Mouse genomic and cellular annotations

Abstract

Mice have emerged as one of the most popular and valuable model organisms in the research of human biology. This is due to their genetic and physiological similarity to humans, short generation times, availability of genetically homologous inbred strains, and relatively easy laboratory maintenance. Therefore, following the release of the initial human reference genome, the generation of the mouse reference genome was prioritised and represented an important scientific resource for the mouse genetics community. In 2002, the Mouse Genome Sequencing Consortium published an initial draft of the mouse reference genome which contained ~ 96% of the euchromatic genome of female C57BL/6 J mice. Almost two decades on from the publication of the initial draft, sequencing efforts have continued to increase the completeness and accuracy of the C57BL/6 J reference genome alongside advances in genome annotation. Additionally new sequencing technologies have provided a wealth of data that has added to the repertoire of annotations associated with traditional genomic annotations. Including but not limited to advances in regulatory elements, the 3D genome and individual cellular states. In this review we focus on the reference genome C57BL/6 J and summarise the different aspects of genomic and cellular annotations, as well as their relevance to mouse genetic research. We denote a genomic annotation as a functional unit of the genome. Cellular annotations are annotations of cell type or state, defined by the transcriptomic expression profile of a cell. Due to the wide-ranging number and diversity of annotations describing the mouse genome, we focus on gene, repeat and regulatory element annotation as well as two relatively new technologies; 3D genome architecture and single-cell sequencing outlining their utility in genetic research and their current challenges.

Fig. 1

Number of annotations in: a GENCODE and RefSeq for mm39. Only annotations that could be obviously matched between resources have been included. b RepeatMasker for mm39

Fig. 2

Annotation of scRNA-Seq data. A Single-cell experimental data is taken as input. B Input data is analysed using either unsupervised or supervised analysis. C Unsupervised analysis is done via clustering, for which there are many algorithms and single-cell tools, such as Seurat, Signac, Monocle and ScanPy D Clustering is done with the guidance of supporting evidence from previous data to identify known clusters, and where necessary identify novel clusters, leading to a new single-cell cluster annotation. E The cluster annotations then form part of the reference datasets which feed into supporting evidence, F and also are the basis for supervised classification of single-cell data. G Supervised classification of single-cell data relies on reference annotations to label cells. Some tools such as Alona and scMCA enable automated annotation, but other tools such as Garnet and ScPred are self-trained. H Supervised classification then produces annotated cells based off of a reference dataset of choice