MuTrack: a genome analysis system for large-scale mutagenesis in the mouse
1 Department of Computer Science, Baylor University, Waco, USA
2 Life Sciences Division, Oak Ridge National Laboratories, Oak Ridge, USA
3 Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, USA
4 Computer Science and Mathematics Division, Oak Ridge National Laboratories, Oak Ridge, USA
5 Graduate School in Genome Science and Technology, University of Tennessee and Oak Ridge National Laboratories, Knoxville, USA
BMC Bioinformatics 2004, 5:11 doi:10.1186/1471-2105-5-11Published: 3 February 2004
Modern biological research makes possible the comprehensive study and development of heritable mutations in the mouse model at high-throughput. Using techniques spanning genetics, molecular biology, histology, and behavioral science, researchers may examine, with varying degrees of granularity, numerous phenotypic aspects of mutant mouse strains directly pertinent to human disease states. Success of these and other genome-wide endeavors relies on a well-structured bioinformatics core that brings together investigators from widely dispersed institutions and enables them to seamlessly integrate data, observations and discussions.
MuTrack was developed as the bioinformatics core for a large mouse phenotype screening effort. It is a comprehensive collection of on-line computational tools and tracks thousands of mutagenized mice from birth through senescence and death. It identifies the physical location of mice during an intensive phenotype screening process at several locations throughout the state of Tennessee and collects raw and processed experimental data from each domain. MuTrack's statistical package allows researchers to access a real-time analysis of mouse pedigrees for aberrant behavior, and subsequent recirculation and retesting. The end result is the classification of potential and actual heritable mutant mouse strains that become immediately available to outside researchers who have expressed interest in the mutant phenotype.
MuTrack demonstrates the effectiveness of using bioinformatics techniques in data collection, integration and analysis to identify unique result sets that are beyond the capacity of a solitary laboratory. By employing the research expertise of investigators at several institutions for a broad-ranging study, the TMGC has amplified the effectiveness of any one consortium member. The bioinformatics strategy presented here lends future collaborative efforts a template for a comprehensive approach to large-scale analysis.