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Cancer is one of the leading causes of death in the world. Approximately one fifth of deaths in the western industrial nations are caused by cancer. Every year several hundreds of thousands of new patients are diagnosed with cancer and several thousands die of cancer. Scientists have been conducting research from different angles for effective prevention, diagnosis and cure of Cancer.
Ever since the genetic basis of cancer has been demonstrated, a race has been ignited globally in the scientific community to identify potential oncogenes and tumor suppressor genes. The genetics of the tumors are complex in nature where combinations of loss of function mutations in tumor suppressor genes and gain of function mutations in oncogenes cause cancers. The identification of these genes is extremely important to devise effective therapies to treat cancer. Insertional mutagenesis systems such as sleeping beauty provide an elegant way to identify genes involved in cancers. More and more researchers are adopting the Sleeping Beauty system for their insertional mutagenesis experiments to identify potential cancer causing genes. Given next generation sequence technologies and the vast amount of data they generate requires novel bioinformatics techniques to process, analyze and meaningfully interpret the data. The goal of this project is to develop a publicly available system for researchers worldwide to analyze the sequence data resulting from insertional mutagenesis experiments.
This system will identify and annotate all the insertion sites resulting from the sequencing of the experiment. It will also identify the Common Insertion sites (CIS) and genes with Common Insertion Sites (gCIS). The Common Insertion Sites being the regions in the genome that are targeted more often than by chance. The whole system is accessible as a web application for use by researchers worldwide performing insertional mutagenesis experiments.