RNAseq of mus musculus melanom
RNA-seq data was processed using the TrimGalore toolkit1 which employs Cutadapt2 to trim low quality bases and Illumina sequencing adapters from the 3 end of the reads.  Only reads that were 20nt or longer after trimming were kept for further analysis.  Reads were mapped to the GRCm38v68 version of the mouse genome and transcriptome3 using the STAR RNA-seq alignment tool4.  Reads were kept for subsequent analysis if they mapped to a single genomic location.   Gene counts were compiled using the HTSeq tool5.  Only genes that had at least 10 reads in any given library were used in subsequent analysis.  Normalization and differential expression was carried out using the DESeq26 Bioconductor7 package with the R statistical programming environment8. The false discovery rate was calculated to control for multiple hypothesis testing.  Gene set enrichment analysis9 was performed to identify differentially regulated pathways and gene ontology terms for each of the comparisons performed.