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Background Biological data resources have become heterogeneous and derive from multiple sources. This introduces challenges in the management and utilization of this data in software development. Although efforts are underway to create a standard format for the transmission and storage of biological data, this objective has yet to be fully realized. Results This work describes an application programming interface (API) that provides a framework for developing an effective biological knowledge ontology for Java-based software projects. The API provides a robust framework for the data acquisition and management needs of an ontology implementation. In addition, the API contains classes to assist in creating GUIs to represent this data visually. Conclusions The Knowledge Discovery Object Model (KDOM) API is particularly useful for medium to large applications, or for a number of smaller software projects with common characteristics or objectives. KDOM can be coupled effectively with other biologically relevant APIs and classes. Source code, libraries, documentation and examples are available at . PMID:14583100

The University of California, Santa Cruz (UCSC) genome database is among the most used sources of genomic annotation in human and other organisms. The database offers an excellent web-based graphical user interface (the UCSC genome browser) and several means for programmatic queries. A simple application programming interface (API) in a scripting language aimed at the biologist was however not yet available. Here, we present the Ruby UCSC API, a library to access the UCSC genome database using Ruby. The API is designed as a BioRuby plug-in and built on the ActiveRecord 3 framework for the object-relational mapping, making writing SQL statements unnecessary. The current version of the API supports databases of all organisms in the UCSC genome database including human, mammals, vertebrates, deuterostomes, insects, nematodes, and yeast.The API uses the bin index-if available-when querying for genomic intervals. The API also supports genomic sequence queries using locally downloaded *.2bit files that are not stored in the official MySQL database. The API is implemented in pure Ruby and is therefore available in different environments and with different Ruby interpreters (including JRuby). Assisted by the straightforward object-oriented design of Ruby and ActiveRecord, the Ruby UCSC API will facilitate biologists to query the UCSC genome database programmatically. The API is available through the RubyGem system. Source code and documentation are available at -ucsc-api/ under the Ruby license. Feedback and help is provided via the website at

The Ensembl Project provides release-specific Perl APIs for efficient high-level programmatic access to data stored in various Ensembl database schema. Although Perl scripts are perfectly suited for processing large volumes of text-based data, Perl is not ideal for developing large-scale software applications nor embedding in graphical interfaces. The provision of a novel Java API would facilitate type-safe, modular, object-orientated development of new Bioinformatics tools with which to access, analyse and visualize Ensembl data. The JEnsembl API implementation provides basic data retrieval and manipulation functionality from the Core, Compara and Variation databases for all species in Ensembl and EnsemblGenomes and is a platform for the development of a richer API to Ensembl datasources. The JEnsembl architecture uses a text-based configuration module to provide evolving, versioned mappings from database schema to code objects. A single installation of the JEnsembl API can therefore simultaneously and transparently connect to current and previous database instances (such as those in the public archive) thus facilitating better analysis repeatability and allowing 'through time' comparative analyses to be performed. Project development, released code libraries, Maven repository and documentation are hosted at SourceForge ( ).

Lately, there is a trend in scientific projects to look for computing resources in the volunteering community. In addition, to reduce the development effort required to port the scientific software stack to all the known platforms, the use of Virtual Machines (VMs)u is becoming increasingly popular. Unfortunately their use further complicates the software installation and operation, restricting the volunteer audience to sufficiently expert people. CernVM WebAPI is a software solution addressing this specific case in a way that opens wide new application opportunities. It offers a very simple API for setting-up, controlling and interfacing with a VM instance in the users computer, while in the same time offloading the user from all the burden of downloading, installing and configuring the hypervisor. WebAPI comes with a lightweight javascript library that guides the user through the application installation process. Malicious usage is prohibited by offering a per-domain PKI validation mechanism. In this contribution we will overview this new technology, discuss its security features and examine some test cases where it is already in use.

The Cancer Imaging Archive (TCIA) hosts publicly available de-identified medical images of cancer from over 25 body sites and over 30,000 patients. Over 400 published studies have utilized freely available TCIA images. Images and metadata are available for download through a web interface or a REST API. Here we present TCIApathfinder, an R client for the TCIA REST API. TCIApathfinder wraps API access in user-friendly R functions that can be called interactively within an R session or easily incorporated into scripts. Functions are provided to explore the contents of the large database and to download image files. TCIApathfinder provides easy access to TCIA resources in the highly popular R programming environment. TCIApathfinder is freely available under the MIT license as a package on CRAN ( -project.org/web/packages/TCIApathfinder/index.html) and at Copyright ©2018, American Association for Cancer Research.

Background Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server. Results MetNetAPI is a versatile Application Programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only. Conclusions An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, Microsoft.NET and R programming environments and offers flexible query and broad

ChEBI is a database and ontology of chemical entities of biological interest. It is widely used as a source of identifiers to facilitate unambiguous reference to chemical entities within biological models, databases, ontologies and literature. ChEBI contains a wealth of chemical data, covering over 46,500 distinct chemical entities, and related data such as chemical formula, charge, molecular mass, structure, synonyms and links to external databases. Furthermore, ChEBI is an ontology, and thus provides meaningful links between chemical entities. Unlike many other resources, ChEBI is fully human-curated, providing a reliable, non-redundant collection of chemical entities and related data. While ChEBI is supported by a web service for programmatic access and a number of download files, it does not have an API library to facilitate the use of ChEBI and its data in cheminformatics software. To provide this missing functionality, libChEBI, a comprehensive API library for accessing ChEBI data, is introduced. libChEBI is available in Java, Python and MATLAB versions from , and provides full programmatic access to all data held within the ChEBI database through a simple and documented API. libChEBI is reliant upon the (automated) download and regular update of flat files that are held locally. As such, libChEBI can be embedded in both on- and off-line software applications. libChEBI allows better support of ChEBI and its data in the development of new cheminformatics software. Covering three key programming languages, it allows for the entirety of the ChEBI database to be accessed easily and quickly through a simple API. All code is open access and freely available.

The NCI Genomic Data Commons (GDC) was launched in 2016 and makes available over 4 petabytes (PB) of cancer genomic and associated clinical data to the research community. This dataset continues to grow and currently includes over 14,500 patients. The GDC is an example of a biomedical data commons, which collocates biomedical data with storage and computing infrastructure and commonly used web services, software applications, and tools to create a secure, interoperable, and extensible resource for researchers. The GDC is (i) a data repository for downloading data that have been submitted to it, and also a system that (ii) applies a common set of bioinformatics pipelines to submitted data; (iii) reanalyzes existing data when new pipelines are developed; and (iv) allows users to build their own applications and systems that interoperate with the GDC using the GDC Application Programming Interface (API). We describe the GDC API and how it has been used both by the GDC itself and by third parties. Cancer Res; 77(21); e15-18. ©2017 AACR . ©2017 American Association for Cancer Research. 2b1af7f3a8