Data acquisition, data storage, and data presentation in a modern genetics laboratory

Modern genetic analysis can be divided into three main areas of investigation. The first is data acquisition, in the form of genomic sequence and the cataloguing of polymorphism data of the single nucleotide polymorphism variety (so called SNPs). Once identified, such genetic information can be adapted into high throughput tests to examine genetic information in large populations, making the analysis of sufficiently large numbers both cost and time effective so that relatively low-penetrant genetic effects can be accurately detected. The third step is correlating variation with phenotype (e.g. disease susceptibility or resistance) for a variety of disorders is paramount in our motivation and indeed is a common goal of modern human genetic analysis. While the technology to acquire vast amounts of genetic data is now well established and continues to expand, the ability to deal with such data, from the process of acquisition, storage, and analysis depends fundamentally on a solid informatics infrastructure as an essential component. Indeed, most of the major gains in productivity in this field are to be realized on the informatics front, and involve automating data acquisition, defining and sorting data in databases for quality control and analysis and facilitating access to data for the large variety of data analyses. Informatics-related issues including those relating to data acquisition, database structure, and analysis tools are summarized here in an effort to define some of the issues relevant to establishing informatics infrastructure in a small genetics laboratory focused on resequencing human immune response genes. From inherited diseases to drug efficacy to the specific genetic changes occurring during tumor development, this new field of medical genetics promises a profound impact on the state of human health. Ultimately, any and all advances in this field will continue to depend on major investments in informatics.