The technological advance in biomedicine field has led to the creation of large and dispersed heterogeneous data silos. The researchers manually explore the information from these silos to find the relationship between DNA alterations and diseases. This task is tedious and time-consuming due to a large number of dispersed data sources, the volume of information to analyze and the heterogeneity of the content format. In this article, we report the design of a tool based on mashups and interactions to identify the cause-effect relationship between diseases and alterations in the human genome considering the challenges of data integration and the support to different formats of content. The proposed tool is not limited to the genetic domain, rather it can be applied to several domains characterized by dispersed data sources and heterogeneous data formats.