Algorithm for Early Detection of Genetic Fusion in Cancer, Tumors and Chronic Bacterial Infection

Abstract

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 reassembling 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, and 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 900 samples with solid supporting evidences and opens a new virotherapy approach of numerically quantized cure for disease like cancer, tumors and asthma.

Keywords: Algorithm, computational modeling, gene fusion, DNA transcription