The Mouse. Berlin: Springer.
25 25 Mohr, U., Dungworth, D.L., Capen, C.C. et al. (eds.) (1996). Pathology of the Aging Mouse., Vols. 1 and 2. Washington DC: ILSI Press.
26 26 Ruberte, J., Carretero, A., and Navarro, M. (eds.) (2017). Morphological Mouse Phenotyping: Anatomy, Histology and Imaging. Amsterdam: Academic.
27 27 Scudamore, C.L. (ed.) (2014). A Practical Guide to the Histology of the Mouse. Oxford: Wiley‐Blackwell.
28 28 Sundberg, J.P. and Boggess, D. (2000). Systematic Approach to Evaluation of Mouse Mutations. Boca Raton, FL: CDC Press.
29 29 Sundberg, J.P. and Ichiki, T. (eds.). Genetically Engineered Mice Handbook. Boca Raton,FL: CRC Press.
30 30 Frith, C.H. and Ward, J.M. (1988). Color Atlas of Neoplastic and Non‐neoplastic Lesions in Aging Mice. Amsterdam: Elsevier.
31 31 Turner, P.V., Brash, M.L., and Smith, D.A. (2017). Pathology of Small Animal Pets. Wiley‐Blackwell.
32 32 Wallig, M.A., Haschek, W.M., Rousseaux, C.G. et al. (eds.) (2018). Fundamentals of Toxicologic Pathology, 3e. London: Academic Press.
33 33 Smith, R.S., John, S.W.M., Nishina, P.M., and Sundberg, J.P. (eds.) (2002). Systematic Evaluation of the Mouse Eye. Boca Raton, FL: CRC Press.
34 34 Sundberg, J.P., Elson, C.O., Bedigian, H., and Birkenmeier, E.H. (1994). Spontaneous, heritable colitis in a new substrain of C3H/HeJ mice. Gastroenterology 107 (6): 1726–1735.
35 35 Ward, J.M., Mahler, J.F., Maronpot, R.R., and Sundberg, J.P. (eds.) (2000). Pathology of Genetically Engineered Mice. Ames, IA: Iowa State University Press.
36 36 Ward, J.M. and Treuting, P.M. (2014). Rodent intestinal epithelial carcinogenesis: pathology and preclinical models. Toxicol. Pathol. 42 (1): 148–161.
37 37 Radaelli, E., Castiglioni, V., Recordati, C. et al. (2016). The pathology of aging 129S6/SvEvTac mice. Vet. Pathol. 53 (2): 477–492.
38 38 Harbison, C.E., Lipman, R.D., and Bronson, R.T. (2016). Strain‐ and diet‐related lesion variability in aging DBA/2, C57BL/6, and DBA/2xC57BL/6 F1 mice. Vet. Pathol. 53 (2): 468–476.
39 39 Mikaelian, I., Nanney, L.B., Parman, K.S. et al. (2004). Antibodies that label paraffin‐embedded mouse tissues: a collaborative endeavor. Toxicol. Pathol. 32 (2): 181–191.
40 40 Janardhan, K.S., Jensen, H., Clayton, N.P., and Herbert, R.A. (2018). Immunohistochemistry in investigative and toxicologic pathology. Toxicol. Pathol. 46 (5): 488–510.
41 41 Ward, J.M. and Rehg, J.E. (2014). Rodent immunohistochemistry: pitfalls and troubleshooting. Vet. Pathol. 51 (1): 88–101.
42 42 Rehg, J.E. and Ward, J.M. (2017). Application of immunohistochemistry in toxicologic pathology of the hematopoietic system. In: Immunopathology in Toxicology and Drug Development (ed. G.A. Parker), 489–561. Springer.
43 43 Meyerholz, D.K. and Beck, A.P. (2018). Fundamental concepts for semiquantitative tissue scoring in translational research. ILAR J. 59 (1): 13–17.
44 44 Bleich, A., Mahler, M., Most, C. et al. (2004). Refined histopathologic scoring system improves power to detect colitis QTL in mice. Mamm. Genome 15 (11): 865–871.
45 45 Jurisic, G., Sundberg, J.P., Bleich, A. et al. (2010). Quantitative lymphatic vessel trait analysis suggests Vcam1 as candidate modifier gene of inflammatory bowel disease. Genes Immun. 11 (3): 219–231.
46 46 Turner, O.C., Aeffner, F., Bangari, D.S. et al. (2020). Society of toxicologic pathology digital pathology and image analysis special interest group article*: opinion on the application of artificial intelligence and machine learning to digital toxicologic pathology. Toxicol. Pathol. 48 (2): 277–294.
47 47 Aeffner, F., Zarella, M.D., Buchbinder, N. et al. (2019). Introduction to digital image analysis in whole‐slide imaging: a white paper from the digital pathology association. J. Pathol. Inform. 10: 9.
48 48 Ward, J.M., Schofield, P.N., and Sundberg, J.P. (2017). Reproducibility of histopathological findings in experimental pathology of the mouse: a sorry tail. Lab. Anim. (NY). 46 (4): 146–151.
49 49 Wolf, J.C. and Maack, G. (2017). Evaluating the credibility of histopathology data in environmental endocrine toxicity studies. Environ. Toxicol. Chem. 36 (3): 601–611.
50 50 Ince, T.A., Ward, J.M., Valli, V.E. et al. (2008). Do‐it‐yourself (DIY) pathology. Nat. Biotechnol. 26 (9): 978–979.
2 The Mouse Online: Open Mouse Biology and Pathology Data Resources for Biomedical Research:
Dale A. Begley, Paul N. Schofield, and John P. Sundberg
Introduction
Large collections of data and resources are available online, ranging from genomic and gene expression databases (GXDs) to archives of experimental data, phenotype data, and bioresources such as strains, cell lines, and tissues. While many core resources contain multispecies data and are good sources for mouse information, this review will cover some of the major websites that provide access to mouse‐centric databases (Table 2.1) and how they can be used to assist with analysis of normal, abnormal, or mutant mice.
Databases fall into two main categories; those where data are gathered, expertly curated, and presented by the database project itself, often integrating data from several sources and analytical tools. The second type of database acts as an archival platform to which data are uploaded by users, often with minimal curation. There are several examples of hybrids of various proportions where uploads are solicited by the database, curated to a different degree, and presented with core data sourced and validated by the database staff. Curated databases may also be long‐term repositories or archives of legacy datasets. Data resources are generally funded out of project/program grants and core institutional funding. The degree of currency and the depth of curation often depends on funding and funding sustainability. Metrics and indicators for database description, and to a degree for assessing database quality, can be found in ELIXIR [1], as the criteria developed for qualification as an ELIXIR core database are widely applicable. A good source of rapidly accessible core information about databases is the annual database issue of Nucleic Acids Research. Databases present their core activities and modes of access in the papers but also publish updates as significant developments are achieved. It is always useful to browse this issue when looking for new sources of data.
FAIR Data Access and Databases of Databases
The move to make scientific data openly discoverable, accessible, and useable has led to the formalization of the principles of open data, known as FAIRsharing [2]. Data has to be free, accessible, interoperable, and reusable. Increasingly databases are trying to comply with the FAIR semantic and access technical requirements as part of data discovery and interoperability, and many databases now provide access computationally through an Application Programming Interface (API) as well as through Hypertext Markup Language (HTML), together with complete data dumps and data reports. Searching using standardized semantics and ontologies provides very powerful ways of searching databases, and for databases such as MouseMine it is possible to use very powerful SPARQL Protocol and RDF (Resource Description Framework) Query Language (SPARQL) queries.
Along with standardization of database structures and access goes formalization of standards, essential for interoperability and data aggregation. Different communities have established specific standards for metadata, minimal information (MI) for reporting (MI standards), and other data structures. These standards and the characteristics of databases can now be found in databases of databases such as the very useful FAIRsharing database. FAIRsharing lists and describes standards used in databases but also the type of data and importantly the species. Currently, FAIR sharing lists 90 databases either dedicated to the mouse or with specifically mouse data [2].
The