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Provides an interface for registering, browsing and annotating Web Services to the Life Science community. BioCatalogue allows registration of services that are specific to the Life Sciences and more generic services that are a direct utility in this domain. User can discover services and annotate them. The software aims to satisfy the needs of service providers, users and experts, bringing them together in a common effort to make Web Services for biology more visible, better documented and easier to use.


Ables to efficiently integrate different type of web-services repositories mapping metadata over a general definition to support scalable service discovery and to achieve flexible inter-communication between tools. jORCA manages repositories heterogeneity supported by the Modular-API that provides a uniform view of metadata (e.g. GRID-based, WSDL-services, BioMoby and others). jORCA is a highly customizable and extensible application that accommodates a broad range of user skills featuring double-click invocation of services in conjunction with advanced execution-control, on the fly data standardization, extensibility of viewer plug-ins, drag-and-drop editing capabilities, plus a file-based browsing style and organization of favourite tools. The integration of bioinformatics Web Services is made easier to support a wider range of users.


A platform-independent software package implemented in Java. CloudDOE encapsulates technical details behind a user-friendly graphical interface, thus liberating scientists from having to perform complicated operational procedures. Users are guided through the user interface to deploy a Hadoop cloud within in-house computing environments and to run applications specifically targeted for bioinformatics, including CloudBurst, CloudBrush, and CloudRS. Interested users may collaborate to improve the source code of CloudDOE to further incorporate more MapReduce bioinformatics tools into CloudDOE and support next-generation big data open source tools, e.g., Hadoop BigTop and Spark.


Processes and generates large datasets. MapReduce is a programming model and an associated implementation that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google, and an average of one hundred thousand MapReduce jobs are executed on Google’s clusters every day, processing a total of more than twenty petabytes of data per day.


A massively parallel, computational database designed for scientific data management and scalable, in-database analytics. Fully programmable in 'R' and Python, SciDB is designed around an array data model which provides compact data storage and high performance operations on ‘omics, chemical, molecular, imaging, LC/MS, etc. data. It runs on commodity HW and performs all data loads, multidimensional selects and advanced math functions like (PCA and clustering) in parallel, entirely in the database, reducing data movement and accelerating interactive data exploration.