Functional Annotation of the Transcriptome of the Pig, Sus scrofa, Based Upon Network Analysis of an RNAseq Transcriptional Atlas - Summers, Kim M.; Bush, Stephen J.; Wu, Chunlei; Su, Andrew I.; Muriuki, Charity; Clark, Emily L.; Finlayson, Heather A.; Eory, Lel; Waddell, Lindsey A.; Talbot, Richard; Archibald, Alan L.; Hume, David A. (Frontiers in Genetics, 2020)
The domestic pig (Sus scrofa) is both an economically-important livestock species and a model for biomedical research. Two highly-contiguous pig reference genomes have recently been released. To support functional annotation of the pig genomes and comparative analysis with large human transcriptomic datasets, we aimed to create a pig gene expression atlas. To achieve this objective, we extended a previous approach developed for the chicken. We down-loaded RNAseq datasets from public repositories, down-sampled to a common depth and quantified expression against a reference transcriptome using the mRNA quantitation tool, Kallisto. We then used the network analysis tool, Graphia to identify clusters of transcripts that were co-expressed across the merged dataset. Consistent with the principle of guilt-by-association, we identified co-expression clusters that were highly tissue or cell-type restricted and contained transcription factors that have previously been implicated in lineage determination. Other clusters were enriched for transcripts associated with biological processes, such as the cell cycle and oxidative phosphorylation. The same approach was used to identify co-expression clusters within RNAseq data from multiple individual liver and brain samples, highlighting cell type, process and region-specific gene expression. Evidence of conserved expression can add confidence to assignment of orthology between pig and human genes. Many transcripts currently identified as novel genes with ENSSSCG or LOC IDs were found to be co-expressed with annotated neighbouring transcripts in the same orientation indicating they may be products of the same transcriptional unit. The meta-analytic approach to utilising public RNAseq data is extendable to include new datasets and new species and provides a framework to support the Functional Annotation of Animals Genomes (FAANG) initiative.