PREDICTION OF CHRONIC BACTERIAL INFECTION BY IDENTIFICATION OF INTER CELLULAR RESPONSES OF GENETIC FUSION CENTERS - Rai, Ankush; R, Jagadeesh Kannan (Asian Journal of Pharmaceutical and Clinical Research, 2017)
In the present study we have designed an algorithm for early detection of DNA fusion to discover the potential transcription which embodies the fusion of gene products derivable from the human DNA with that of bacterial and cancerous viruses, resulting from the several breakage points and re-assembling of different chromosomes, or that of within a chromosome. Without relying on existing annotations the proposed algorithm proves its efficacy in detecting alignment of RNA sequences from unannotated splice variants of known genome strands. Using this algorithm in the age of Big Data analytics the potential threat of cancer, tuberculosis, tumors & asthma can be predicted beforehand while scaling such effects, ranging from individual to population scale. We have also reported the results of the algorithm for over 90 samples with solid supporting evidences and opens a new virotherapy approach of numerically quantized cure for disease like cancer, tumors & asthma.