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openBIS: a flexible framework for managing and analyzing complex data in biology research.

Bauch A, Adamczyk I, Buczek P, Elmer FJ, Enimanev K, Glyzewski P, Kohler M, Pylak T, Quandt A, Ramakrishnan C, Beisel C, Malmström L, Aebersold R, Rinn B - BMC Bioinformatics (2011)

Bottom Line: Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.This framework can be extended and has been customized for different data types acquired by a range of technologies. openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies.The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.

ABSTRACT

Background: Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.

Results: We have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies.

Conclusions: openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.

Show MeSH
openBIS deployment model. The overview is shown in the upper half, a more detailed description of the DSS is shown in the lower half. Companion Servers are shown in green. Arrows indicate how the components interact with each other.
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Figure 3: openBIS deployment model. The overview is shown in the upper half, a more detailed description of the DSS is shown in the lower half. Companion Servers are shown in green. Arrows indicate how the components interact with each other.

Mentions: The basic openBIS deployment consists of two servers, the Application Server (AS) and the Data Store Server (DSS) (Figure 3). Generally speaking, the AS manages the metadata and links to the data while the DSS manages the data itself. To this end, the AS sets up and uses an RDBMS to persist users, authorization information, entities like data spaces and samples and their metadata, as well as index information about all datasets. The DSS manages the datasets in the data store, which is not writable by other parts of the system. Different types of clients like e.g. a web browser, a graphical Matlab client, or a command-line client can access openBIS through the AS and DSS.


openBIS: a flexible framework for managing and analyzing complex data in biology research.

Bauch A, Adamczyk I, Buczek P, Elmer FJ, Enimanev K, Glyzewski P, Kohler M, Pylak T, Quandt A, Ramakrishnan C, Beisel C, Malmström L, Aebersold R, Rinn B - BMC Bioinformatics (2011)

openBIS deployment model. The overview is shown in the upper half, a more detailed description of the DSS is shown in the lower half. Companion Servers are shown in green. Arrows indicate how the components interact with each other.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3275639&req=5

Figure 3: openBIS deployment model. The overview is shown in the upper half, a more detailed description of the DSS is shown in the lower half. Companion Servers are shown in green. Arrows indicate how the components interact with each other.
Mentions: The basic openBIS deployment consists of two servers, the Application Server (AS) and the Data Store Server (DSS) (Figure 3). Generally speaking, the AS manages the metadata and links to the data while the DSS manages the data itself. To this end, the AS sets up and uses an RDBMS to persist users, authorization information, entities like data spaces and samples and their metadata, as well as index information about all datasets. The DSS manages the datasets in the data store, which is not writable by other parts of the system. Different types of clients like e.g. a web browser, a graphical Matlab client, or a command-line client can access openBIS through the AS and DSS.

Bottom Line: Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.This framework can be extended and has been customized for different data types acquired by a range of technologies. openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies.The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland.

ABSTRACT

Background: Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.

Results: We have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies.

Conclusions: openBIS is currently being used by several SystemsX.ch and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.

Show MeSH