Precise estimates of the single-nucleotide mutation rate and its variability are essential to the study of human genome evolution and genetic diseases. Here we use ~36 million singleton variants observed in 3,716 whole-genome sequences to characterize the heterogeneity of germline mutation rates across the genome. Adjacent-nucleotide context is the strongest predictor of mutability, with mutation rates varying by >650-fold depending on the identity of three bases upstream or downstream of the mutated site. Histone modifications, replication timing, recombination rate, and other local genomic features further modify mutability; magnitude and direction of this modification varies with the sequence context. Compared to estimates based on common variants used in previous approaches, singleton-based estimates provide a more accurate prediction of the mutation patterns seen in an independent dataset of ~46,000 de novo mutations; and incorporating the effects of genomic features further improves the prediction. The effects of sequence contexts, genomic features, and their interactions reported here capture the most refined portrait to date of the germline mutation patterns in humans.