Genome-based detection methods provide the most conclusive means for establishing the presence of microbial species. A prime example of their use is in the detection of bacterial species, many of which are naturally vital or dangerous to human health, or can be genetically engineered to be so. However, current genomic detection methods are cost-prohibitive and inevitably use unique sensors that are specific to each species to be detected. In this thesis we advocate the use of combinatorial and non-specific identifiers for detection, made possible by exploiting the sparsity inherent in the species detection problem in a clinical or environmental sample. By modifying the sensor design process, we have developed new molecular biology tools with advantages that were not possible in their previous incarnations. Chief among these advantages are a universal species detection platform, the ability to discover unknown species, and the elimination of PCR, an expensive and laborious amplification step prerequisite in every molecular biology detection technique. Finally, we introduce a sparsity-based model for analyzing the millions of raw sequencing reads generated during whole genome sequencing for species detection, and achieve significant reductions in computational speed and high accuracy.