A Fusion Detection Algorithm Targeted to Gene Expression Outliers Using Rna-Seq Data

Nowadays there exist many bioinformatics tools that analyse sequencing data to identify genetic aberrations. The genetic aberrations can be amplification, mutation, copy number variation, fusion and so on. And they may cause pathology such as cancer. The identification of driving mechanism of pathology may lead us towards personalized or precise medicine. Personalized medicine is a medical model that uses genetic information of person’s disease to diagnose or treat the disease. The fusion detection tools are a subset of the bioinformatics tools that are targeted to find gene fusions. Usually, they have long execution time and work on the whole dataset to find all possible fusions. In this work we have designed and implemented target based fusion detection pipeline that works on a single gene. We had the sequencing data and the target gene that is highly expressed as input. We used alignment tools, statistical analysis, filtering and data mining techniques to find possible fusion partners