mice, current technical capabilities limit the performance of single cell proteomics/phosphoproteomics. As our intent with the current study was to provide a complementary and integrative multi-omic analysis of the DS, we chose to employ bulk RNA-sequencing so that our findings could be integrated and compared to proteomic/phosphoproteomic findings. Quality control of the raw sequence data was performed using FastQC/MultiQC. The sequence reads were mapped to the UCSC mm10 reference genome using STAR (Spliced Transcripts Alignment to a Reference) for the B6 mouse (Dobin et al. 2013). Although the HAP/LAP mice are derived from a heterogenous stock, read mismatch parameters were not adjusted as we find the default STAR alignment settings regarding mismatches to be liberal. (STAR allows up to 10 mismatches across an alignment which equates to 10 mismatches in 150 (2×75 paired read) bases in this experiment. Uniquely mapped reads were retained by filtering for a MAPQ of 60 during alignment and 10 during counting. Therefore, we applied a balanced approach where mismatches were allowed but there was high confidence in the mapping location for a fragment to be counted.) To evaluate the quality of the RNA-sequencing data, the numbers of reads that map to different annotated regions (e.g.
