title: HotSpotter: efficient visualization of driver mutations.
abstract: BACKGROUND
  Driver mutations are positively selected during the evolution of cancers. The relative frequency of a particular mutation within a gene is typically used as a criterion for identifying a driver mutation. However, driver mutations may occur with relative infrequency at a particular site, but cluster within a region of the gene. When analyzing across different cancers, particular mutation sites or mutations within a particular region of the gene may be of relatively low frequency in some cancers, but still provide selective growth advantage.
  
  
  RESULTS
  This paper presents a method that allows rapid and easy visualization of mutation data sets and identification of potential gene mutation hotspot sites and/or regions. As an example, we identified hotspot regions in the NFE2L2 gene that are potentially functionally relevant in endometrial cancer, but would be missed using other analyses.
  
  
  CONCLUSIONS
  HotSpotter is a quick, easy-to-use visualization tool that delivers gene identities with associated mutation locations and frequencies overlaid upon a large cancer mutation reference set. This allows the user to identify potential driver mutations that are less frequent in a cancer or are localized in a hotspot region of relatively infrequent mutations.
authors.forename: Jason
authors.initials: J
authors.lastname: Roszik
authors.affiliation: Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, 7455 Fannin St, Houston, TX 77054, USA. swoodman@mdanderson.org.
authors.forename: Scott E
authors.initials: SE
authors.lastname: Woodman
mesh_terms: D019295:Computational Biology / Q000379:methods*; D009154:Mutation; D012984:Software