Mutations provide the raw material of evolution, and thus our ability to study evolution depends fundamentally on whether we have precise measure- ments of mutational rates and patterns. Here we explore the rates and patterns of mutations using i) de novo mutations from Drosophila melanogaster mutation accumulation lines and ii) polymorphisms segregating at extremely low frequencies. The first, mutation accumulation (MA) lines, are the product of maintaining flies in tiny populations for many generations, therefore rendering natural selection ineffective and allowing new mutations to accrue in the genome. In addition to generating a novel dataset of sequenced MA lines, we perform a meta-analysis of all published MA studies in D. melanogaster, which allows more precise estimates of mutational patterns across the genome. In the second half of this work, we identify polymorphisms segregating at extremely low frequencies using several publicly available population genomic data sets from natural populations of D. melanogaster. Extremely rare polymorphisms are difficult to detect with high confidence due to the problem of distinguish- ing them from sequencing error, however a dataset of true rare polymorphisms would allow the quantification of mutational patterns. This is due to the fact that rare polymorphisms, much like de novo mutations, are on average younger and also relatively unaffected by the filter of natural selection. We identify a high quality set of ∼70,000 rare polymorphisms, fully validated with resequencing, and use this dataset to measure mutational patterns in the genome. This includes identifying a high rate of multi-nucleotide mutation events at both short (∼5bp) and long (∼1kb) genomic distances, showing that mutation drives GC content lower in already GC-poor regions, and finding that the context-dependency of the mutation spectrum predicts long-term evolutionary patterns at four-fold synonymous sites. We also show that de novo mutations from independent mutation accumulation experiments display similar patterns of single nucleotide mutation, and match well the patterns of mutation found in natural populations.