Share thisThough not always used, relational databases are the most effective method of storing animal colony data.
Maintaining a colony of laboratory animals is often vital to the success of a research effort. In fact, the use of genetically modified animals has become mainstream in all branches of modern medical and biomedical research. Rodents are among the most commonly used animals, and a large industry has grown up around producing transgenic mouse lines and distributing them to researchers. Researchers and institutions then find themselves in the position of managing animal breeding colonies and require the tools that will allow them to do that efficiently and effectively. Especially important for keeping facility costs down and productivity up are information technology tools that allow managers to keep complete records and generate comprehensive reports. By far the most effective method of record keeping for such colonies is a relational database system designed specifically for the purpose. Many such systems are readily available to researchers, especially researchers keeping transgenic mice, yet a large number of labs have not yet adopted this approach.
Relational Databases versus Spreadsheets
Relational databases are by far the most effective solution for storing animal colony data, yet they are not always used, even by labs employing software to store their animal records. All too often, well-meaning colony managers try to turn spreadsheet applications like Microsoft Excel into colony management software. Spreadsheets can, in fact, do quite well for small colonies, but quickly break down when more than about 100 animals or three generations are tracked.
Spreadsheets are simply poorly suited for the type of information most researchers need to maintain about their colonies. Spreadsheets are excellent for things like time-series data (each row contains a date and then one or more values) and experimental results (treatment data versus outcome). However, animal colony information is inherently relational. For example, when tracking a small animal colony, one might have one sheet for the animals with each row as a separate animal. Then one might have a second spreadsheet for cages. The sheet would contain details about the cages along with animal information for the animals in each cage. Then one might have a third spreadsheet for litters with information such as the date of birth and parents. Listed along with each litter, one could have information about the animals in the litter.
There are several problems with this solution. The first is data replication. If one wants to see information such as the date of birth, sex, or phenotype of an animal on the cage sheet, one must copy all of this information about that animal from the animal sheet to the cage sheet; likewise on the litter sheet. If an error is discovered, the error must then be fixed not only in the original location, but also in all locations where the data is replicated. A second, related problem is in consistency of data entry. Great care must be taken to enter information about an animal in exactly the same way across multiple sheets, as minor errors in punctuation and capitalization can cause confusion down the road. A third problem involves the usability of the data. To find, for example, information about an animal’s mother, one must search through the entire list of animals to find the one matching the recorded ID. For a few hundred mice this is feasible, but as a colony grows, it becomes tedious. Tasks such as finding all the offspring of a given animal are labor intensive for any size of colony.