For over a decade, a large research community has relied on a flawed reference assembly of the genome ofRattus norvegicusknown as Rnor_6.0. The seventh assembly of the rat reference genome4mRatBN7.2, based on the inbred Brown Norway rat, corrects numerous misplaced segments, reduces base-level errors by approximately 9-fold, and increases contiguity by 290-fold, despite some remaining regions of potential misassembly. Gene annotations are now more complete, significantly improving the mapping precision of genomic, transcriptomic, and proteomics data sets. SimpleLiftOverfrom Rnor_6.0 to mRatBN7.2 misses ∼12% of variants. To facilitate the transition to mRatBN7.2, we performed a joint analysis of 163 whole genomes representing 120 strains/substrains. We defined 20.0 million sequence variations, of which 18.7 thousand are predicted to potentially impact the function of 6,677 genes. Phylogenetic analysis confirmed historical records and prior results and refined the ancestral relationship of these strains. Sixteen million polymorphisms segregate in the widely studied heterogeneous stock rat population, and 11313 million variants segregate collectively in the HXB/BXH and FXLE/LEXF strain families. Some inbred strains differ by only 132 M variants, and closely related substrains segregate by even fewer variants. We generated a new rat genetic map based on data from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites.

DNA sequencing can discover not only single-base variants but also copy-number alterations (CNAs). In shotgun sequencing, regions of CNAs show step-wise changes in read depth when compared to adjacent "normal" regions, allowing their detection by parametric statistical tests that compare the mean coverage in suspected regions against that of a baseline distribution.Traditionally, the power of such a test depends on (1) the integer number of copy number change,(2) the overall sequencing depth, (3) the length of the CNA region, (4) the read length and (5) the variation of coverage along the genome, which depends on many experimental factors, including whether the chosen platform is whole-genome, whole-exome, or targeted-panel sequencing. In cases involving inadvertent sample mixing or genuine somatic mosaicism, power also depends on the mixing ratio. However, the analysis of statistical power that considers the interplay 1