Genomic landscape of rat strain and substrain variation.

BACKGROUND: Since the completion of the rat reference genome in 2003, whole-genome sequencing data from more than 40 rat strains have become available. These data represent the broad range of strains that are used in rat research including commonly used substrains. Currently, this wealth of information cannot be used to its full extent, because the variety of different variant calling algorithms employed by different groups impairs comparison between strains. In addition, all rat whole genome sequencing studies to date used an outdated reference genome for analysis (RGSC3.4 released in 2004). RESULTS: Here we present a comprehensive, multi-sample and uniformly called set of genetic variants in 40 rat strains, including 19 substrains. We reanalyzed all primary data using a recent version of the rat reference assembly (RGSC5.0 released in 2012) and identified over 12 million genomic variants (SNVs, indels and structural variants) among the 40 strains. 28,318 SNVs are specific to individual substrains, which may be explained by introgression from other unsequenced strains and ongoing evolution by genetic drift. Substrain SNVs may have a larger predicted functional impact compared to older shared SNVs. CONCLUSIONS: In summary we present a comprehensive catalog of uniformly analyzed genetic variants among 40 widely used rat inbred strains based on the RGSC5.0 assembly. This represents a valuable resource, which will facilitate rat functional genomic research. In line with previous observations, our genome-wide analyses do not show evidence for contribution of multiple ancestral founder rat subspecies to the currently used rat inbred strains, as is the case for mouse. In addition, we find that the degree of substrain variation is highly variable between strains, which is of importance for the correct interpretation of experimental data from different labs.

Deconvolution of overlapping isotopic clusters improves quantification of stable isotope-labeled peptides.

High-resolution mass spectrometry and the use of stable isotopes have greatly improved our ability to quantify proteomes. Typically, the relative abundance of peptides is estimated by identifying the isotopic clusters and by comparing the peak intensities of peptide pairs. However, when the mass shift between the labeled peptides is small, there can be the possibility for overlap of the isotopic clusters which will hamper quantification accuracy with a typical upwards bias for the heavier peptide. Here, we investigated the impact of the overlapping peak issue with respect to dimethyl based quantification and we confirmed there can be need for correction. In addition, we present a tool that can correct overlapping issues when they arise which is based on modeling isotopic distributions. We demonstrate that our approach leads to improved accuracy and precision of protein quantification.