The mouse is the most widely used model organism to study human disease, but often mouse biology cannot be extrapolated to humans. A deep comparison of mouse and human physiology at the molecular level is essential for understanding under which circumstances the mouse can be a suitable model of human biology and for creating better mouse models. Advances in next-generation sequencing technologies fostered genome-wide annotation of functional DNA elements, enabling extensive comparison of the human and mouse genomes.
At the transcriptional level, human and mouse gene expression profiles are conserved overall, although the degree of conservation varies depending on the tissues and the genes that are compared. Therefore, the question of whether the human and mouse transcriptomes cluster preferentially by tissue or organ or by species does not have an answer overall, and it depends specifically on the genes being considered.
Conservation of expression is not a direct consequence of conservation in regulatory sequences, including promoters and enhancers. Although gene regulatory networks are preserved overall between human and mouse, transcription binding sites are often not conserved.
Inter-individual genetic variation can affect human gene expression, but such variation cannot be modelled in inbred strains of laboratory mice because their genetic variation is small compared to the human population. An expansion of the current studies on the relationship between genetic variation and gene expression in outbred mice might provide helpful insights to understand the same relationship in humans.
Emerging technologies — such single-cell genomics and single-cell spatial transcriptomics — and time series experiments will improve the annotation of human and mouse genomes, refine the current definitions of homologous cell types and homologous (molecular) phenotypes, and ultimately help scientists to identify which mouse models are the most appropriate to address a given biological question.
Cross-species comparisons of genomes, transcriptomes and gene regulation are now feasible at unprecedented resolution and throughput, enabling the comparison of human and mouse biology at the molecular level. Insights have been gained into the degree of conservation between human and mouse at the level of not only gene expression but also epigenetics and inter-individual variation. However, a number of limitations exist, including incomplete transcriptome characterization and difficulties in identifying orthologous phenotypes and cell types, which are beginning to be addressed by emerging technologies. Ultimately, these comparisons will help to identify the conditions under which the mouse is a suitable model of human physiology and disease, and optimize the use of animal models.
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Chinwalla, A. T. et al. Initial sequencing and comparative analysis of the mouse genome. Nature 420, 520–562 (2002). This article comprehensively characterizes the initial sequence of the mouse genome and is still a valuable reference for comparative genomics.
Adams, D. J. & van der Weyden, L. Contemporary approaches for modifying the mouse genome. Physiol. Genomics 34, 225–238 (2008).
Bedell, M. A., Jenkins, N. A. & Copeland, N. G. Mouse models of human disease. Part I: techniques and resources for genetic analysis in mice. Genes Dev. 11, 1–10 (1997).
Singh, P., Schimenti, J. C. & Bolcun-Filas, E. A mouse geneticist's practical guide to CRISPR applications. Genetics 199, 1–15 (2015).
Bult, C. J. et al. Mouse genome database 2016. Nucleic Acids Res. 44, D840–D847 (2016).
Qin, W. et al. Generating mouse models using CRISPR-Cas9-mediated genome editing. Curr. Protoc. Mouse Biol. 6, 39–66 (2016).
European Commission. Report from the commission to the council and the European parliament: seventh report on the statistics on the number of animals used for experimental and other scientific purposes in the member states of the European Union. Eur-lex http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52013DC0859&from=EN (2013).
Home Office. Annual statistics of scientific procedures on living animals Great Britain 2014. Gov.uk https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/469508/spanimals14.pdf (2014).
Hay, M., Thomas, D. W., Craighead, J. L., Economides, C. & Rosenthal, J. Clinical development success rates for investigational drugs. Nat. Biotechnol. 32, 40–51 (2014). A comprehensive survey of clinical success rates across the drug industry.
Mak, I., Evaniew, N. & Ghert, M. Lost in translation: animal models and clinical trials in cancer treatment. Am. J. Transl Res. 6, 114–118 (2014).
Morgan, R. A. Human tumor xenografts: the good, the bad, and the ugly. Mol. Ther. 20, 882 (2012).
Lonsdale, J. et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Abbott, A. Europe to map the human epigenome. Nature 477, 518 (2011).
The FANTOM Consortium and the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Yue, F. et al. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515, 355–364 (2014). This paper from the Mouse ENCODE Consortium presents an extensive catalogue of mouse DNA elements identified through hundreds of next-generation sequencing assays.
Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).
Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).
Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).
Mudge, J. M. & Harrow, J. Creating reference gene annotation for the mouse C57BL6/J genome assembly. Mamm. Genome 26, 366–378 (2015).
Pervouchine, D. et al. Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression for thousands of genes. Nat. Commun. 6, 5903 (2015).
Herrero, J. et al. Ensembl comparative genomics resources. Database (Oxford) 2016, bav096 (2016).
Wang, Y., Gao, L., Conrad, C. G. & Andreadis, A. Saitohin, which is nested within the tau gene, interacts with tau and Abl and its human-specific allele influences Abl phosphorylation. J. Cell. Biochem. 112, 3482–3488 (2011).
Shi, X., Sun, M., Liu, H., Yao, Y. & Song, Y. Long non-coding RNAs: a new frontier in the study of human diseases. Cancer Lett. 339, 159–166 (2013).
Wapinski, O. & Chang, H. Y. Long noncoding RNAs and human disease. Trends Cell Biol. 21, 354–361 (2011).
Esteller, M. Non-coding RNAs in human disease. Nat. Rev. Genet. 12, 861–874 (2011).
Derrien, T. et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 22, 1775–1789 (2012).
Cabili, M. N. et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 25, 1915–1927 (2011).
Ulitsky, I. Evolution to the rescue: using comparative genomics to understand long non-coding RNAs. Nat. Rev. Genet. 17, 601–614 (2016). This Review summarizes current strategies for identifying lncRNAs and their function through comparative analysis across different species.
Nawrocki, E. P. et al. Rfam 12.0: updates to the RNA families database. Nucleic Acids Res. 43, D130–D137 (2014).
Pignatelli, M. et al. ncRNA orthologies in the vertebrate lineage. Database (Oxford) 2016, bav127 (2016).
Hezroni, H. et al. Principles of long noncoding RNA evolution derived from direct comparison of transcriptomes in 17 species. Cell Rep. 11, 1110–1122 (2015).
Necsulea, A. et al. The evolution of lncRNA repertoires and expression patterns in tetrapods. Nature 505, 635–640 (2014).
Washietl, S., Kellis, M. & Garber, M. Evolutionary dynamics and tissue specificity of human long noncoding RNAs in six mammals. Genome Res. 24, 616–628 (2014).
Chen, J. et al. Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs. Genome Biol. 17, 19 (2016).
Engström, P. G. et al. Complex loci in human and mouse genomes. PLoS Genet. 2, e47 (2006).
Faghihi, M. A. & Wahlestedt, C. Regulatory roles of natural antisense transcripts. Nat. Rev. Mol. Cell Biol. 10, 637–643 (2009).
Roux, J., Gonzalez-Porta, M. & Robinson-Rechavi, M. Comparative analysis of human and mouse expression data illuminates tissue-specific evolutionary patterns of miRNAs. Nucleic Acids Res. 40, 5890–5900 (2012).
Meunier, J. et al. Birth and expression evolution of mammalian microRNA genes. Genome Res. 23, 34–45 (2013).
Kutter, C. et al. Pol III binding in six mammals shows conservation among amino acid isotypes despite divergence among tRNA genes. Nat. Genet. 43, 948–955 (2011).
Zhang, B. et al. Changes in snoRNA and snRNA abundance in the human, chimpanzee, macaque, and mouse brain. Genome Biol. Evol. 8, 840–850 (2016).
Matera, A. G., Terns, R. M. & Terns, M. P. Non-coding RNAs: lessons from the small nuclear and small nucleolar RNAs. Nat. Rev. Mol. Cell Biol. 8, 209–220 (2007).
Huang, Y. et al. Molecular functions of small regulatory noncoding RNA. Biochemistry (Mosc.) 78, 221–230 (2013).
Li, Y. & Kowdley, K. V. MicroRNAs in common human diseases. Genomics Proteomics Bioinformatics 10, 246–253 (2012).
Lin, S. & Gregory, R. I. MicroRNA biogenesis pathways in cancer. Nat. Rev. Cancer 15, 321–333 (2015).
Park, C. Y., Choi, Y. & McManus, M. T. Analysis of microRNA knockouts in mice. Hum. Mol. Genet. 19, R169–R175 (2010).
Kozomara, A. & Griffiths-Jones, S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39, D152–D157 (2010).
Landgraf, P. et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414 (2007).
Goodenbour, J. M. & Pan, T. Diversity of tRNA genes in eukaryotes. Nucleic Acids Res. 34, 6137–6146 (2006).
Chan, P. P. & Lowe, T. M. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 37, D93–D97 (2009).
Zheng-Bradley, X., Rung, J., Parkinson, H. & Brazma, A. Large scale comparison of global gene expression patterns in human and mouse. Genome Biol. 11, R124 (2010).
Jolliffe, I. Principal Component Analysis (Wiley Online Library, 2002).
Cox, M. A. & Cox, T. F. in Handbook of Data Visualization (eds Chen, C., Härdle, W. & Unwin, A.) 315–347 (Springer, 2008).
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
McCall, M. N., Uppal, K., Jaffee, H. A., Zilliox, M. J. & Irizarry, R. A. The gene expression barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res. 39, D1011–D1015 (2011).
Liao, B.-Y. & Zhang, J. Evolutionary conservation of expression profiles between human and mouse orthologous genes. Mol. Biol. Evol. 23, 530–540 (2006). This was the first paper to highlight the importance of normalization in comparative transcriptomics studies.
Yanai, I., Graur, D. & Ophir, R. Incongruent expression profiles between human and mouse orthologous genes suggest widespread neutral evolution of transcription control. OMICS 8, 15–24 (2004).
Pishesha, N. et al. Transcriptional divergence and conservation of human and mouse erythropoiesis. Proc. Natl Acad. Sci. USA 111, 4103–4108 (2014).
Schroder, K. et al. Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages. Proc. Natl Acad. Sci. USA 109, E944–E953 (2012).
Jubb, A. W., Young, R. S., Hume, D. A. & Bickmore, W. A. Enhancer turnover is associated with a divergent transcriptional response to glucocorticoid in mouse and human macrophages. J. Immunol. 196, 813–822 (2016).
Seok, J. et al. Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc. Natl Acad. Sci. USA 110, 3507–3512 (2013).
Takao, K. & Miyakawa, T. Genomic responses in mouse models greatly mimic human inflammatory diseases. Proc. Natl Acad. Sci. USA 112, 1167–1172 (2015).
Warren, H. S. et al. Mice are not men. Proc. Natl Acad. Sci. USA 112, E345 (2015).
Shay, T., Lederer, J. A. & Benoist, C. Genomic responses to inflammation in mouse models mimic humans: we concur, apples to oranges comparisons won't do. Proc. Natl Acad. Sci. USA 112, E346 (2015).
Romero, I. G., Ruvinsky, I. & Gilad, Y. Comparative studies of gene expression and the evolution of gene regulation. Nat. Rev. Genet. 13, 505–516 (2012).
Necsulea, A. & Kaessmann, H. Evolutionary dynamics of coding and non-coding transcriptomes. Nat. Rev. Genet. 15, 734–748 (2014). This is a Review on comparative transcriptomics studies in vertebrates.
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621–628 (2008).
Stamatoyannopoulos, J. A. et al. An encyclopedia of mouse DNA elements (Mouse ENCODE). Genome Biol. 13, 418 (2012).
Lin, S. et al. Comparison of the transcriptional landscapes between human and mouse tissues. Proc. Natl Acad. Sci. USA 111, 17224–17229 (2014).
Sudmant, P. H., Alexis, M. S. & Burge, C. B. Meta-analysis of RNA-seq expression data across species, tissues and studies. Genome Biol. 16, 287 (2015).
Su, A. I. et al. Large-scale analysis of the human and mouse transcriptomes. Proc. Natl Acad. Sci. USA 99, 4465–4470 (2002).
Chan, E. T. et al. Conservation of core gene expression in vertebrate tissues. J. Biol. 8, 33 (2009).
Gilad, Y. & Mizrahi-Man, O. A reanalysis of mouse ENCODE comparative gene expression data. F1000Res. 4, 121 (2015).
Brawand, D. et al. The evolution of gene expression levels in mammalian organs. Nature 478, 343–348 (2011).
Barbosa-Morais, N. L. et al. The evolutionary landscape of alternative splicing in vertebrate species. Science 338, 1587–1593 (2012).
Merkin, J., Russell, C., Chen, P. & Burge, C. B. Evolutionary dynamics of gene and isoform regulation in mammalian tissues. Science 338, 1593–1599 (2012). References 76 and 77 are the first systematic studies on alternative splicing evolution across vertebrates by RNA-seq.
Breschi, A. et al. Gene-specific patterns of expression variation across organs and species. Genome Biol. 17, 151 (2016). This study investigated the pattern of gene expression variation across tissues and species individually for each gene along the set of vertebrate orthologues.
Bortvin, A. et al. Incomplete reactivation of Oct4-related genes in mouse embryos cloned from somatic nuclei. Development 130, 1673–1680 (2003).
Hardison, R. C. A guide to translation of research results from model organisms to human. Genome Biol. 17, 161 (2016).
Soumillon, M. et al. Cellular source and mechanisms of high transcriptome complexity in the mammalian testis. Cell Rep. 3, 2179–2190 (2013).
Liao, B.-Y. & Zhang, J. Low rates of expression profile divergence in highly ex-pressed genes and tissue-specific genes during mammalian evolution. Mol. Biol. Evol. 23, 1119–1128 (2006).
Wang, Y. & Rekaya, R. A comprehensive analysis of gene expression evolution between humans and mice. Evol. Bioinform. Online 5, 81–90 (2009).
Koonin, E. V. & Wolf, Y. I. Constraints and plasticity in genome and molecular-phenome evolution. Nat. Rev. Genet. 11, 487–498 (2010).
Vakhrusheva, O. A., Bazykin, G. A. & Kondrashov, A. S. Genome-level analysis of selective constraint without apparent sequence conservation. Genome Biol. Evol. 5, 532–541 (2013).
Carvunis, A.-R. et al. Evidence for a common evolutionary rate in metazoan transcriptional networks. eLife 4, e11615 (2015).
Weirauch, M. T. & Hughes, T. R. Conserved expression without conserved regulatory sequence: the more things change, the more they stay the same. Trends Genet. 26, 66–74 (2010).
Pai, A. A. & Gilad, Y. Comparative studies of gene regulatory mechanisms. Curr. Opin. Genet. Dev. 29, 68–74 (2014).
Odom, D. T. et al. Tissue-specific transcriptional regulation has diverged significantly between human and mouse. Nat. Genet. 39, 730–732 (2007).
Johnson, R. et al. Evolution of the vertebrate gene regulatory network controlled by the transcriptional repressor REST. Mol. Biol. Evol. 26, 1491–1507 (2009).
Schmidt, D. et al. Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science 328, 1036–1040 (2010).
Kunarso, G. et al. Transposable elements have rewired the core regulatory network of human embryonic stem cells. Nat. Genet. 42, 631–634 (2010).
Ballester, B. et al. Multi-species, multi-transcription factor binding highlights con-served control of tissue-specific biological pathways. eLife 3, e02626 (2014).
Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and char-acterization. Nat. Methods 9, 215–216 (2012).
Cheng, Y. et al. Principles of regulatory information conservation between mouse and human. Nature 515, 371–375 (2014). A comparative analysis of the binding sites of 32 transcription factors through ChIP–seq in human and mouse cell lines.
Denas, O. et al. Genome-wide comparative analysis reveals human–mouse regulatory landscape and evolution. BMC Genomics 16, 87 (2015).
Vierstra, J. et al. Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution. Science 346, 1007–1012 (2014). An analysis of open chromatin regions in 45 mouse cell and tissue types by DNase-seq, with a comparison to humans.
Bourque, G. et al. Evolution of the mammalian transcription factor binding repertoire via transposable elements. Genome Res. 18, 1752–1762 (2008).
Villar, D. et al. Enhancer evolution across 20 mammalian species. Cell 160, 554–566 (2015). This study compares the liver enhancer landscape of 20 mammals, as inferred from ChIP–seq of H3K27ac and H3K4me3.
Stergachis, A. B. et al. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature 515, 365–370 (2014).
Young, R. S. Lineage-specific genomics: frequent birth and death in the human genome. Bioessays 38, 654–663 (2016).
Visel, A. et al. Ultraconservation identifies a small subset of extremely constrained developmental enhancers. Nat. Genet. 40, 158–160 (2008).
Pennacchio, L. A. et al. In vivo enhancer analysis of human conserved non-coding sequences. Nature 444, 499–502 (2006).
Visel, A., Minovitsky, S., Dubchak, I. & Pennacchio, L. A. VISTA Enhancer Browser — a database of tissue-specific human enhancers. Nucleic Acids Res. 35, D88–D92 (2007).
Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).
1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015). A milestone paper on human genetic variation from the 1000 Genomes Project Consortium.
Levy, S. et al. The diploid genome sequence of an individual human. PLoS Biol. 5, e254 (2007).
Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013).
GTEx Consortium et al. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Yang, H. et al. Subspecific origin and haplotype diversity in the laboratory mouse. Nat. Genet. 43, 648–655 (2011).
Beck, J. A. et al. Genealogies of mouse inbred strains. Nat. Genet. 24, 23–25 (2000).
Wade, C. M. et al. The mosaic structure of variation in the laboratory mouse genome. Nature 420, 574–578 (2002). This paper explains how the most common laboratory mouse strains were created from wild mice.
Yang, H., Bell, T. A., Churchill, G. A. & de Villena, F. P.-M. On the subspecific origin of the laboratory mouse. Nat. Genet. 39, 1100–1107 (2007).
Keane, T. M. et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).
Matsuo, N. et al. Behavioral profiles of three C57BL/6 substrains. Front. Behav. Neurosci. 4, 29 (2010).
Kiselycznyk, C. & Holmes, A. All (C57BL/6) mice are not created equal. Front. Neurosci. 5, 10 (2011).
Doran, A. G. et al. Deep genome sequencing and variation analysis of 13 inbred mouse strains defines candidate phenotypic alleles, private variation, and homozygous truncating mutations. Genome Biol. 17, 167 (2016).
Turk, R. et al. Gene expression variation between mouse inbred strains. BMC Genomics 5, 57 (2004).
Holgersen, K. et al. High-resolution gene expression profiling using RNA sequencing in patients with inflammatory bowel disease and in mouse models of colitis. J. Crohns Colitis 9, 492–506 (2015).
Mostafavi, S. et al. Variation and genetic control of gene expression in primary immunocytes across inbred mouse strains. J. Immunol. 193, 4485–4496 (2014).
Pritchard, C. C., Hsu, L., Delrow, J. & Nelson, P. S. Project normal: defining normal variance in mouse gene expression. Proc. Natl Acad. Sci. USA 98, 13266–13271 (2001).
Bogue, M. A., Churchill, G. A. & Chesler, E. J. Collaborative cross and diversity outbred data resources in the Mouse Phenome Database. Mamm. Genome 26, 511–520 (2015). This paper illustrates the current status of the Mouse Phenome Database as an established resource for studying mouse genetic variation.
Chick, J. M. et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature 534, 500–505 (2016). This paper describes the relationship between eQTLs and protein quantitative trait loci in outbred mice.
Deng, Q., Ramskold, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).
Vickaryous, M. K. & Hall, B. K. Human cell type diversity, evolution, development, and classification with special reference to cells derived from the neural crest. Biol. Rev. 81, 425–455 (2006).
Abdulreda, M., Caicedo, A. & Berggren, P. A natural body window to study human pancreatic islet cell function and survival. CellR4 1, 111–122 (2013).
Mabbott, N. A., Baillie, J. K., Brown, H., Freeman, T. C. & Hume, D. A. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics 14, 632 (2013).
Hume, D. A., Summers, K. M., Raza, S., Baillie, J. K. & Freeman, T. C. Functional clustering and lineage markers: insights into cellular differentiation and gene function from large-scale microarray studies of purified primary cell populations. Genomics 95, 328–338 (2010).
Lee, Y.-s., Krishnan, A., Zhu, Q. & Troyanskaya, O. G. Ontology-aware classification of tissue and cell-type signals in gene expression profiles across platforms and technologies. Bioinformatics 29, 3036–3044 (2013).
Li, J. et al. Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types. EMBO Rep. 17, 178–187 (2016).
Saliba, A.-E., Westermann, A. J., Gorski, S. A. & Vogel, J. Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res. 42, 8845–8860 (2014).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C. & Teichmann, S. A. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).
Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).
Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).
Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
Scialdone, A. et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535, 289–293 (2016).
Xue, Z. et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500, 593–597 (2013). A dynamic transcriptional study of human and mouse early embryo development at single-cell resolution.
Blekhman, R., Marioni, J. C., Zumbo, P., Stephens, M. & Gilad, Y. Sex-specific and lineage-specific alternative splicing in primates. Genome Res. 20, 180–189 (2010).
Mele, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).
Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E. & Hogenesch, J. B. A circadian gene expression atlas in mammals: implications for biology and medicine. Proc. Natl Acad. Sci. USA 111, 16219–16224 (2014).
Romero, I. G., Pai, A. A., Tung, J. & Gilad, Y. RNA-seq: impact of RNA degradation on transcript quantification. BMC Biol. 12, 42 (2014).
Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially re-solved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Gharib, S. A. et al. Of mice and men: comparative proteomics of bronchoalveolar fluid. Eur. Respir. J. 35, 1388–1395 (2010).
Maher, B. ENCODE: the human encyclopaedia. Nature 489, 46–48 (2012).
Von Meyenn, F. et al. Comparative principles of DNA methylation reprogramming during human and mouse in vitro primordial germ cell specification. Dev. Cell 39, 104–115 (2016).
Nakamura, T. et al. A developmental coordinate of pluripotency among mice, monkeys and humans. Nature 537, 57–62 (2016).
Rosenthal, N. & Brown, S. The mouse ascending: perspectives for human-disease models. Nat. Cell Biol. 9, 993–999 (2007).
Onos, K. D., Rizzo, S. J. S., Howell, G. R. & Sasner, M. Toward more predictive genetic mouse models of Alzheimer's disease. Brain Res. Bull. 122, 1–11 (2016).
Blesa, J., Phani, S., Jackson-Lewis, V. & Przedborski, S. Classic and new animal models of Parkinson's disease. J. Biomed. Biotechnol. 2012, 845618 (2012).
Ribeiro, F. M., Camargos, E. R., de Souza, L. C. & Teixeira, A. L. Animal models of neurodegenerative diseases. Rev. Bras. Psiquiatr. 35 (Suppl. 2), S82–S91 (2013).
Antonarakis, S. E. Down syndrome and the complexity of genome dosage imbalance. Nat. Rev. Genet. 18, 147–163 (2016).
Rueda, N., Florez, J. & Martinez-Cue, C. Mouse models of Down syndrome as a tool to unravel the causes of mental disabilities. Neural Plast. 2012, 584071 (2012).
Steimer, T. Animal models of anxiety disorders in rats and mice: some conceptual issues. Dialogues Clin. Neurosci. 13, 495–506 (2011).
Schweinfurth, N. & Lang, U. E. Behavioral testing of mice concerning anxiety and depression. Z. Psychol. 223, 151–156 (2015).
Lynch, W. J., Nicholson, K. L., Dance, M. E., Morgan, R. W. & Foley, P. L. Animal models of substance abuse and addiction: implications for science, animal welfare, and society. Comp. Med. 60, 177–188 (2010).
Ellacott, K. L., Morton, G. J., Woods, S. C., Tso, P. & Schwartz, M. W. Assessment of feeding behavior in laboratory mice. Cell Metab. 12, 10–17 (2010).
Mestas, J. & Hughes, C. C. Of mice and not men: differences between mouse and human immunology. J. Immunol. 172, 2731–2738 (2004).
Karpel, M. E., Boutwell, C. L. & Allen, T. M. BLT humanized mice as a small animal model of HIV infection. Curr. Opin. Virol. 13, 75–80 (2015).
Chayama, K. et al. Animal model for study of human hepatitis viruses. J. Gastroenterol. Hepatol. 26, 13–18 (2011).
Silverman, J. L., Yang, M., Lord, C. & Crawley, J. N. Behavioural phenotyping assays for mouse models of autism. Nat. Rev. Neurosci. 11, 490–502 (2010).
Vanhooren, V. & Libert, C. The mouse as a model organism in aging research: usefulness, pitfalls and possibilities. Ageing Res. Rev. 12, 8–21 (2013).
Bult, C. J. et al. Mouse Tumor Biology (MTB): a database of mouse models for human cancer. Nucleic Acids Res. 43, D818–D824 (2015).
Justice, M. J. & Dhillon, P. Using the mouse to model human disease: increasing validity and reproducibility. Dis. Model. Mech. 9, 101–103 (2016).
Rangarajan, A. & Weinberg, R. A. Comparative biology of mouse versus human cells: modelling human cancer in mice. Nat. Rev. Cancer 3, 952–959 (2003).
Egan, M. E. How useful are cystic fibrosis mouse models? Drug Discov. Today Dis. Models 6, 35–41 (2009).
Fisher, J. T., Zhang, Y. & Engelhardt, J. F. Comparative biology of cystic fibrosis animal models. Methods Mol. Biol. 742, 311–334 (2011).
McGreevy, J. W., Hakim, C. H., McIntosh, M. A. & Duan, D. Animal models of Duchenne muscular dystrophy: from basic mechanisms to gene therapy. Dis. Model. Mech. 8, 195–213 (2015).
Modrek, B. & Lee, C. J. Alternative splicing in the human, mouse and rat genomes is associated with an increased frequency of exon creation and/or loss. Nat. Genet. 34, 177–180 (2003).
Abril, J. F., Castelo, R. & Guigo, R. Comparison of splice sites in mammals and chicken. Genome Res. 15, 111–119 (2005).
Sorek, R. & Ast, G. Intronic sequences flanking alternatively spliced exons are conserved between human and mouse. Genome Res. 13, 1631–1637 (2003).
Zambelli, F., Pavesi, G., Gissi, C., Horner, D. S. & Pesole, G. Assessment of orthologous splicing isoforms in human and mouse orthologous genes. BMC Genomics 11, 534 (2010).
Tilgner, H. et al. Comprehensive transcriptome analysis using synthetic long-read sequencing reveals molecular co-association of distant splicing events. Nat. Biotechnol. 33, 736–742 (2015).
Sharon, D., Tilgner, H., Grubert, F. & Snyder, M. A single-molecule long-read survey of the human transcriptome. Nat. Biotechnol. 31, 1009–1014 (2013).
The authors acknowledge G. Schiavo and D. Cirillo for insightful discussion. The authors acknowledge support of the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013–2017' of the Centres de Recerca de Catalunya Programme/Generalitat de Catalunya and the Programa de Ayudas Formación de Personal investigador (FPI) del Ministerio de Economia y Competitividad (BES-2012-055848).
The authors declare no competing financial interests.
Preserved genomic order and orientation of genes or other elements between species.
- Xenograft models of cancer
Created when cancerous tissue from a person's primary tumour is implanted directly into an immunodeficient mouse.
- Chromatin domains
Functionally distinct chromosomal regions that confer structural organization to eukaryotic genomes and represent regulatory units for gene expression and chromosome behaviour.
- Cap analysis of gene expression
(CAGE). In CAGE, short (∼ 20-nucleotide) sequence tags originating from the 5′ end of full-length mRNAs are sequenced to identify transcription events on a genome-wide scale.
- GENCODE annotation
The GENCODE project produces high-quality reference gene annotation and experimental validation for human and mouse genomes.
- Long non-coding RNAs
(lncRNAs). Non-protein coding transcripts that are longer than 200 nucleotides. This somewhat arbitrary limit distinguishes lncRNAs from small regulatory RNAs.
Homologous genes in different species that have evolved from a common ancestral gene by speciation.
(miRNAs). Derived from primary transcripts with features similar to mRNAs that are enzymatically processed to their mature length of 21–24 nucleotides by Drosha and Dicer enzymes.
- Transfer RNAs
(tRNAs). Adaptor RNA molecules (76–90 nucleotides) that serve as the physical link between the mRNA and the amino acid sequence of proteins by carrying an amino acid to the ribosome, as directed by the codon in an mRNA.
- Small nuclear RNAs and small nucleolar RNAs
(snRNAs and snoRNAs). Classes of short non-coding RNAs (100–200 nucleotides) that have important regulatory roles in nuclear ribonucleoprotein complexes.
A pair of genes that descended from a common ancestral gene.
- Hierarchical clustering
A statistical method in which objects (for example, gene expression profiles for different individuals or tissue samples) are grouped into a hierarchy, which is visualized in a dendrogram. Objects close to each other in the hierarchy, as measured by tracing the branch heights, are also close by some measure of distance — for example, between gene expression profiles. Individuals or samples with similar expression profiles will be close together in terms of branch lengths.
- Euclidean distance
The Euclidean distance between points p and q is the length of the line segment connecting them in a multidimensional space. In gene expression analysis, p and q are usually vectors of expression values in two samples or conditions.
- Dimensionality reduction techniques
These reduce multidimensional data to a minimal number of dimensions for visualization by identifying those dimensions that capture the most important information underlying the data structure.
- Principal component analysis
(PCA). Orthogonal linear transformation that transforms the original data to a new coordinate system, such that the greatest variance of the projected data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.
- Multidimensional scaling
(MDS). A technique used to display the information contained in a distance matrix. It aims to place each object in N-dimensional space such that the between-object distances are preserved as well as possible.
- t-Distributed stochastic neighbour embedding
(t-SNE). A nonlinear dimensionality reduction technique that is based on the probability distribution over pairs of high-dimensional objects that are embedded into a space of two or three dimensions. Similar objects are modelled by nearby points, and dissimilar objects are modelled by distant points.
Methods used to adjust measurements so that they can be appropriately compared among samples. For example, in microarray analysis, methods such as quantile normalization manipulate common characteristics of the data.
- DNA exaptation
The shift in the function of a DNA sequence during evolution.
- Expression quantitative trait loci
(eQTLs). Genomic loci that contribute to variation in the expression levels of mRNAs.
- Allele-specific expression
Expression variation between the two haplotypes of a diploid individual, as distinguished by heterozygous sites.
- Ischaemic time
In the case of organ donors, the time elapsed between the death of a donor and the organ extraction.
Segments of DNA that originate from functional genes, but have lost at least some of the ability of the parent gene in terms of expression or coding potential.
- Precision medicine
An emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment and lifestyle for each person.
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Breschi, A., Gingeras, T. & Guigó, R. Comparative transcriptomics in human and mouse. Nat Rev Genet 18, 425–440 (2017). https://doi.org/10.1038/nrg.2017.19
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