General Information
| Description | A data integration framework for annotating high-throughput datasets |
| Homepage | https://github.com/childsish/sofia/releases/latest |
Publications
SoFIA: a data integration framework for annotating high-throughput datasets
Motivation:
Integrating heterogeneous datasets from several sources is a common bioinformatics task that often requires implementing a complex workflow intermixing database access, data filtering, format conversions, identifier mapping, among further diverse operations. Data integration is especially important when annotating next generation sequencing data, where a multitude of diverse tools and heterogeneous databases can be used to provide a large variety of annotation for genomic locations, such a single nucleotide variants or genes. Each tool and data source is potentially useful for a given project and often more than one are used in parallel for the same purpose. However, software that always produces all available data is difficult to maintain and quickly leads to an excess of data, creating an information overload rather than the desired goal-oriented and integrated result.
Results:
We present SoFIA, a framework for workflow-driven data integration with a focus on genomic annotation. SoFIA conceptualizes workflow templates as comprehensive workflows that cover as many data integration operations as possible in a given domain. However, these templates are not intended to be executed as a whole; instead, when given an integration task consisting of a set of input data and a set of desired output data, SoFIA derives a minimal workflow that completes the task. These workflows are typically fast and create exactly the information a user wants without requiring them to do any implementation work. Using a comprehensive genome annotation template, we highlight the flexibility, extensibility and power of the framework using real-life case studies.
Availability and Implementation:
https://github.com/childsish/sofia/releases/latest under the GNU General Public License
Contact:
liam.childs@hu-berlin.de
Supplementary information:
Supplementary data are available at Bioinformatics online.
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