Design and implementation of a generalized laboratory data model
<p>Abstract</p> <p>Background</p> <p>Investigators in the biological sciences continue to exploit laboratory automation methods and have dramatically increased the rates at which they can generate data. In many environments, the methods themselves also evolve in a rapid...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2007-09-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/8/362 |
| _version_ | 1811260044725977088 |
|---|---|
| author | Nhan Mike Carmichael Lynn Leong Shin Hepler Todd Crouse Kevin Chinwalla Asif T Dooling David J Pohl Craig S Smith Scott Wendl Michael C Oberkfell Benjamin J Mardis Elaine R Hillier LaDeana W Wilson Richard K |
| author_facet | Nhan Mike Carmichael Lynn Leong Shin Hepler Todd Crouse Kevin Chinwalla Asif T Dooling David J Pohl Craig S Smith Scott Wendl Michael C Oberkfell Benjamin J Mardis Elaine R Hillier LaDeana W Wilson Richard K |
| author_sort | Nhan Mike |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Investigators in the biological sciences continue to exploit laboratory automation methods and have dramatically increased the rates at which they can generate data. In many environments, the methods themselves also evolve in a rapid and fluid manner. These observations point to the importance of robust information management systems in the modern laboratory. Designing and implementing such systems is non-trivial and it appears that in many cases a database project ultimately proves unserviceable.</p> <p>Results</p> <p>We describe a general modeling framework for laboratory data and its implementation as an information management system. The model utilizes several abstraction techniques, focusing especially on the concepts of inheritance and meta-data. Traditional approaches commingle event-oriented data with regular entity data in <it>ad hoc </it>ways. Instead, we define distinct regular entity and event schemas, but fully integrate these via a standardized interface. The design allows straightforward definition of a "processing pipeline" as a sequence of events, obviating the need for separate workflow management systems. A layer above the event-oriented schema integrates events into a workflow by defining "processing directives", which act as automated project managers of items in the system. Directives can be added or modified in an almost trivial fashion, i.e., without the need for schema modification or re-certification of applications. Association between regular entities and events is managed via simple "many-to-many" relationships. We describe the programming interface, as well as techniques for handling input/output, process control, and state transitions.</p> <p>Conclusion</p> <p>The implementation described here has served as the Washington University Genome Sequencing Center's primary information system for several years. It handles all transactions underlying a throughput rate of about 9 million sequencing reactions of various kinds per month and has handily weathered a number of major pipeline reconfigurations. The basic data model can be readily adapted to other high-volume processing environments.</p> |
| first_indexed | 2024-04-12T18:41:35Z |
| format | Article |
| id | doaj.art-7568770e0c5d4d3fb26310466bd3e958 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T18:41:35Z |
| publishDate | 2007-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-7568770e0c5d4d3fb26310466bd3e9582022-12-22T03:20:46ZengBMCBMC Bioinformatics1471-21052007-09-018136210.1186/1471-2105-8-362Design and implementation of a generalized laboratory data modelNhan MikeCarmichael LynnLeong ShinHepler ToddCrouse KevinChinwalla Asif TDooling David JPohl Craig SSmith ScottWendl Michael COberkfell Benjamin JMardis Elaine RHillier LaDeana WWilson Richard K<p>Abstract</p> <p>Background</p> <p>Investigators in the biological sciences continue to exploit laboratory automation methods and have dramatically increased the rates at which they can generate data. In many environments, the methods themselves also evolve in a rapid and fluid manner. These observations point to the importance of robust information management systems in the modern laboratory. Designing and implementing such systems is non-trivial and it appears that in many cases a database project ultimately proves unserviceable.</p> <p>Results</p> <p>We describe a general modeling framework for laboratory data and its implementation as an information management system. The model utilizes several abstraction techniques, focusing especially on the concepts of inheritance and meta-data. Traditional approaches commingle event-oriented data with regular entity data in <it>ad hoc </it>ways. Instead, we define distinct regular entity and event schemas, but fully integrate these via a standardized interface. The design allows straightforward definition of a "processing pipeline" as a sequence of events, obviating the need for separate workflow management systems. A layer above the event-oriented schema integrates events into a workflow by defining "processing directives", which act as automated project managers of items in the system. Directives can be added or modified in an almost trivial fashion, i.e., without the need for schema modification or re-certification of applications. Association between regular entities and events is managed via simple "many-to-many" relationships. We describe the programming interface, as well as techniques for handling input/output, process control, and state transitions.</p> <p>Conclusion</p> <p>The implementation described here has served as the Washington University Genome Sequencing Center's primary information system for several years. It handles all transactions underlying a throughput rate of about 9 million sequencing reactions of various kinds per month and has handily weathered a number of major pipeline reconfigurations. The basic data model can be readily adapted to other high-volume processing environments.</p>http://www.biomedcentral.com/1471-2105/8/362 |
| spellingShingle | Nhan Mike Carmichael Lynn Leong Shin Hepler Todd Crouse Kevin Chinwalla Asif T Dooling David J Pohl Craig S Smith Scott Wendl Michael C Oberkfell Benjamin J Mardis Elaine R Hillier LaDeana W Wilson Richard K Design and implementation of a generalized laboratory data model BMC Bioinformatics |
| title | Design and implementation of a generalized laboratory data model |
| title_full | Design and implementation of a generalized laboratory data model |
| title_fullStr | Design and implementation of a generalized laboratory data model |
| title_full_unstemmed | Design and implementation of a generalized laboratory data model |
| title_short | Design and implementation of a generalized laboratory data model |
| title_sort | design and implementation of a generalized laboratory data model |
| url | http://www.biomedcentral.com/1471-2105/8/362 |
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