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Annual Review of Animal Biosciences

Volume 13, 2025

Comparative Genomics and Epigenomics of Transcriptional Regulation

Abstract

Transcriptional regulation in response to diverse physiological cues involves complicated biological processes. Recent initiatives that leverage whole genome sequencing and annotation of regulatory elements significantly contribute to our understanding of transcriptional gene regulation. Advances in the data sets available for comparative genomics and epigenomics can identify evolutionarily constrained regulatory variants and shed light on noncoding elements that influence transcription in different tissues and developmental stages across species. Most epigenomic data, however, are generated from healthy subjects at specific developmental stages. To bridge the genotype–phenotype gap, future research should focus on generating multidimensional epigenomic data under diverse physiological conditions. Farm animal species offer advantages in terms of feasibility, cost, and experimental design for such integrative analyses in comparison to humans. Deep learning modeling and cutting-edge technologies in sequencing and functional screening and validation also provide great promise for better understanding transcriptional regulation in this dynamic field.

Keyword(s): comparative genomics and epigenomics, deep learning modeling, molecule QTL, noncoding variants, quantitative trait loci, regulatory elements, transcriptional regulation
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/content/journals/10.1146/annurev-animal-111523-102217
2025-02-18
2025-06-22
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Literature Cited

  1. 1.
    Hagai T, Chen X, Miragaia RJ, Rostom R, Gomes T, et al. 2018.. Gene expression variability across cells and species shapes innate immunity. . Nature 563:(7730):197–202
     [Crossref] [Google Scholar]
  2. 2.
    Wolf S, Melo D, Garske KM, Pallares LF, Lea AJ, Ayroles JF. 2023.. Characterizing the landscape of gene expression variance in humans. . PLOS Genet. 19:(7):e1010833
     [Crossref] [Google Scholar]
  3. 3.
    Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, et al. 2012.. Systematic localization of common disease-associated variation in regulatory DNA. . Science 337:(6099):1190–95
     [Crossref] [Google Scholar]
  4. 4.
    Mostafavi H, Spence JP, Naqvi S, Pritchard JK. 2023.. Systematic differences in discovery of genetic effects on gene expression and complex traits. . Nat. Genet. 55:(11):1866–75
     [Crossref] [Google Scholar]
  5. 5.
    Pan Z, Wang Y, Wang M, Wang Y, Zhu X, et al. 2023.. An atlas of regulatory elements in chicken: a resource for chicken genetics and genomics. . Sci. Adv. 9:(18):eade1204
     [Crossref] [Google Scholar]
  6. 6.
    Pan Z, Yao Y, Yin H, Cai Z, Wang Y, et al. 2021.. Pig genome functional annotation enhances the biological interpretation of complex traits and human disease. . Nat. Commun. 12::5848
     [Crossref] [Google Scholar]
  7. 7.
    Hill MS, Vande Zande P, Wittkopp PJ. 2021.. Molecular and evolutionary processes generating variation in gene expression. . Nat. Rev. Genet. 22:(4):203–15
     [Crossref] [Google Scholar]
  8. 8.
    Shih CH, Fay J. 2021.. Cis-regulatory variants affect gene expression dynamics in yeast. . eLife 10::e68469
     [Crossref] [Google Scholar]
  9. 9.
    Klemm SL, Shipony Z, Greenleaf WJ. 2019.. Chromatin accessibility and the regulatory epigenome. . Nat. Rev. Genet. 20:(4):207–20
     [Crossref] [Google Scholar]
  10. 10.
    Li YI, van de Geijn B, Raj A, Knowles DA, Petti AA, et al. 2016.. RNA splicing is a primary link between genetic variation and disease. . Science 352:(6285):600–4
     [Crossref] [Google Scholar]
  11. 11.
    Xiao M-S, Zhang B, Li Y-S, Gao Q, Sun W, Chen W. 2016.. Global analysis of regulatory divergence in the evolution of mouse alternative polyadenylation. . Mol. Syst. Biol. 12:(12):890
     [Crossref] [Google Scholar]
  12. 12.
    Pai AA, Cain CE, Mizrahi-Man O, De Leon S, Lewellen N, et al. 2012.. The contribution of RNA decay quantitative trait loci to inter-individual variation in steady-state gene expression levels. . PLOS Genet. 8:(10):e1003000
     [Crossref] [Google Scholar]
  13. 13.
    Genereux DP, Serres A, Armstrong J, Johnson J, Marinescu VD, et al. 2020.. A comparative genomics multitool for scientific discovery and conservation. . Nature 587:(7833):240–45
     [Crossref] [Google Scholar]
  14. 14.
    Rhie A, McCarthy SA, Fedrigo O, Damas J, Formenti G, et al. 2021.. Towards complete and error-free genome assemblies of all vertebrate species. . Nature 592:(7856):737–46
     [Crossref] [Google Scholar]
  15. 15.
    Nurk S, Koren S, Rhie A, Rautiainen M, Bzikadze AV, et al. 2022.. The complete sequence of a human genome. . Science 376:(6588):44–53
     [Crossref] [Google Scholar]
  16. 16.
    Dunham I, Kundaje A, Aldred SF, Collins PJ, Davis CA, et al. 2012.. An integrated encyclopedia of DNA elements in the human genome. . Nature 489:(7414):57–74
     [Crossref] [Google Scholar]
  17. 17.
    Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, et al. 2020.. Expanded encyclopaedias of DNA elements in the human and mouse genomes. . Nature 583:(7818):699–710
     [Crossref] [Google Scholar]
  18. 18.
    Kern C, Wang Y, Xu X, Pan Z, Halstead M, et al. 2021.. Functional annotations of three domestic animal genomes provide vital resources for comparative and agricultural research. . Nat. Commun. 12::1821
     [Crossref] [Google Scholar]
  19. 19.
    Hardison RC, Taylor J. 2012.. Genomic approaches towards finding cis-regulatory modules in animals. . Nat. Rev. Genet. 13:(7):469–83
     [Crossref] [Google Scholar]
  20. 20.
    Kaplow IM, Schäffer DE, Wirthlin ME, Lawler AJ, Brown AR, et al. 2022.. Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin. . BMC Genom. 23:(1):291
     [Crossref] [Google Scholar]
  21. 21.
    Snetkova V, Ypsilanti AR, Akiyama JA, Mannion BJ, Plajzer-Frick I, et al. 2021.. Ultraconserved enhancer function does not require perfect sequence conservation. . Nat. Genet. 53:(4):521–28
     [Crossref] [Google Scholar]
  22. 22.
    Villar D, Berthelot C, Aldridge S, Rayner TF, Lukk M, et al. 2015.. Enhancer evolution across 20 mammalian species. . Cell 160:(3):554–66
     [Crossref] [Google Scholar]
  23. 23.
    Birney E, Stamatoyannopoulos JA, Dutta A, Guigó R, Gingeras TR, et al. 2007.. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. . Nature 447:(7146):799–816
     [Crossref] [Google Scholar]
  24. 24.
    Dutta P, Talenti A, Young R, Jayaraman S, Callaby R, et al. 2020.. Whole genome analysis of water buffalo and global cattle breeds highlights convergent signatures of domestication. . Nat. Commun. 11::4739
     [Crossref] [Google Scholar]
  25. 25.
    Clark TC, Naseer S, Gundappa MK, Laurent A, Perquis A, et al. 2023.. Conserved and divergent arms of the antiviral response in the duplicated genomes of salmonid fishes. . Genomics 115:(4):110663
     [Crossref] [Google Scholar]
  26. 26.
    Dermitzakis ET, Clark AG. 2002.. Evolution of transcription factor binding sites in mammalian gene regulatory regions: conservation and turnover. . Mol. Biol. Evol. 19:(7):1114–21
     [Crossref] [Google Scholar]
  27. 27.
    Kuderna LFK, Ulirsch JC, Rashid S, Ameen M, Sundaram L, et al. 2024.. Identification of constrained sequence elements across 239 primate genomes. . Nature 625::735–42
     [Crossref] [Google Scholar]
  28. 28.
    Andrews G, Fan K, Pratt HE, Phalke N, Zoonomia Consort., et al. 2023.. Mammalian evolution of human cis-regulatory elements and transcription factor binding sites. . Science 380:(6643):eabn7930
     [Crossref] [Google Scholar]
  29. 29.
    Yue F, Cheng Y, Breschi A, Vierstra J, Wu W, et al. 2014.. A comparative encyclopedia of DNA elements in the mouse genome. . Nature 515:(7527):355–64
     [Crossref] [Google Scholar]
  30. 30.
    Shen Y, Yue F, McCleary DF, Ye Z, Edsall L, et al. 2012.. A map of the cis-regulatory sequences in the mouse genome. . Nature 488:(7409):116–20
     [Crossref] [Google Scholar]
  31. 31.
    Zu S, Li YE, Wang K, Armand EJ, Mamde S, et al. 2023.. Single-cell analysis of chromatin accessibility in the adult mouse brain. . Nature 624:(7991):378–89
     [Crossref] [Google Scholar]
  32. 32.
    Zhang K, Hocker JD, Miller M, Hou X, Chiou J, et al. A single-cell atlas of chromatin accessibility in the human genome. . Cell 184:(24):5985–6001.e19
     [Crossref] [Google Scholar]
  33. 33.
    Georgakopoulos-Soares I, Deng C, Agarwal V, Chan CSY, Zhao J, et al. 2023.. Transcription factor binding site orientation and order are major drivers of gene regulatory activity. . Nat. Commun. 14::2333
     [Crossref] [Google Scholar]
  34. 34.
    Du AY, Chobirko JD, Zhuo X, Feschotte C, Wang T. 2024.. Regulatory transposable elements in the encyclopedia of DNA elements. . Nat. Commun. 15::7594
     [Crossref] [Google Scholar]
  35. 35.
    Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, et al. 2012.. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. . Genome Res. 22:(9):1775–89
     [Crossref] [Google Scholar]
  36. 36.
    Uszczynska-Ratajczak B, Lagarde J, Frankish A, Guigó R, Johnson R. 2018.. Towards a complete map of the human long non-coding RNA transcriptome. . Nat. Rev. Genet. 19:(9):535–48
     [Crossref] [Google Scholar]
  37. 37.
    Statello L, Guo CJ, Chen LL, Huarte M. 2021.. Gene regulation by long non-coding RNAs and its biological functions. . Nat. Rev. Mol. Cell Biol. 22:(2):96–118
     [Crossref] [Google Scholar]
  38. 38.
    Jehl F, Muret K, Bernard M, Boutin M, Lagoutte L, et al. 2020.. An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues. . Sci. Rep. 10::20457
     [Crossref] [Google Scholar]
  39. 39.
    Degalez F, Charles M, Foissac S, Zhou H, Guan D, et al. 2024.. Enriched atlas of lncRNA and protein-coding genes for the GRCg7b chicken assembly and its functional annotation across 47 tissues. . Genomics 14::6588
     [Google Scholar]
  40. 40.
    Lagarrigue S, Lorthiois M, Degalez F, Gilot D, Derrien T. 2022.. LncRNAs in domesticated animals: from dog to livestock species. . Mamm. Genome 33:(2):248–70
     [Crossref] [Google Scholar]
  41. 41.
    Degalez F, Bardou P, Lagarrigue S. 2024.. GEGA (Gallus Enriched Gene Annotation): an online tool gathering genomics and functional information across 47 tissues for protein-coding genes and lncRNA enriched atlas including Ensembl & Refseq genome annotations. . NAR Genom. Bioinform. 6:(3):lqae101
     [Crossref] [Google Scholar]
  42. 42.
    Kurylo C, Guyomar C, Foissac S, Djebali S. 2023.. TAGADA: a scalable pipeline to improve genome annotations with RNA-seq data. . NAR Genom. Bioinform. 5:(4):lqad089
     [Crossref] [Google Scholar]
  43. 43.
    Sarropoulos I, Marin R, Cardoso-Moreira M, Kaessmann H. 2019.. Developmental dynamics of lncRNAs across mammalian organs and species. . Nature 571:(7766):510–14
     [Crossref] [Google Scholar]
  44. 44.
    Hezroni H, Koppstein D, Schwartz MG, Avrutin A, Bartel DP, Ulitsky I. 2015.. Principles of long noncoding RNA evolution derived from direct comparison of transcriptomes in 17 species. . Cell Rep. 11:(7):1110–22
     [Crossref] [Google Scholar]
  45. 45.
    Ulitsky I. 2016.. Evolution to the rescue: using comparative genomics to understand long non-coding RNAs. . Nat. Rev. Genet. 17:(10):601–14
     [Crossref] [Google Scholar]
  46. 46.
    Quinn JJ, Chang HY. 2016.. Unique features of long non-coding RNA biogenesis and function. . Nat. Rev. Genet. 17:(1):47–62
     [Crossref] [Google Scholar]
  47. 47.
    Muret K, Désert C, Lagoutte L, Boutin M, Gondret F, et al. 2019.. Long noncoding RNAs in lipid metabolism: literature review and conservation analysis across species. . BMC Genom. 20::882
     [Crossref] [Google Scholar]
  48. 48.
    Foissac S, Djebali S, Munyard K, Vialaneix N, Rau A, et al. 2019.. Multi-species annotation of transcriptome and chromatin structure in domesticated animals. . BMC Biol. 17::108
     [Crossref] [Google Scholar]
  49. 49.
    Smith J, Alfieri JM, Anthony N, Arensburger P, Athrey GN, et al. 2023.. Fourth Report on Chicken Genes and Chromosomes 2022. . Cytogenet. Genome Res. 162:(8–9):405–528
     [Google Scholar]
  50. 50.
    Ross CJ, Rom A, Spinrad A, Gelbard-Solodkin D, Degani N, Ulitsky I. 2021.. Uncovering deeply conserved motif combinations in rapidly evolving noncoding sequences. . Genome Biol. 22::29
     [Crossref] [Google Scholar]
  51. 51.
    Huang W, Xiong T, Zhao Y, Heng J, Han G, et al. 2024.. Computational prediction and experimental validation identify functionally conserved lncRNAs from zebrafish to human. . Nat. Genet. 56::124–35
     [Crossref] [Google Scholar]
  52. 52.
    Degalez F, Allain C, Lagoutte L, Lagarrigue S. 2023.. Gene orthology detection for long non-coding RNA (LncRNA). Presented at the 39th International Society for Animal Genetics Conference, July 2–7 , Cape Town, S. Afr:.
     [Google Scholar]
  53. 53.
    Boix CA, James BT, Park YP, Meuleman W, Kellis M. 2021.. Regulatory genomic circuitry of human disease loci by integrative epigenomics. . Nature 590:(7845):300–7
     [Crossref] [Google Scholar]
  54. 54.
    Modencode Consort., Roy S, Ernst J, Kharchenko PV, Kheradpour P, et al. 2010.. Identification of functional elements and regulatory circuits by Drosophila modENCODE. . Science 330:(6012):1787–97
     [Crossref] [Google Scholar]
  55. 55.
    Baranasic D, Hörtenhuber M, Balwierz PJ, Zehnder T, Mukarram AK, et al. 2022.. Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis-regulatory elements. . Nat. Genet. 54:(7):1037–50
     [Crossref] [Google Scholar]
  56. 56.
    Stunnenberg HG, Int. Hum. Epigenome Consort., Hirst M. 2016.. The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery. . Cell 167:(5):1145–49
     [Crossref] [Google Scholar]
  57. 57.
    Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. 2015.. Integrative analysis of 111 reference human epigenomes. . Nature 518:(7539):317–30
     [Crossref] [Google Scholar]
  58. 58.
    Meuleman W, Muratov A, Rynes E, Halow J, Lee K, et al. 2020.. Index and biological spectrum of human DNase I hypersensitive sites. . Nature 584:(7820):244–51
     [Crossref] [Google Scholar]
  59. 59.
    Vierstra J, Lazar J, Sandstrom R, Halow J, Lee K, et al. 2020.. Global reference mapping of human transcription factor footprints. . Nature 583:(7818):729–36
     [Crossref] [Google Scholar]
  60. 60.
    Pang B, Snyder MP. 2020.. Systematic identification of silencers in human cells. . Nat. Genet. 52:(3):254–63
     [Crossref] [Google Scholar]
  61. 61.
    Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al. 2012.. The accessible chromatin landscape of the human genome. . Nature 489:(7414):75–82
     [Crossref] [Google Scholar]
  62. 62.
    Sullivan PF, Meadows JRS, Gazal S, Phan BN, Li X, et al. 2023.. Leveraging base-pair mammalian constraint to understand genetic variation and human disease. . Science 380:(6643):eabn2937
     [Crossref] [Google Scholar]
  63. 63.
    Kaplow IM, Lawler AJ, Schäffer DE, Srinivasan C, Sestili HH, et al. 2023.. Relating enhancer genetic variation across mammals to complex phenotypes using machine learning. . Science 380:(6643):eabm7993
     [Crossref] [Google Scholar]
  64. 64.
    Christmas MJ, Kaplow IM, Genereux DP, Dong MX, Hughes GM, et al. 2023.. Evolutionary constraint and innovation across hundreds of placental mammals. . Science 380::eabn3943
     [Crossref] [Google Scholar]
  65. 65.
    Xiao S, Xie D, Cao X, Yu P, Xing X, et al. 2012.. Comparative epigenomic annotation of regulatory DNA. . Cell 149:(6):1381–92
     [Crossref] [Google Scholar]
  66. 66.
    Zemke NR, Armand EJ, Wang W, Lee S, Zhou J, et al. 2023.. Conserved and divergent gene regulatory programs of the mammalian neocortex. . Nature 624:(7991):390–402
     [Crossref] [Google Scholar]
  67. 67.
    Smith ZD, Chan MM, Humm KC, Karnik R, Mekhoubad S, et al. 2014.. DNA methylation dynamics of the human preimplantation embryo. . Nature 511:(7511):611–15
     [Crossref] [Google Scholar]
  68. 68.
    Partridge EC, Chhetri SB, Prokop JW, Ramaker RC, Jansen CS, et al. 2020.. Occupancy maps of 208 chromatin-associated proteins in one human cell type. . Nature 583:(7818):720–28
     [Crossref] [Google Scholar]
  69. 69.
    Fan K, Pfister E, Weng Z. 2023.. Toward a comprehensive catalog of regulatory elements. . Hum. Genet. 142:(8):1091–111
     [Crossref] [Google Scholar]
  70. 70.
    Liu H, Zeng Q, Zhou J, Bartlett A, Wang B-A, et al. 2023.. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. . Nature 624::366–77
     [Crossref] [Google Scholar]
  71. 71.
    Tian W, Zhou J, Bartlett A, Zeng Q, Liu H, et al. 2023.. Single-cell DNA methylation and 3D genome architecture in the human brain. . Science 382:(6667):eadf5357
     [Crossref] [Google Scholar]
  72. 72.
    Yue X, Xie Z, Li M, Wang K, Li X, et al. 2022.. Simultaneous profiling of histone modifications and DNA methylation via nanopore sequencing. . Nat. Commun. 13::7939
     [Crossref] [Google Scholar]
  73. 73.
    Klughammer J, Romanovskaia D, Nemc A, Posautz A, Seid CA, et al. 2023.. Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species. . Nat. Commun. 14::232
     [Crossref] [Google Scholar]
  74. 74.
    Haghani A, Li CZ, Robeck TR, Zhang J, Lu AT, et al. 2023.. DNA methylation networks underlying mammalian traits. . Science 381:(6658):eabq5693
     [Crossref] [Google Scholar]
  75. 75.
    Chen S, Liu S, Shi S, Yin H, Tang Y, et al. 2024.. Cross-species comparative DNA methylation reveals novel insights into complex traits genetics among cattle, sheep and goats. . Mo. Biol. Evol. 41:(2):msae003
     [Crossref] [Google Scholar]
  76. 76.
    Lu AT, Fei Z, Haghani A, Robeck TR, Zoller JA, et al. 2023.. Universal DNA methylation age across mammalian tissues. . Nat. Aging 3:(9):1144–66
     [Crossref] [Google Scholar]
  77. 77.
    Yu M, Ren B. 2017.. The three-dimensional organization of mammalian genomes. . Annu. Rev. Cell Dev. Biol. 33::265–89
     [Crossref] [Google Scholar]
  78. 78.
    Avdeyev P, Zhou J. 2022.. Computational approaches for understanding sequence variation effects on the 3D genome architecture. . Annu. Rev. Biomed. Data Sci. 5::183–204
     [Crossref] [Google Scholar]
  79. 79.
    Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, et al. 2009.. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. . Science 326:(5950):289–93
     [Crossref] [Google Scholar]
  80. 80.
    Dixon JR, Jung I, Selvaraj S, Shen Y, Antosiewicz-Bourget JE, et al. 2015.. Chromatin architecture reorganization during stem cell differentiation. . Nature 518:(7539):331–36
     [Crossref] [Google Scholar]
  81. 81.
    Liao Y, Zhang X, Chakraborty M, Emerson JJ. 2021.. Topologically associating domains and their role in the evolution of genome structure and function in Drosophila. . Genome Res. 31:(3):397–410
     [Crossref] [Google Scholar]
  82. 82.
    Rao SSP, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, et al. 2014.. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. . Cell 159:(7):1665–80
     [Crossref] [Google Scholar]
  83. 83.
    Corbo M, Damas J, Bursell MG, Lewin HA. 2022.. Conservation of chromatin conformation in carnivores. . PNAS 119:(9):e2120555119
     [Crossref] [Google Scholar]
  84. 84.
    Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, et al. 2012.. Topological domains in mammalian genomes identified by analysis of chromatin interactions. . Nature 485:(7398):376–80
     [Crossref] [Google Scholar]
  85. 85.
    Cardoso-Moreira M, Halbert J, Valloton D, Velten B, Chen C, et al. 2019.. Gene expression across mammalian organ development. . Nature 571:(7766):505–9
     [Crossref] [Google Scholar]
  86. 86.
    Sepp M, Leiss K, Murat F, Okonechnikov K, Joshi P, et al. 2023.. Cellular development and evolution of the mammalian cerebellum. . Nature 625::788–96
     [Crossref] [Google Scholar]
  87. 87.
    Gorkin DU, Barozzi I, Zhao Y, Zhang Y, Huang H, et al. 2020.. An atlas of dynamic chromatin landscapes in mouse fetal development. . Nature 583:(7818):744–51
     [Crossref] [Google Scholar]
  88. 88.
    van der Velde A, Fan K, Tsuji J, Moore JE, Purcaro MJ, et al. 2021.. Annotation of chromatin states in 66 complete mouse epigenomes during development. . Commun. Biol. 4::239
     [Crossref] [Google Scholar]
  89. 89.
    Domcke S, Hill AJ, Daza RM, Cao J, O'Day DR, et al. 2020.. A human cell atlas of fetal chromatin accessibility. . Science 370:(6518):eaba7612
     [Crossref] [Google Scholar]
  90. 90.
    Claussnitzer M, Dankel SN, Klocke B, Grallert H, Glunk V, et al. 2014.. Leveraging cross-species transcription factor binding site patterns: from diabetes risk loci to disease mechanisms. . Cell 156:(1):343–58
     [Crossref] [Google Scholar]
  91. 91.
    Albert FW, Kruglyak L. 2015.. The role of regulatory variation in complex traits and disease. . Nat. Rev. Genet. 16:(4):197–212
     [Crossref] [Google Scholar]
  92. 92.
    Aguet F, Alasoo K, Li YI, Battle A, Im HK, et al. 2023.. Molecular quantitative trait loci. . Nat. Rev. Methods Primers 3::4
     [Crossref] [Google Scholar]
  93. 93.
    GTEx Consort. 2020.. The GTEx Consortium atlas of genetic regulatory effects across human tissues. . Science 369:(6509):1318–30
     [Crossref] [Google Scholar]
  94. 94.
    Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, et al. 2021.. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. . Nat. Genet. 53:(9):1300–10
     [Crossref] [Google Scholar]
  95. 95.
    Kerimov N, Hayhurst JD, Peikova K, Manning JR, Walter P, et al. 2021.. A compendium of uniformly processed human gene expression and splicing quantitative trait loci. . Nat. Genet. 53:(9):1290–99
     [Crossref] [Google Scholar]
  96. 96.
    Huang D, Feng X, Yang H, Wang J, Zhang W, et al. 2023.. QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes. . Nucleic Acids Res. 51:(D1):D1122–28
     [Crossref] [Google Scholar]
  97. 97.
    Kerimov N, Tambets R, Hayhurst JD, Rahu I, Kolberg P, et al. 2023.. eQTL Catalogue 2023: new datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs. . PLOS Genet. 19:(9):e1010932
     [Crossref] [Google Scholar]
  98. 98.
    Pallares LF, Melo D, Wolf S, Cofer EM, Abhyankar V, et al. 2023.. Saturating the eQTL map in Drosophila melanogaster: genome-wide patterns of cis and trans regulation of transcriptional variation in outbred populations. . bioRxiv 541576. https://www.biorxiv.org/content/10.1101/2023.05.20.541576v3
  99. 99.
    Munro D, Wang T, Chitre AS, Polesskaya O, Ehsan N, et al. 2022.. The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats. . Nucleic Acids. Res. 50:(19):10882–95
     [Crossref] [Google Scholar]
  100. 100.
    Philip VM, He H, Saul MC, Dickson PE, Bubier JA, Chesler EJ. 2023.. Gene expression genetics of the striatum of Diversity Outbred mice. . Sci. Data 10::522
     [Crossref] [Google Scholar]
  101. 101.
    Jasinska AJ, Zelaya I, Service SK, Peterson CB, Cantor RM, et al. 2017.. Genetic variation and gene expression across multiple tissues and developmental stages in a non-human primate. . Nat. Genet. 49:(12):1714–21
     [Crossref] [Google Scholar]
  102. 102.
    Fair BJ, Blake LE, Sarkar A, Pavlovic BJ, Cuevas C, Gilad Y. 2020.. Gene expression variability in human and chimpanzee populations share common determinants. . eLife 9::e59929
     [Crossref] [Google Scholar]
  103. 103.
    Lin W, Wall JD, Li G, Newman D, Yang Y, et al. 2023.. Genetic regulatory effects in response to a high cholesterol, high fat diet in baboons. . bioRxiv 551489. https://doi.org/1101/2023.08.01.551489
  104. 104.
    Nat. Genet. 2022.. The CattleGTEx atlas reveals regulatory mechanisms underlying complex traits. . 54(9):1273–74
  105. 105.
    Liu S, Gao Y, Canela-Xandri O, Wang S, Yu Y, et al. 2022.. A multi-tissue atlas of regulatory variants in cattle. . Nat. Genet. 54:(9):1438–47
     [Crossref] [Google Scholar]
  106. 106.
    Teng J, Gao Y, Yin H, Bai Z, Liu S, et al. 2024.. A compendium of genetic regulatory effects across pig tissues. . Nat. Genet. 56:(1):112–23
     [Crossref] [Google Scholar]
  107. 107.
    Guan D, Bai Z, Zhu X, Zhong C, Hou Y, et al. The ChickenGTEx pilot analysis: a reference of regulatory variants across 28 chicken tissues. . bioRxiv 546670. https://doi.org/10.1101/2023.06.27.546670v1
  108. 108.
    Yazar S, Alquicira-Hernandez J, Wing K, Senabouth A, Gordon MG, et al. 2022.. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. . Science 376:(6589):eabf3041
     [Crossref] [Google Scholar]
  109. 109.
    Soskic B, Cano-Gamez E, Smyth DJ, Ambridge K, Ke Z, et al. 2022.. Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation. . Nat. Genet. 54:(6):817–26
     [Crossref] [Google Scholar]
  110. 110.
    Schmiedel BJ, Gonzalez-Colin C, Fajardo V, Rocha J, Madrigal A, et al. 2022.. Single-cell eQTL analysis of activated T cell subsets reveals activation and cell type-dependent effects of disease-risk variants. . Sci. Immunol. 7:(68):eabm2508
     [Crossref] [Google Scholar]
  111. 111.
    Kumasaka N, Rostom R, Huang N, Polanski K, Meyer KB, et al. 2023.. Mapping interindividual dynamics of innate immune response at single-cell resolution. . Nat. Genet. 55:(6):1066–75
     [Crossref] [Google Scholar]
  112. 112.
    van der Wijst MGP, de Vries DH, Groot HE, Trynka G, Hon CC, et al. 2020.. The single-cell eQTLGen consortium. . eLife 9::e52155
     [Crossref] [Google Scholar]
  113. 113.
    IGVF Consort. 2023.. The Impact of Genomic Variation on Function (IGVF) Consortium. . ArXiv 13708. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402186/
  114. 114.
    Umans BD, Battle A, Gilad Y. 2021.. Where are the disease-associated eQTLs?. Trends Genet. 37:(2):109–24
     [Crossref] [Google Scholar]
  115. 115.
    Natl. Advis. Child Health Hum. Dev. Counc. 2019.. 201910 Developmental Genotype-Tissue Expression (dGTEx) Project. https://www.nichd.nih.gov/about/advisory/council/archive/201910/dGTEx-PPB-201910
    [Google Scholar]
  116. 116.
    Tung J, Zhou X, Alberts SC, Stephens M, Gilad Y. 2015.. The genetic architecture of gene expression levels in wild baboons. . eLife 4::e04729
     [Crossref] [Google Scholar]
  117. 117.
    Zhao R, Talenti A, Fang L, Liu S, Liu G, et al. 2022.. The conservation of human functional variants and their effects across livestock species. . Commun. Biol. 5::1003
     [Crossref] [Google Scholar]
  118. 118.
    Santhanam N, Sanchez-Roige S, Liang Y, Chitre AS, Munro D, et al. 2023.. RatXcan: framework for translating genetic results between species via transcriptome-wide association analyses. . bioRxiv 494719. https://www.biorxiv.org/content/10.1101/2022.06.03.494719v4
  119. 119.
    Yang Z, Zeng X, Zhao Y, Chen R. 2023.. AlphaFold2 and its applications in the fields of biology and medicine. . Signal Transduct. Target Ther. 8::115
     [Crossref] [Google Scholar]
  120. 120.
    Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, et al. 2018.. A universal SNP and small-indel variant caller using deep neural networks. . Nat. Biotechnol. 36:(10):983–87
     [Crossref] [Google Scholar]
  121. 121.
    Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A. 2019.. A primer on deep learning in genomics. . Nat. Genet. 51::12–18
     [Crossref] [Google Scholar]
  122. 122.
    Eraslan G, Avsec Ž, Gagneur J, Theis FJ. 2019.. Deep learning: new computational modelling techniques for genomics. . Nat. Rev. Genet. 20:(7):389–403
     [Crossref] [Google Scholar]
  123. 123.
    Sapoval N, Aghazadeh A, Nute MG, Antunes DA, Balaji A, et al. 2022.. Current progress and open challenges for applying deep learning across the biosciences. . Nat. Commun. 13::1728
     [Crossref] [Google Scholar]
  124. 124.
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, et al. 2021.. Highly accurate protein structure prediction with AlphaFold. . Nature 596:(7873):583–89
     [Crossref] [Google Scholar]
  125. 125.
    Zhou J, Troyanskaya OG. 2015.. Predicting effects of noncoding variants with deep learning-based sequence model. . Nat. Methods 12:(10):931–34
     [Crossref] [Google Scholar]
  126. 126.
    Routhier E, Mozziconacci J. 2022.. Genomics enters the deep learning era. . PeerJ 10::e13613
     [Crossref] [Google Scholar]
  127. 127.
    Alharbi WS, Rashid M. 2022.. A review of deep learning applications in human genomics using next-generation sequencing data. . Hum. Genom. 16::26
     [Crossref] [Google Scholar]
  128. 128.
    Khodabandelou G, Routhier E, Mozziconacci J. 2020.. Genome annotation across species using deep convolutional neural networks. . PeerJ Comput. Sci. 6::e278
     [Crossref] [Google Scholar]
  129. 129.
    Kelley DR. 2020.. Cross-species regulatory sequence activity prediction. . PLOS Comput. Biol. 16:(7):e1008050
     [Crossref] [Google Scholar]
  130. 130.
    Cochran K, Srivastava D, Shrikumar A, Balsubramani A, Hardison RC, et al. 2022.. Domain-adaptive neural networks improve cross-species prediction of transcription factor binding. . Genome Res. 32:(3):512–23
     [Crossref] [Google Scholar]
  131. 131.
    Kwon SB, Ernst J. 2021.. Learning a genome-wide score of human-mouse conservation at the functional genomics level. . Nat. Commun. 12::2495
     [Crossref] [Google Scholar]
  132. 132.
    Li J, Zhao T, Guan D, Pan Z, Bai Z, et al. 2023.. Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits. . Cell Genom. 3:(10):100390
     [Crossref] [Google Scholar]
  133. 133.
    Bordeira-Carriço R, Teixeira J, Duque M, Galhardo M, Ribeiro D, et al. 2022.. Multidimensional chromatin profiling of zebrafish pancreas to uncover and investigate disease-relevant enhancers. . Nat. Commun. 13::1945
     [Crossref] [Google Scholar]
  134. 134.
    Pennacchio LA, Visel A. 2010.. Limits of sequence and functional conservation. . Nat. Genet. 42:(7):557–58
     [Crossref] [Google Scholar]
  135. 135.
    Toneyan S, Tang Z, Koo PK. 2022.. Evaluating deep learning for predicting epigenomic profiles. . Nat. Mach. Intell. 4:(12):1088–100
     [Crossref] [Google Scholar]
  136. 136.
    Novakovsky G, Dexter N, Libbrecht MW, Wasserman WW, Mostafavi S. 2023.. Obtaining genetics insights from deep learning via explainable artificial intelligence. . Nat. Rev. Genet. 24:(2):125–37
     [Crossref] [Google Scholar]
  137. 137.
    Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. 2015.. The technology and biology of single-cell RNA sequencing. . Mol. Cell 58:(4):610–20
     [Crossref] [Google Scholar]
  138. 138.
    Healey HM, Bassham S, Cresko WA. 2022.. Single-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis. . Genetics 220:(3):iyac017
     [Crossref] [Google Scholar]
  139. 139.
    van der Wijst MGP, Brugge H, de Vries DH, Deelen P, Swertz MA, et al. 2018.. Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. . Nat. Genet. 50:(4):493–97
     [Crossref] [Google Scholar]
  140. 140.
    Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. 2023.. Single-cell genomics meets human genetics. . Nat. Rev. Genet. 24:(8):535–49
     [Crossref] [Google Scholar]
  141. 141.
    Yarlagadda S, Giorgio TD. 2024.. A guide to single-cell RNA sequencing analysis using web-based tools for non-bioinformatician. . FEBS J. 291:(12):2545–61
     [Crossref] [Google Scholar]
  142. 142.
    Zhang S, Li X, Lin J, Lin Q, Wong KC. 2023.. Review of single-cell RNA-seq data clustering for cell-type identification and characterization. . RNA 29:(5):517–30
     [Crossref] [Google Scholar]
  143. 143.
    Ma P, Fang P, Ren T, Fang L, Xiao S. 2022.. Porcine intestinal organoids: overview of the state of the art. . Viruses 14:(5):1110
     [Crossref] [Google Scholar]
  144. 144.
    Yin Y, Liu PY, Shi Y, Li P. 2021.. Single-cell sequencing and organoids: a powerful combination for modelling organ development and diseases. . Rev. Physiol. Biochem. Pharmacol. 179::189–210
     [Crossref] [Google Scholar]
  145. 145.
    Wang J, Gao M, Cheng M, Luo J, Lu M, et al. 2024.. Single-cell transcriptional analysis of lamina propria lymphocytes in the jejunum reveals innate lymphoid cell-like cells in pigs. . J. Immunol. 212:(1):130–42
     [Crossref] [Google Scholar]
  146. 146.
    Li T, Morselli M, Su T, Million M, Larauche M, et al. 2023.. Comparative transcriptomics reveals highly conserved regional programs between porcine and human colonic enteric nervous system. . Commun. Biol. 6::98
     [Crossref] [Google Scholar]
  147. 147.
    Wang L, Gao P, Li C, Liu Q, Yao Z, et al. 2023.. A single-cell atlas of bovine skeletal muscle reveals mechanisms regulating intramuscular adipogenesis and fibrogenesis. . J. Cachexia Sarcopenia Muscle 14:(5):2152–67
     [Crossref] [Google Scholar]
  148. 148.
    Cai C, Wan P, Wang H, Cai X, Wang J, et al. 2023.. Transcriptional and open chromatin analysis of bovine skeletal muscle development by single-cell sequencing. . Cell Prolif. 56:(9):e13430
     [Crossref] [Google Scholar]
  149. 149.
    Maxwell M, Söderlund R, Härtle S, Wattrang E. 2024.. Single-cell RNA-seq mapping of chicken peripheral blood leukocytes. . BMC Genom. 25::124
     [Crossref] [Google Scholar]
  150. 150.
    Chen G, Chen J, Qi L, Yin Y, Lin Z, et al. 2024.. Bulk and single-cell alternative splicing analyses reveal roles of TRA2B in myogenic differentiation. . Cell Prolif. 57:(2):e13545
     [Crossref] [Google Scholar]
  151. 151.
    Lyu P, Hoang T, Santiago CP, Thomas ED, Timms AE, et al. 2021.. Gene regulatory networks controlling temporal patterning, neurogenesis, and cell-fate specification in mammalian retina. . Cell Rep. 37:(7):109994
     [Crossref] [Google Scholar]
  152. 152.
    Preissl S, Gaulton KJ, Ren B. 2023.. Characterizing cis-regulatory elements using single-cell epigenomics. . Nat. Rev. Genet. 24:(1):21–43
     [Crossref] [Google Scholar]
  153. 153.
    Gao Y, Li J, Cai G, Wang Y, Yang W, et al. 2022.. Single-cell transcriptomic and chromatin accessibility analyses of dairy cattle peripheral blood mononuclear cells and their responses to lipopolysaccharide. . BMC Genom. 23::338
     [Crossref] [Google Scholar]
  154. 154.
    Yang P, Corbett R, Daharsh L, Uribe JH, Byrne KA, et al. 2024.. Definition of regulatory elements and transcription factors controlling porcine immune cell gene expression at single cell resolution using single nucleus ATAC-seq. . Genomics 116::110944
     [Crossref] [Google Scholar]
  155. 155.
    Forcato M, Romano O, Bicciato S. 2021.. Computational methods for the integrative analysis of single-cell data. . Brief. Bioinform. 22:(3):bbaa042
     [Crossref] [Google Scholar]
  156. 156.
    Kim D, Tran A, Kim HJ, Lin Y, Yang JYH, Yang P. 2023.. Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data. . npj Syst. Biol. Appl. 9::51
     [Crossref] [Google Scholar]
  157. 157.
    Tangeman JA, Rebull SM, Grajales-Esquivel E, Weaver JM, Bendezu-Sayas S, et al. 2024.. Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology. . Development 151:(1):dev202249
     [Crossref] [Google Scholar]
  158. 158.
    Bao W, Fan H, Xu C, Du C, Wang H, et al. 2022.. Single-cell transcriptomic and chromatin accessibility atlas of peripheral blood mononuclear cells reveal the immune cell heterogeneity of pigs. . Res. Square. https://www.researchsquare.com/article/rs-1887867/v1
    [Google Scholar]
  159. 159.
    Sheng X, Guan Y, Ma Z, Wu J, Liu H, et al. 2021.. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. . Nat. Genet. 53:(9):1322–33
     [Crossref] [Google Scholar]
  160. 160.
    Hu T, Chitnis N, Monos D, Dinh A. 2021.. Next-generation sequencing technologies: an overview. . Hum. Immunol. 82:(11):801–11
     [Crossref] [Google Scholar]
  161. 161.
    Razaghi R. 2022.. Using nanopore sequencing to interrogate the genome and epigenome. PhD Diss ., Johns Hopkins Univ., Baltimore:. http://jhir.library.jhu.edu/handle/1774.2/68007
    [Google Scholar]
  162. 162.
    Shumate A, Wong B, Pertea G, Pertea M. 2022.. Improved transcriptome assembly using a hybrid of long and short reads with StringTie. . PLOS Comput. Biol. 18:(6):e1009730
     [Crossref] [Google Scholar]
  163. 163.
    Vlasova-St. Louis I. 2021.. Applications of RNA-Seq in Biology and Medicine. London:: InTechOpen
     [Google Scholar]
  164. 164.
    Oikonomopoulos S, Bayega A, Fahiminiya S, Djambazian H, Berube P, Ragoussis J. 2020.. Methodologies for transcript profiling using long-read technologies. . Front. Genet. 11::606
     [Crossref] [Google Scholar]
  165. 165.
    Benegas G, Fischer J, Song YS. 2022.. Robust and annotation-free analysis of alternative splicing across diverse cell types in mice. . eLife 11::e73520
     [Crossref] [Google Scholar]
  166. 166.
    Cardona-Alberich A, Tourbez M, Pearce SF, Sibley CR. 2021.. Elucidating the cellular dynamics of the brain with single-cell RNA sequencing. . RNA Biol. 18:(7):1063–84
     [Crossref] [Google Scholar]
  167. 167.
    Wen L, Tang F. 2022.. Recent advances in single-cell sequencing technologies. . Precis. Clin. Med. 5:(1):pbac002
     [Crossref] [Google Scholar]
  168. 168.
    Yuan CU, Quah FX, Hemberg M. 2024.. Single-cell and spatial transcriptomics: bridging current technologies with long-read sequencing. . Mol. Aspects Med. 96::101255
     [Crossref] [Google Scholar]
  169. 169.
    Lebrigand K, Bergenstråhle J, Thrane K, Mollbrink A, Meletis K, et al. 2023.. The spatial landscape of gene expression isoforms in tissue sections. . Nucleic Acids. Res. 51:(8):e47
     [Crossref] [Google Scholar]
  170. 170.
    Inoue F, Kreimer A, Ashuach T, Ahituv N, Yosef N. 2019.. Identification and massively parallel characterization of regulatory elements driving neural induction. . Cell Stem Cell 25:(5):713–27.e10
     [Crossref] [Google Scholar]
  171. 171.
    Dong S, Boyle AP. 2022.. Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome. . Nucleic Acids Res. 50:(1):e6
     [Crossref] [Google Scholar]
  172. 172.
    Findlay SD, Romo L, Burge CB. 2024.. Quantifying negative selection in human 3′ UTRs uncovers constrained targets of RNA-binding proteins. . Nat. Commun. 15::85
     [Crossref] [Google Scholar]
  173. 173.
    Gallego Romero I, Lea AJ. 2023.. Leveraging massively parallel reporter assays for evolutionary questions. . Genome Biol. 24::26
     [Crossref] [Google Scholar]
  174. 174.
    Pang B, van Weerd JH, Hamoen FL, Snyder MP. 2022.. Identification of non-coding silencer elements and their regulation of gene expression. . Nat. Rev. Mol. Cell Biol. 24::383–95
     [Crossref] [Google Scholar]
  175. 175.
    Zhao S, Hong CKY, Myers CA, Granas DM, White MA, et al. 2023.. A single-cell massively parallel reporter assay detects cell-type-specific gene regulation. . Nat. Genet. 55:(2):346–54
     [Crossref] [Google Scholar]
  176. 176.
    Koido M, Hon CC, Koyama S, Kawaji H, Murakawa Y, et al. 2023.. Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning. . Nat. Biomed. Eng. 7:(6):830–44
     [Crossref] [Google Scholar]
  177. 177.
    Bravo González-Blas C, Matetovici I, Hillen H, Taskiran II, Vandepoel R, et al. 2024.. Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation. . Nat. Cell Biol. 26:(1):153–67
     [Crossref] [Google Scholar]
  178. 178.
    Örd T, Örd D, Adler P, Örd T. 2023.. Genome-wide census of ATF4 binding sites and functional profiling of trait-associated genetic variants overlapping ATF4 binding motifs. . PLOS Genet. 19:(10):e1011014
     [Crossref] [Google Scholar]
  179. 179.
    Martella A, Fisher DI. 2021.. Regulation of gene expression and the elucidative role of CRISPR-based epigenetic modifiers and CRISPR-induced chromosome conformational changes. . CRISPR J. 4:(1):43–57
     [Crossref] [Google Scholar]
  180. 180.
    Dixit A, Parnas O, Li B, Chen J, Fulco CP, et al. 2016.. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. . Cell 167:(7):1853–66.e17
     [Crossref] [Google Scholar]
  181. 181.
    Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H, et al. 2016.. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. . Cell 167:(7):1883–96.e15
     [Crossref] [Google Scholar]
  182. 182.
    Borys SM, Younger ST. 2020.. Identification of functional regulatory elements in the human genome using pooled CRISPR screens. . BMC Genom. 21::107
     [Crossref] [Google Scholar]
  183. 183.
    Nuñez JK, Chen J, Pommier GC, Cogan JZ, Replogle JM, et al. 2021.. Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing. . Cell 184:(9):2503–19.e17
     [Crossref] [Google Scholar]
  184. 184.
    Policarpi C, Munafò M, Tsagkris S, Carlini V, Hackett JA. 2024.. Systematic epigenome editing captures the context-dependent instructive function of chromatin modifications. . Synth. Biol. 56::1168–80
     [Google Scholar]
  185. 185.
    Lin L, DeMartino J, Wang D, Van Son GJF, Van Der Linden R, et al. 2023.. Unbiased transcription factor CRISPR screen identifies ZNF800 as master repressor of enteroendocrine differentiation. . Science 382:(6669):451–58
     [Crossref] [Google Scholar]
  186. 186.
    Hansen SL, Larsen HL, Pikkupeura LM, Maciag G, Guiu J, et al. 2023.. An organoid-based CRISPR-Cas9 screen for regulators of intestinal epithelial maturation and cell fate. . Sci. Adv. 9:(28):eadg4055
     [Crossref] [Google Scholar]
  187. 187.
    Liang J, Wei J, Cao J, Qian J, Gao R, et al. 2023.. In-organoid single-cell CRISPR screening reveals determinants of hepatocyte differentiation and maturation. . Genome Biol. 24::251
     [Crossref] [Google Scholar]
  188. 188.
    Kawasaki M, Goyama T, Tachibana Y, Nagao I, Ambrosini YM. 2022.. Farm and companion animal organoid models in translational research: a powerful tool to bridge the gap between mice and humans. . Front. Med. Technol. 4::895379
     [Crossref] [Google Scholar]
  189. 189.
    Liu J, Wei X, Zhang Y, Ran Y, Qu B, et al. 2024.. dCas9-guided demethylation of the AKT1 promoter improves milk protein synthesis in a bovine mastitis mammary gland epithelial model induced by using Staphylococcus aureus. . Cell Biol. Int. 48:(3):300–10
     [Crossref] [Google Scholar]
  190. 190.
    Dehler CE, Lester K, Della Pelle G, Jouneau L, Houel A, et al. 2019.. Viral resistance and IFN signaling in STAT2 knockout fish cells. . J. Immunol. 203:(2):465–75
     [Crossref] [Google Scholar]
  191. 191.
    Verdile N, Camin F, Pavlovic R, Pasquariello R, Stuknytė M, et al. 2023.. Distinct organotypic platforms modulate rainbow trout (Oncorhynchus mykiss) intestinal cell differentiation in vitro. . Cells 12:(14):1843
     [Crossref] [Google Scholar]
  192. 192.
    Clark EL, Archibald AL, Daetwyler HD, Groenen MAM, Harrison PW, et al. 2020.. From FAANG to fork: application of highly annotated genomes to improve farmed animal production. . Genome Biol. 21::285
     [Crossref] [Google Scholar]
  193. 193.
    Lampe GD, King RT, Halpin-Healy TS, Klompe SE, Hogan MI, et al. 2024.. Targeted DNA integration in human cells without double-strand breaks using CRISPR-associated transposases. . Nat. Biotechnol. 42:(1):87–98
     [Crossref] [Google Scholar]
  194. 194.
    Thompson D, Regev A, Roy S. 2015.. Comparative analysis of gene regulatory networks: from network reconstruction to evolution. . Annu. Rev. Cell Dev. Biol. 31::399–428
     [Crossref] [Google Scholar]
  195. 195.
    Johnston I, Kent M, Pierre B, Looseley M, Bargelloni L, et al. 2024.. Advancing fish breeding in aquaculture through genome functional annotation. . Aquaculture 583::740589
     [Crossref] [Google Scholar]
  196. 196.
    Hum. Cell Atlas Stand. Technol. Work. Group, Rozenblatt-Rosen O, Shin JW, Rood JE, Hupalowska A, et al. 2021.. Building a high-quality Human Cell Atlas. . Nat. Biotechnol. 39:(2):149–53
     [Crossref] [Google Scholar]
  197. 197.
    Kapoor M, Tuggle CK, Burdett T, Tickle T, Harrison P, et al. 2023.. PSII-6 computational tools and resources for analysis and exploration of single-cell Rnaseq data in agriculture. . J. Anim. Sci. 101:(Suppl. 2):267–68
     [Crossref] [Google Scholar]
  198. 198.
    Tekman M, Batut B, Ostrovsky A, Antoniewski C, Clements D, et al. 2020.. A single-cell RNA-sequencing training and analysis suite using the Galaxy framework. . GigaScience 9:(10):giaa102
     [Crossref] [Google Scholar]
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Comparative Genomics and Epigenomics of Transcriptional Regulation
Annual Review of Animal Biosciences 13, 73 (2025); https://doi.org/10.1146/annurev-animal-111523-102217
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