RCK Domain Model of Calcium Activation in BK Channels

Potassium ion channels are ubiquitously expressed from bacteria to mammals where they are involved in various processes ranging from the regulation of osmotic pressure in a single cell to the electrical response in muscle fibers to the generation of action potentials in neurons. The B K channel family (BK for Big K+ conductance) is an interesting subfamily of K+ channels responsive to both membrane voltage and intracellular calcium ion. The unique, high-affinity Ca2+ sensitivity of B K channels is critical to their physiological function in various cell types. The mechanism by which Ca2+ activates B K channel gating, however, is not well understood. Here we present a structure-based approach to the study of B K channels with the goal of providing a structural and functional model of the Ca2+-activation mechanism. Sequence analysis of BK channel C-terminal domains and domains from prokaryotic homologs reveals the conservation of unique positions defining a novel regulatory domain associated with K conduction, the R C K domain. Crystal structures of R C K domains from prokaryotic sources relate the conservation of sequence to the structure, assembly and function of these domains. W e propose a hypothetical model for the structure and function of the Cterminal domains of B K as a set of R C K domains that conduct the Ca -activation mechanism. The features and constraints predicted by the R C K domain model are tested by the electrophysiological assay of a variety of human B K constructs. The results support a domain structure and assembly consistent with the proposed model for the B K C-terminus. In addition, the results identify residues and regions involved in Ca + activation: the Ca2+-binding event and the transduction of the binding energy through protein conformational changes to the channel domain. The R C K domain model thus provides a framework for the study of Ca2+ activation in B K channels

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

From Scientific Discovery to Cures: Bright Stars within a Galaxy

We propose that data mining and network analysis utilizing public databases can identify and quantify relationships between scientific discoveries and major advances in medicine (cures). Further development of such approaches could help to increase public understanding and governmental support for life science research and could enhance decision making in the quest for cures

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

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

GenMAPP 2: New features and resources for pathway analysis

BACKGROUND: Microarray technologies have evolved rapidly, enabling biologists to quantify genome-wide levels of gene expression, alternative splicing, and sequence variations for a variety of species. Analyzing and displaying these data present a significant challenge. Pathway-based approaches for analyzing microarray data have proven useful for presenting data and for generating testable hypotheses. RESULTS: To address the growing needs of the microarray community we have released version 2 of Gene Map Annotator and Pathway Profiler (GenMAPP), a new GenMAPP database schema, and integrated resources for pathway analysis. We have redesigned the GenMAPP database to support multiple gene annotations and species as well as custom species database creation for a potentially unlimited number of species. We have expanded our pathway resources by utilizing homology information to translate pathway content between species and extending existing pathways with data derived from conserved protein interactions and coexpression. We have implemented a new mode of data visualization to support analysis of complex data, including time-course, single nucleotide polymorphism (SNP), and splicing. GenMAPP version 2 also offers innovative ways to display and share data by incorporating HTML export of analyses for entire sets of pathways as organized web pages. CONCLUSION: GenMAPP version 2 provides a means to rapidly interrogate complex experimental data for pathway-level changes in a diverse range of organisms

SNPLogic: an interactive single nucleotide polymorphism selection, annotation, and prioritization system

SNPLogic (http://www.snplogic.org) brings together single nucleotide polymorphism (SNP) information from numerous sources to provide a comprehensive SNP selection, annotation and prioritization system for design and analysis of genotyping projects. SNPLogic integrates information about the genetic context of SNPs (gene, chromosomal region, functional location, haplotypes tags and overlap with transcription factor binding sites, splicing sites, miRNAs and evolutionarily conserved regions), genotypic data (allele frequencies per population and validation method), coverage of commercial arrays (ParAllele, Affymetrix and Illumina), functional predictions (modeled on structure and sequence) and connections or established associations (biological pathways, gene ontology terms and OMIM disease terms). The SNPLogic web interface facilitates construction and annotation of user-defined SNP lists that can be saved, shared and exported. Thus, SNPLogic can be used to identify and prioritize candidate SNPs, assess custom and commercial arrays panels and annotate new SNP data with publicly available information. We have found integration of SNP annotation in the context of pathway information and functional prediction scores to be a powerful approach to the analysis and interpretation of SNP-disease association data

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

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs

Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThing Explorer is distributed as a lightweight application that dynamically retrieves information at query time. More information can be found at https://explorer.biothings.io, and code is available at https://github.com/biothings/biothings_explorer

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

BACKGROUND: Many complementary solutions are available for the identifier mapping problem. 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. RESULTS: Here we present BridgeDb, a software framework for gene, protein and metabolite identifier mapping. This framework provides a standardized interface layer through which bioinformatics tools can be connected to different identifier mapping services. This approach makes it easier for tool developers to support identifier mapping. Mapping services can be combined or merged to support multi-omics experiments or to integrate custom microarray annotations. BridgeDb provides its own ready-to-go mapping services, both in webservice and local database forms. However, the framework is intended for customization and adaptation to any identifier mapping service. BridgeDb has already been integrated into several bioinformatics applications. CONCLUSION: By uncoupling bioinformatics tools from mapping services, BridgeDb improves capability and flexibility of those tools. All described software is open source and available at http://www.bridgedb.org