In cancer research, background models for mutation rates have been extensively 
calibrated in coding regions, leading to the identification of many driver 
genes, recurrently mutated more than expected. Noncoding regions are also 
associated with disease; however, background models for them have not been 
investigated in as much detail. This is partially due to limited noncoding 
functional annotation. Also, great mutation heterogeneity and potential 
correlations between neighboring sites give rise to substantial overdispersion 
in mutation count, resulting in problematic background rate estimation. Here, we 
address these issues with a new computational framework called LARVA. It 
integrates variants with a comprehensive set of noncoding functional elements, 
modeling the mutation counts of the elements with a β-binomial distribution to 
handle overdispersion. LARVA, moreover, uses regional genomic features such as 
replication timing to better estimate local mutation rates and mutational 
hotspots. We demonstrate LARVA's effectiveness on 760 whole-genome tumor 
sequences, showing that it identifies well-known noncoding drivers, such as 
mutations in the TERT promoter. Furthermore, LARVA highlights several novel 
highly mutated regulatory sites that could potentially be noncoding drivers. We 
make LARVA available as a software tool and release our highly mutated 
annotations as an online resource (larva.gersteinlab.org).