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Background  Studies of  de novo  mutations offer great promise to improve our understanding of human disease. After a causal gene has been identified, it is natural to hypothesize that disease relevant mutations accumulate within a sub-sequence of the gene – for example, an exon, a protein domain, or at CpG sites. These assessments are typically qualitative, because we lack methodology to assess the statistical significance of sub-gene mutational burden ultimately to infer disease-relevant biology.    Methods  To address this issue, we present a generalized algorithm to grade the significance of  de novo  mutational burden within a gene ascertained from affected probands, based on our model for mutation rate informed by local sequence context.    Results  We applied our approach to 268 newly identified  de novo  germline mutations by re-sequencing the coding exons and flanking intronic regions of  RB1  in 642 sporadic, bilateral probands affected with retinoblastoma (RB). We confirm enrichment of loss-of-function mutations, but demonstrate that previously noted ‘hotspots’ of nonsense mutations in  RB1  are compatible with the elevated mutation rates expected at CpG sites, refuting a RB specific pathogenic mechanism. Our approach demonstrates an enrichment of splice-site donor mutations of exon 6 and 12 but depletion at exon 5, indicative of previously unappreciated heterogeneity in penetrance within this class of substitution. We demonstrate the enrichment of missense mutations to the pocket domain of  RB1 , which contains the known Arg661Trp low-penetrance mutation.    Conclusion  Our approach is generalizable to any phenotype, and affirms the importance of statistical interpretation of de novo mutations found in human genomes.    Electronic supplementary material  The online version of this article (doi:10.1186/s12864-017-3522-z) contains supplementary material, which is available to authorized users.

De novo mutational profile in RB1 clarified using a mutation rate modeling algorithm