Meiotic recombination between highly-similar duplicated sequences (non-allelic homologous recombination, NAHR) generates deletions, duplications, inversions, and translocations, and is responsible for genetic diseases known as ‘genomic disorders’, most of which are caused by altered copy number of dosage sensitive genes. NAHR Hotspots have been identified within some duplicated sequences. We have developed sperm-based assays to measure the de novo rate of reciprocal deletions and duplications at 4 NAHR hotspots. We used these assays to dissect the relative rates of NAHR between different pairs of duplicated sequences. We show that: (i) these NAHR hotspots are specific to meiosis, (ii) deletions are generated at a higher rate than their reciprocal duplications in the male germline and (iii) some of these genomic disorders are likely to have been under-ascertained clinically, most notably the duplication of 7q11, the reciprocal of the Williams-Beuren Syndrome deletion.

DNA methylation is an indispensible epigenetic modification of mammalian genomes. Consequently there is great interest in strategies for genome-wide/whole-genome DNA methylation analysis, and immunoprecipitation-based methods have proven to be a powerful option. Such methods are rapidly shifting the bottleneck from data generation to data analysis, necessitating the development of better analytical tools. Until now, a major analytical difficulty associated with immunoprecipitation-based DNA methylation profiling has been the inability to estimate absolute methylation levels. Here we report the development of a novel cross-platform algorithm – Bayesian Tool for Methylation Analysis (Batman) – for analyzing Methylated DNA Immunoprecipitation (MeDIP) profiles generated using arrays (MeDIP-chip) or next-generation sequencing (MeDIP-seq). The latter is an approach we have developed to elucidate the first high-resolution whole-genome DNA methylation profile (DNA methylome) of any mammalian genome. MeDIP-seq/MeDIP-chip combined with Batman represent robust, quantitative, and cost-effective functional genomic strategies for elucidating the function of DNA methylation.