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- Bessani, A., et al.
(författare)
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BiobankCloud : A platform for the secure storage, sharing, and processing of large biomedical data sets
- 2016
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Ingår i: 1st International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2015 and Workshop on Big-Graphs Online Querying, Big-O(Q) 2015 held in conjunction with 41st International Conference on Very Large Data Bases, VLDB 2015. - Cham : Springer. - 9783319415758 - 9783319415765 ; , s. 89-105
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Konferensbidrag (refereegranskat)abstract
- Biobanks store and catalog human biological material that is increasingly being digitized using next-generation sequencing (NGS). There is, however, a computational bottleneck, as existing software systems are not scalable and secure enough to store and process the incoming wave of genomic data from NGS machines. In the BiobankCloud project, we are building a Hadoop-based platform for the secure storage, sharing, and parallel processing of genomic data. We extended Hadoop to include support for multi-tenant studies, reduced storage requirements with erasure coding, and added support for extensible and consistent metadata. On top of Hadoop, we built a scalable scientific workflow engine featuring a proper workflow definition language focusing on simple integration and chaining of existing tools, adaptive scheduling on Apache Yarn, and support for iterative dataflows. Our platform also supports the secure sharing of data across different, distributed Hadoop clusters. The software is easily installed and comes with a user-friendly web interface for running, managing, and accessing data sets behind a secure 2-factor authentication. Initial tests have shown that the engine scales well to dozens of nodes. The entire system is open-source and includes pre-defined workflows for popular tasks in biomedical data analysis, such as variant identification, differential transcriptome analysis using RNA-Seq, and analysis of miRNA-Seq and ChIP-Seq data.
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- Bux, M., et al.
(författare)
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SAASFEE : Scalable scientific workflow execution engine
- 2015
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Ingår i: Proceedings of the VLDB Endowment. - : Association for Computing Machinery (ACM). - 2150-8097. ; 8:12, s. 1892-1895, s. 1892-1903
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Tidskriftsartikel (refereegranskat)abstract
- Across many fields of science, primary data sets like sensor read-outs, time series, and genomic sequences are analyzed by complex chains of specialized tools and scripts exchanging intermediate results in domain-specific file formats. Scientific work ow management systems (SWfMSs) support the development and execution of these tool chains by providing work ow specification languages, graphical editors, fault-tolerant execution engines, etc. However, many SWfMSs are not prepared to handle large data sets because of inadequate support for distributed computing. On the other hand, most SWfMSs that do support distributed computing only allow static task execution orders. We present SAASFEE, a SWfMS which runs arbitrarily complex work ows on Hadoop YARN. Work ows are specified in Cuneiform, a functional work ow language focusing on parallelization and easy integration of existing software. Cuneiform work ows are executed on Hi-WAY, a higher-level scheduler for running work ows on YARN. Distinct features of SAASFEE are the ability to execute iterative work ows, an adaptive task scheduler, re-executable provenance traces, and compatibility to selected other work ow systems. In the demonstration, we present all components of SAASFEE using real-life work ows from the field of genomics.
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