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Agricultural products are becoming more homogenous in order to conform to consumer expectations. In doing so, they have become a high-risk target for widespread crop failure due to pathogens or other agricultural maladies. Presented here are a collection of tools and a work-flow for the wholesale detection and characterization of disease resistance-associated proteins from whole-genome sequences. These techniques were also adapted for identifying potential miRNA regulatory sequences. Putative R-gene sequences identified from the Malus x Domestica 'Golden Delicious' genome were acquired for verification against wild apple species. Resistance gene analogs were PCR amplified from R-gene associated domains (TIR, NBS, and LRR) in wild apple cultivars. Known R-gene sequences were clustered alongside the PCR analogs and putative R-genes from 'Golden Delicious'. A covariance model approach for the de novo detection of 3,187 putative pre-miRNA regulatory sequences is also explored. Vitis vinifera sequences used to build this model were retrieved with 38% efficiency. Computational clusters may be useful in the physical mapping of sequences to the chromosomally duplicated gene clusters characteristic of R-genes. Known R-genes included in computational clusters may also clarify the function of the unknown sequences. A possibility exists for the identification of resistance genes that have been lost in the selective breeding of commercial cultivars.