Design and implementation of a generalized laboratory data model.

BACKGROUND: 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. RESULTS: 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 ad hoc 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. CONCLUSION: 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.

A physical map of the chicken genome.

Strategies for assembling large, complex genomes have evolved to include a combination of whole-genome shotgun sequencing and hierarchal map-assisted sequencing. Whole-genome maps of all types can aid genome assemblies, generally starting with low-resolution cytogenetic maps and ending with the highest resolution of sequence. Fingerprint clone maps are based upon complete restriction enzyme digests of clones representative of the target genome, and ultimately comprise a near-contiguous path of clones across the genome. Such clone-based maps are used to validate sequence assembly order, supply long-range linking information for assembled sequences, anchor sequences to the genetic map and provide templates for closing gaps. Fingerprint maps are also a critical resource for subsequent functional genomic studies, because they provide a redundant and ordered sampling of the genome with clones. In an accompanying paper we describe the draft genome sequence of the chicken, Gallus gallus, the first species sequenced that is both a model organism and a global food source. Here we present a clone-based physical map of the chicken genome at 20-fold coverage, containing 260 contigs of overlapping clones. This map represents approximately 91% of the chicken genome and enables identification of chicken clones aligned to positions in other sequenced genomes.