Implementing a genomic data management system using iRODS in the Wellcome Trust Sanger Institute
1 Wellcome Trust Sanger Institute, Informatics System Group, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
2 Wellcome Trust Sanger Institute, New Sequencing Technologies, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
3 Wellcome Trust Sanger Institute, Infrastructure Management Team, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, UK
BMC Bioinformatics 2011, 12:361 doi:10.1186/1471-2105-12-361Published: 9 September 2011
Increasingly large amounts of DNA sequencing data are being generated within the Wellcome Trust Sanger Institute (WTSI). The traditional file system struggles to handle these increasing amounts of sequence data. A good data management system therefore needs to be implemented and integrated into the current WTSI infrastructure. Such a system enables good management of the IT infrastructure of the sequencing pipeline and allows biologists to track their data.
We have chosen a data grid system, iRODS (Rule-Oriented Data management systems), to act as the data management system for the WTSI. iRODS provides a rule-based system management approach which makes data replication much easier and provides extra data protection. Unlike the metadata provided by traditional file systems, the metadata system of iRODS is comprehensive and allows users to customize their own application level metadata. Users and IT experts in the WTSI can then query the metadata to find and track data.
The aim of this paper is to describe how we designed and used (from both system and user viewpoints) iRODS as a data management system. Details are given about the problems faced and the solutions found when iRODS was implemented. A simple use case describing how users within the WTSI use iRODS is also introduced.
iRODS has been implemented and works as the production system for the sequencing pipeline of the WTSI. Both biologists and IT experts can now track and manage data, which could not previously be achieved. This novel approach allows biologists to define their own metadata and query the genomic data using those metadata.