAT), and muscle was performed. During HFD feeding, the mice became increasingly obese and showed a gradual increase in glucose intolerance. IR became first manifest in liver (week 6) and then in WAT (week 12), while skeletal muscle remained insulin-sensitive. Microarray analysis showed rapid upregulation of carbohydrate (only liver) and lipid metabolism genes (liver, WAT). Metabolomics revealed significant changes in the ratio of saturated to polyunsaturated fatty acids (liver, WAT, plasma) and in the concentrations of glucose, gluconeogenesis and Krebs cycle metabolites, and branched amino acids (liver). HFD evoked an early hepatic inflammatory response which then gradually declined to near baseline. By contrast, inflammation in WAT increased over time, reaching highest values in week 12. In skeletal muscle, carbohydrate metabolism, lipid metabolism, and inflammation was gradually suppressed with HFD. CONCLUSIONS/SIGNIFICANCE: HFD-induced IR is a time- and tissue-dependent process that starts in liver and proceeds in WAT. IR development is paralleled by tissue-specific gene expression changes, metabolic adjustments, changes in lipid composition, and inflammatory responses in liver and WAT involving p65-NFkB and SOCS3. The alterations in skeletal muscle are largely opposite to those in liver and WAT

Acute cholecystitis in high risk surgical patients: percutaneous cholecystostomy versus laparoscopic cholecystectomy (CHOCOLATE trial): Study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Laparoscopic cholecystectomy in acute calculous cholecystitis in high risk patients can lead to significant morbidity and mortality. Percutaneous cholecystostomy may be an alternative treatment option but the current literature does not provide the surgical community with evidence based advice.</p> <p>Methods/Design</p> <p>The CHOCOLATE trial is a randomised controlled, parallel-group, superiority multicenter trial. High risk patients, defined as APACHE-II score 7-14, with acute calculous cholecystitis will be randomised to laparoscopic cholecystectomy or percutaneous cholecystostomy. During a two year period 284 patients will be enrolled from 30 high volume teaching hospitals. The primary endpoint is a composite endpoint of major complications within three months following randomization and need for re-intervention and mortality during the follow-up period of one year. Secondary endpoints include all other complications, duration of hospital admission, difficulty of procedures and total costs.</p> <p>Discussion</p> <p>The CHOCOLATE trial is designed to provide the surgical community with an evidence based guideline in the treatment of acute calculous cholecystitis in high risk patients.</p> <p>Trial Registration</p> <p>Netherlands Trial Register (NTR): <a href="http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=2666">NTR2666</a></p

Mining Biological Pathways Using WikiPathways Web Services

WikiPathways is a platform for creating, updating, and sharing biological pathways [1]. Pathways can be edited and downloaded using the wiki-style website. Here we present a SOAP web service that provides programmatic access to WikiPathways that is complementary to the website. We describe the functionality that this web service offers and discuss several use cases in detail. Exposing WikiPathways through a web service opens up new ways of utilizing pathway information and assisting the community curation process