In gene therapy the integration process of the viral DNA genome into the host cell genome is a necessary step for virus integration. Just few years ago, retrovirus integration was believed to be random and the chance of accidentally activating a gene was considered remote. It has been seen that this process is not random and different viruses may show different preferences to integrate in some specific areas of the genome. Tumorigensis associated to some studies in gene therapy is suspected to be caused by insertion process. Depending on whether the provirus integrates into or in the vicinity of genes (Transcription Start Sites , TSS), normal trascription can be enhanced or disrupted thus inducing oncogenic mutations. This is called “insertional mutagenesis”. Investigating whether an area over the genome could be favoured by retrovirus integration is a crucial aspect in gene therapy. These area are called “Common Integration Sites”(CIS)or “hotspots”. In the paper we stressed the importance of developing statistical procedures leading to a unique definition of CIS rather than a “problem related” definition. We here propose some statistical solutions for the search of hotspots based on the “Peaksheight distribution”, which account within the null hypothesis for the possible non-random behaviour of the integrations.