In this paper we present the development and implementation of a comprehensive interoperable computation tool to assist biomedical researchers to conduct, analyze and interpret experiments on a significantly accelerated pace. Explosion of biological data due to large scale genomic research and advances in high throughput tools results in massive distributed datasets. High complex biological data require unconventional computational approaches and knowledge-based solutions. Distributed datasets need to be reduced to smaller datasets that can be efficiently queried. Lack of customized and tailored computational tools severely hampers efficient work in laboratories.In this paper we report on the development and use of a tailor-made computational tool for revealing the fundamental nature of DNA-Protein interactions of zinc finger type engineered DNA-binding proteins that are poised to be widely used as zinc finger nucleases (ZNFs) to regulating genes and precisely modifying the genome. Designing customized DNA-binding proteins for medical use requires the processing of very large datasets. We evaluate a cloud-based practical distributed file system with Map Reduce parallel processing and its column oriented Hbase databases. Proven genetic String algorithms are studied in distributed parallel framework. We address the practical implementation of transforming any String Algorithm to map reduce framework which can parallel process on each machine.