Analyze Diet
PLoS genetics2023; 19(3); e1010468; doi: 10.1371/journal.pgen.1010468

Functional annotation of the animal genomes: An integrated annotation resource for the horse.

Abstract: The genomic sequence of the horse has been available since 2009, providing critical resources for discovering important genomic variants regarding both animal health and population structures. However, to fully understand the functional implications of these variants, detailed annotation of the horse genome is required. Due to the limited availability of functional data for the equine genome, as well as the technical limitations of short-read RNA-seq, existing annotation of the equine genome contains limited information about important aspects of gene regulation, such as alternate isoforms and regulatory elements, which are either not transcribed or transcribed at a very low level. To solve above problems, the Functional Annotation of the Animal Genomes (FAANG) project proposed a systemic approach to tissue collection, phenotyping, and data generation, adopting the blueprint laid out by the Encyclopedia of DNA Elements (ENCODE) project. Here we detail the first comprehensive overview of gene expression and regulation in the horse, presenting 39,625 novel transcripts, 84,613 candidate cis-regulatory elements (CRE) and their target genes, 332,115 open chromatin regions genome wide across a diverse set of tissues. We showed substantial concordance between chromatin accessibility, chromatin states in different genic features and gene expression. This comprehensive and expanded set of genomics resources will provide the equine research community ample opportunities for studies of complex traits in the horse.
Publication Date: 2023-03-02 PubMed ID: 36862752PubMed Central: PMC10013926DOI: 10.1371/journal.pgen.1010468Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
  • Review
  • Journal Article
  • Research Support
  • U.S. Gov't
  • Non-P.H.S.
  • Research Support
  • Non-U.S. Gov't

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article is about a comprehensive study done on the horse genome, presenting new findings about gene expression and regulation, which were achieved using a systemic approach proposed by the Functional Annotation of the Animal Genomes (FAANG) project.

Introduction

  • The research focuses on the thorough annotation of the horse genome. While the entire genomic sequence of the horse has been known since 2009, understanding the full implications of its genetic variants has been a challenge due to limited functional data.
  • Solving this deficit in information requires the detailed annotation of the horse genome, particularly understanding gene regulation aspects such as alternate isoforms and regulatory elements – something that traditional methods were not able to achieve.

The FAANG Project

  • To address these limitations, this research incorporated the methodologies from the Functional Annotation of the Animal Genomes (FAANG) project, a system that presents a new way of tissue collection, phenotyping, and data generation.
  • The FAANG approach was inspired by the Encyclopedia of DNA Elements (ENCODE) project, showing its solid foundation in recognized research.

Results

  • As a result of this research, for the first time, a comprehensive overview of gene expression and regulation in the horse is presented. This includes the discovery of 39,625 novel transcripts, which are unique sequences of RNA molecules that indicate the presence of genetic information.
  • A major highlight of the study is the identification of 84,613 candidate cis-regulatory elements (CRE) and their target genes, which play a critical role in controlling the transcription of nearby genes.
  • The study also identified 332,115 open chromatin regions across various tissues. The openness or closeness of these chromatin regions usually affects gene regulation, providing essential clues about the genomic functioning.
  • The research also established significant concordance between chromatin accessibility, chromatin states in different genic features, and gene expression, offering a deep dive into genomic analysis previously not available for the horse.

Conclusion

  • The findings from this research provide a comprehensive and expanded set of genomics resources. These resources will indeed serve as a significant contribution to ongoing and future research, allowing for in-depth studies of complex traits in horses.

Cite This Article

APA
Peng S, Dahlgren AR, Donnelly CG, Hales EN, Petersen JL, Bellone RR, Kalbfleisch T, Finno CJ. (2023). Functional annotation of the animal genomes: An integrated annotation resource for the horse. PLoS Genet, 19(3), e1010468. https://doi.org/10.1371/journal.pgen.1010468

Publication

ISSN: 1553-7404
NlmUniqueID: 101239074
Country: United States
Language: English
Volume: 19
Issue: 3
Pages: e1010468
PII: e1010468

Researcher Affiliations

Peng, Sichong
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America.
Dahlgren, Anna R
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America.
Donnelly, Callum G
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America.
Hales, Erin N
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America.
Petersen, Jessica L
  • Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America.
Bellone, Rebecca R
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America.
  • Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America.
Kalbfleisch, Ted
  • Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, United States of America.
Finno, Carrie J
  • Department of Population Health and Reproduction, School of Veterinary Medicine, University of California-Davis, Davis, California, United States of America.

MeSH Terms

  • Horses / genetics
  • Animals
  • Transcriptome
  • Molecular Sequence Annotation
  • Organ Specificity
  • Chromatin
  • Regulatory Elements, Transcriptional
  • Genome
  • Transcription Initiation Site
  • Sequence Analysis, RNA
  • Gene Expression Regulation

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 82 references
  1. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR, Blöcker H, Distl O, Edgar RC, Garber M, Leeb T, Mauceli E, MacLeod JN, Penedo MC, Raison JM, Sharpe T, Vogel J, Andersson L, Antczak DF, Biagi T, Binns MM, Chowdhary BP, Coleman SJ, Della Valle G, Fryc S, Guérin G, Hasegawa T, Hill EW, Jurka J, Kiialainen A, Lindgren G, Liu J, Magnani E, Mickelson JR, Murray J, Nergadze SG, Onofrio R, Pedroni S, Piras MF, Raudsepp T, Rocchi M, Røed KH, Ryder OA, Searle S, Skow L, Swinburne JE, Syvänen AC, Tozaki T, Valberg SJ, Vaudin M, White JR, Zody MC, Lander ES, Lindblad-Toh K. Genome sequence, comparative analysis, and population genetics of the domestic horse.. Science 2009 Nov 6;326(5954):865-7.
    doi: 10.1126/science.1178158pmc: PMC3785132pubmed: 19892987google scholar: lookup
  2. Kalbfleisch TS, Rice ES, DePriest MS Jr, Walenz BP, Hestand MS, Vermeesch JR, O Connell BL, Fiddes IT, Vershinina AO, Saremi NF, Petersen JL, Finno CJ, Bellone RR, McCue ME, Brooks SA, Bailey E, Orlando L, Green RE, Miller DC, Antczak DF, MacLeod JN. Improved reference genome for the domestic horse increases assembly contiguity and composition.. Commun Biol 2018;1:197.
    doi: 10.1038/s42003-018-0199-zpmc: PMC6240028pubmed: 30456315google scholar: lookup
  3. Raudsepp T, Finno CJ, Bellone RR, Petersen JL. Ten years of the horse reference genome: insights into equine biology, domestication and population dynamics in the post-genome era.. Anim Genet 2019 Dec;50(6):569-597.
    doi: 10.1111/age.12857pmc: PMC6825885pubmed: 31568563google scholar: lookup
  4. O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, Astashyn A, Badretdin A, Bao Y, Blinkova O, Brover V, Chetvernin V, Choi J, Cox E, Ermolaeva O, Farrell CM, Goldfarb T, Gupta T, Haft D, Hatcher E, Hlavina W, Joardar VS, Kodali VK, Li W, Maglott D, Masterson P, McGarvey KM, Murphy MR, O'Neill K, Pujar S, Rangwala SH, Rausch D, Riddick LD, Schoch C, Shkeda A, Storz SS, Sun H, Thibaud-Nissen F, Tolstoy I, Tully RE, Vatsan AR, Wallin C, Webb D, Wu W, Landrum MJ, Kimchi A, Tatusova T, DiCuccio M, Kitts P, Murphy TD, Pruitt KD. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.. Nucleic Acids Res 2016 Jan 4;44(D1):D733-45.
    doi: 10.1093/nar/gkv1189pmc: PMC4702849pubmed: 26553804google scholar: lookup
  5. Howe KL, Achuthan P, Allen J, Allen J, Alvarez-Jarreta J, Amode MR, Armean IM, Azov AG, Bennett R, Bhai J, Billis K, Boddu S, Charkhchi M, Cummins C, Da Rin Fioretto L, Davidson C, Dodiya K, El Houdaigui B, Fatima R, Gall A, Garcia Giron C, Grego T, Guijarro-Clarke C, Haggerty L, Hemrom A, Hourlier T, Izuogu OG, Juettemann T, Kaikala V, Kay M, Lavidas I, Le T, Lemos D, Gonzalez Martinez J, Marugán JC, Maurel T, McMahon AC, Mohanan S, Moore B, Muffato M, Oheh DN, Paraschas D, Parker A, Parton A, Prosovetskaia I, Sakthivel MP, Salam AIA, Schmitt BM, Schuilenburg H, Sheppard D, Steed E, Szpak M, Szuba M, Taylor K, Thormann A, Threadgold G, Walts B, Winterbottom A, Chakiachvili M, Chaubal A, De Silva N, Flint B, Frankish A, Hunt SE, IIsley GR, Langridge N, Loveland JE, Martin FJ, Mudge JM, Morales J, Perry E, Ruffier M, Tate J, Thybert D, Trevanion SJ, Cunningham F, Yates AD, Zerbino DR, Flicek P. Ensembl 2021.. Nucleic Acids Res 2021 Jan 8;49(D1):D884-D891.
    doi: 10.1093/nar/gkaa942pmc: PMC7778975pubmed: 33137190google scholar: lookup
  6. Equus caballus RefSeq Annotation Release 103 [Internet]. RefSeq. [cited 2021 Sep 10]. Available from: https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Equus_caballus/103/
  7. Ensembl Genebuild 106.3, Eq쪳.0 [Internet]. 2019. Available from: https://uswest.ensembl.org/Equus_caballus/Info/Annotation
  8. Frankish A, Diekhans M, Ferreira AM, Johnson R, Jungreis I, Loveland J, Mudge JM, Sisu C, Wright J, Armstrong J, Barnes I, Berry A, Bignell A, Carbonell Sala S, Chrast J, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, García Girón C, Gonzalez JM, Grego T, Hardy M, Hourlier T, Hunt T, Izuogu OG, Lagarde J, Martin FJ, Martínez L, Mohanan S, Muir P, Navarro FCP, Parker A, Pei B, Pozo F, Ruffier M, Schmitt BM, Stapleton E, Suner MM, Sycheva I, Uszczynska-Ratajczak B, Xu J, Yates A, Zerbino D, Zhang Y, Aken B, Choudhary JS, Gerstein M, Guigó R, Hubbard TJP, Kellis M, Paten B, Reymond A, Tress ML, Flicek P. GENCODE reference annotation for the human and mouse genomes.. Nucleic Acids Res 2019 Jan 8;47(D1):D766-D773.
    doi: 10.1093/nar/gky955pmc: PMC6323946pubmed: 30357393google scholar: lookup
  9. Roundtree IA, He C. RNA epigenetics--chemical messages for posttranscriptional gene regulation.. Curr Opin Chem Biol 2016 Feb;30:46-51.
    doi: 10.1016/j.cbpa.2015.10.024pmc: PMC4731286pubmed: 26625014google scholar: lookup
  10. Lee TI, Young RA. Transcriptional regulation and its misregulation in disease.. Cell 2013 Mar 14;152(6):1237-51.
    doi: 10.1016/j.cell.2013.02.014pmc: PMC3640494pubmed: 23498934google scholar: lookup
  11. Soukarieh O, Gaildrat P, Hamieh M, Drouet A, Baert-Desurmont S, Frébourg T, Tosi M, Martins A. Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools.. PLoS Genet 2016 Jan;12(1):e1005756.
  12. De Paoli-Iseppi R, Gleeson J, Clark MB. Isoform Age - Splice Isoform Profiling Using Long-Read Technologies.. Front Mol Biosci 2021;8:711733.
    doi: 10.3389/fmolb.2021.711733pmc: PMC8364947pubmed: 34409069google scholar: lookup
  13. Chen SY, Deng F, Jia X, Li C, Lai SJ. A transcriptome atlas of rabbit revealed by PacBio single-molecule long-read sequencing.. Sci Rep 2017 Aug 9;7(1):7648.
    doi: 10.1038/s41598-017-08138-zpmc: PMC5550469pubmed: 28794490google scholar: lookup
  14. Sharon D, Tilgner H, Grubert F, Snyder M. A single-molecule long-read survey of the human transcriptome.. Nat Biotechnol 2013 Nov;31(11):1009-14.
    doi: 10.1038/nbt.2705pmc: PMC4075632pubmed: 24108091google scholar: lookup
  15. Suryamohan K, Krishnankutty SP, Guillory J, Jevit M, Schröder MS, Wu M, Kuriakose B, Mathew OK, Perumal RC, Koludarov I, Goldstein LD, Senger K, Dixon MD, Velayutham D, Vargas D, Chaudhuri S, Muraleedharan M, Goel R, Chen YJ, Ratan A, Liu P, Faherty B, de la Rosa G, Shibata H, Baca M, Sagolla M, Ziai J, Wright GA, Vucic D, Mohan S, Antony A, Stinson J, Kirkpatrick DS, Hannoush RN, Durinck S, Modrusan Z, Stawiski EW, Wiley K, Raudsepp T, Kini RM, Zachariah A, Seshagiri S. The Indian cobra reference genome and transcriptome enables comprehensive identification of venom toxins.. Nat Genet 2020 Jan;52(1):106-117.
    doi: 10.1038/s41588-019-0559-8pmc: PMC8075977pubmed: 31907489google scholar: lookup
  16. Hansen AS, Pustova I, Cattoglio C, Tjian R, Darzacq X. CTCF and cohesin regulate chromatin loop stability with distinct dynamics.. Elife 2017 May 3;6.
    doi: 10.7554/eLife.25776pmc: PMC5446243pubmed: 28467304google scholar: lookup
  17. Stevens TJ, Lando D, Basu S, Atkinson LP, Cao Y, Lee SF, Leeb M, Wohlfahrt KJ, Boucher W, O'Shaughnessy-Kirwan A, Cramard J, Faure AJ, Ralser M, Blanco E, Morey L, Sansó M, Palayret MGS, Lehner B, Di Croce L, Wutz A, Hendrich B, Klenerman D, Laue ED. 3D structures of individual mammalian genomes studied by single-cell Hi-C.. Nature 2017 Apr 6;544(7648):59-64.
    doi: 10.1038/nature21429pmc: PMC5385134pubmed: 28289288google scholar: lookup
  18. Sos BC, Fung HL, Gao DR, Osothprarop TF, Kia A, He MM, Zhang K. Characterization of chromatin accessibility with a transposome hypersensitive sites sequencing (THS-seq) assay.. Genome Biol 2016 Feb 4;17:20.
    doi: 10.1186/s13059-016-0882-7pmc: PMC4743176pubmed: 26846207google scholar: lookup
  19. Liu C, Wang M, Wei X, Wu L, Xu J, Dai X, Xia J, Cheng M, Yuan Y, Zhang P, Li J, Feng T, Chen A, Zhang W, Chen F, Shang Z, Zhang X, Peters BA, Liu L. An ATAC-seq atlas of chromatin accessibility in mouse tissues.. Sci Data 2019 May 20;6(1):65.
    doi: 10.1038/s41597-019-0071-0pmc: PMC6527694pubmed: 31110271google scholar: lookup
  20. Warburton A, Breen G, Rujescu D, Bubb VJ, Quinn JP. Characterization of a REST-Regulated Internal Promoter in the Schizophrenia Genome-Wide Associated Gene MIR137.. Schizophr Bull 2015 May;41(3):698-707.
    doi: 10.1093/schbul/sbu117pmc: PMC4393679pubmed: 25154622google scholar: lookup
  21. Giorgio E, Robyr D, Spielmann M, Ferrero E, Di Gregorio E, Imperiale D, Vaula G, Stamoulis G, Santoni F, Atzori C, Gasparini L, Ferrera D, Canale C, Guipponi M, Pennacchio LA, Antonarakis SE, Brussino A, Brusco A. A large genomic deletion leads to enhancer adoption by the lamin B1 gene: a second path to autosomal dominant adult-onset demyelinating leukodystrophy (ADLD).. Hum Mol Genet 2015 Jun 1;24(11):3143-54.
    doi: 10.1093/hmg/ddv065pmc: PMC4424952pubmed: 25701871google scholar: lookup
  22. Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, Tsai MC, Hung T, Argani P, Rinn JL, Wang Y, Brzoska P, Kong B, Li R, West RB, van de Vijver MJ, Sukumar S, Chang HY. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis.. Nature 2010 Apr 15;464(7291):1071-6.
    pmc: PMC3049919pubmed: 20393566doi: 10.1038/nature08975google scholar: lookup
  23. Jiang C, Pugh BF. Nucleosome positioning and gene regulation: advances through genomics.. Nat Rev Genet 2009 Mar;10(3):161-72.
    doi: 10.1038/nrg2522pmc: PMC4860946pubmed: 19204718google scholar: lookup
  24. Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide.. Curr Protoc Mol Biol 2015 Jan 5;109:21.29.1-21.29.9.
  25. Corces MR, Trevino AE, Hamilton EG, Greenside PG, Sinnott-Armstrong NA, Vesuna S, Satpathy AT, Rubin AJ, Montine KS, Wu B, Kathiria A, Cho SW, Mumbach MR, Carter AC, Kasowski M, Orloff LA, Risca VI, Kundaje A, Khavari PA, Montine TJ, Greenleaf WJ, Chang HY. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues.. Nat Methods 2017 Oct;14(10):959-962.
    doi: 10.1038/nmeth.4396pmc: PMC5623106pubmed: 28846090google scholar: lookup
  26. Halstead MM, Kern C, Saelao P, Chanthavixay G, Wang Y, Delany ME, Zhou H, Ross PJ. Systematic alteration of ATAC-seq for profiling open chromatin in cryopreserved nuclei preparations from livestock tissues.. Sci Rep 2020 Mar 23;10(1):5230.
    doi: 10.1038/s41598-020-61678-9pmc: PMC7089989pubmed: 32251359google scholar: lookup
  27. Peng S, Bellone R, Petersen JL, Kalbfleisch TS, Finno CJ. Successful ATAC-Seq From Snap-Frozen Equine Tissues.. Front Genet 2021;12:641788.
    doi: 10.3389/fgene.2021.641788pmc: PMC8242358pubmed: 34220931google scholar: lookup
  28. Zentner GE, Henikoff S. Regulation of nucleosome dynamics by histone modifications.. Nat Struct Mol Biol 2013 Mar;20(3):259-66.
    doi: 10.1038/nsmb.2470pubmed: 23463310google scholar: lookup
  29. Zhang Y, Sun Z, Jia J, Du T, Zhang N, Tang Y, Fang Y, Fang D. Overview of Histone Modification.. Adv Exp Med Biol 2021;1283:1-16.
    doi: 10.1007/978-981-15-8104-5_1pubmed: 33155134google scholar: lookup
  30. Hyun K, Jeon J, Park K, Kim J. Writing, erasing and reading histone lysine methylations.. Exp Mol Med 2017 Apr 28;49(4):e324.
    doi: 10.1038/emm.2017.11pmc: PMC6130214pubmed: 28450737google scholar: lookup
  31. Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, Barrera LO, Van Calcar S, Qu C, Ching KA, Wang W, Weng Z, Green RD, Crawford GE, Ren B. Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome.. Nat Genet 2007 Mar;39(3):311-8.
    doi: 10.1038/ng1966pubmed: 17277777google scholar: lookup
  32. Santos-Rosa H, Schneider R, Bannister AJ, Sherriff J, Bernstein BE, Emre NC, Schreiber SL, Mellor J, Kouzarides T. Active genes are tri-methylated at K4 of histone H3.. Nature 2002 Sep 26;419(6905):407-11.
    doi: 10.1038/nature01080pubmed: 12353038google scholar: lookup
  33. Lauberth SM, Nakayama T, Wu X, Ferris AL, Tang Z, Hughes SH, Roeder RG. H3K4me3 interactions with TAF3 regulate preinitiation complex assembly and selective gene activation.. Cell 2013 Feb 28;152(5):1021-36.
    doi: 10.1016/j.cell.2013.01.052pmc: PMC3588593pubmed: 23452851google scholar: lookup
  34. Bian C, Xu C, Ruan J, Lee KK, Burke TL, Tempel W, Barsyte D, Li J, Wu M, Zhou BO, Fleharty BE, Paulson A, Allali-Hassani A, Zhou JQ, Mer G, Grant PA, Workman JL, Zang J, Min J. Sgf29 binds histone H3K4me2/3 and is required for SAGA complex recruitment and histone H3 acetylation.. EMBO J 2011 Jun 17;30(14):2829-42.
    pmc: PMC3160252pubmed: 21685874doi: 10.1038/emboj.2011.193google scholar: lookup
  35. Eberl HC, Spruijt CG, Kelstrup CD, Vermeulen M, Mann M. A map of general and specialized chromatin readers in mouse tissues generated by label-free interaction proteomics.. Mol Cell 2013 Jan 24;49(2):368-78.
    doi: 10.1016/j.molcel.2012.10.026pubmed: 23201125google scholar: lookup
  36. Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, Hanna J, Lodato MA, Frampton GM, Sharp PA, Boyer LA, Young RA, Jaenisch R. Histone H3K27ac separates active from poised enhancers and predicts developmental state.. Proc Natl Acad Sci U S A 2010 Dec 14;107(50):21931-6.
    doi: 10.1073/pnas.1016071107pmc: PMC3003124pubmed: 21106759google scholar: lookup
  37. Boyer LA, Plath K, Zeitlinger J, Brambrink T, Medeiros LA, Lee TI, Levine SS, Wernig M, Tajonar A, Ray MK, Bell GW, Otte AP, Vidal M, Gifford DK, Young RA, Jaenisch R. Polycomb complexes repress developmental regulators in murine embryonic stem cells.. Nature 2006 May 18;441(7091):349-53.
    doi: 10.1038/nature04733pubmed: 16625203google scholar: lookup
  38. Burns EN, Bordbari MH, Mienaltowski MJ, Affolter VK, Barro MV, Gianino F, Gianino G, Giulotto E, Kalbfleisch TS, Katzman SA, Lassaline M, Leeb T, Mack M, Müller EJ, MacLeod JN, Ming-Whitfield B, Alanis CR, Raudsepp T, Scott E, Vig S, Zhou H, Petersen JL, Bellone RR, Finno CJ. Generation of an equine biobank to be used for Functional Annotation of Animal Genomes project.. Anim Genet 2018 Dec;49(6):564-570.
    doi: 10.1111/age.12717pmc: PMC6264908pubmed: 30311254google scholar: lookup
  39. Donnelly CG, Bellone RR, Hales EN, Nguyen A, Katzman SA, Dujovne GA, Knickelbein KE, Avila F, Kalbfleisch TS, Giulotto E, Kingsley NB, Tanaka J, Esdaile E, Peng S, Dahlgren A, Fuller A, Mienaltowski MJ, Raudsepp T, Affolter VK, Petersen JL, Finno CJ. Generation of a Biobank From Two Adult Thoroughbred Stallions for the Functional Annotation of Animal Genomes Initiative.. Front Genet 2021;12:650305.
    doi: 10.3389/fgene.2021.650305pmc: PMC7982670pubmed: 33763124google scholar: lookup
  40. Liu T. MACS: Model-based Analysis for ChIP-Seq. 2022.
  41. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based analysis of ChIP-Seq (MACS).. Genome Biol 2008;9(9):R137.
    doi: 10.1186/gb-2008-9-9-r137pmc: PMC2592715pubmed: 18798982google scholar: lookup
  42. Kingsley NB, Kern C, Creppe C, Hales EN, Zhou H, Kalbfleisch TS, MacLeod JN, Petersen JL, Finno CJ, Bellone RR. Functionally Annotating Regulatory Elements in the Equine Genome Using Histone Mark ChIP-Seq.. Genes (Basel) 2019 Dec 18;11(1).
    doi: 10.3390/genes11010003pmc: PMC7017286pubmed: 31861495google scholar: lookup
  43. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression.. Nat Methods 2017 Apr;14(4):417-419.
    doi: 10.1038/nmeth.4197pmc: PMC5600148pubmed: 28263959google scholar: lookup
  44. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.. F1000Res 2015;4:1521.
  45. Zhang Y, Wong CH, Birnbaum RY, Li G, Favaro R, Ngan CY, Lim J, Tai E, Poh HM, Wong E, Mulawadi FH, Sung WK, Nicolis S, Ahituv N, Ruan Y, Wei CL. Chromatin connectivity maps reveal dynamic promoter-enhancer long-range associations.. Nature 2013 Dec 12;504(7479):306-310.
    doi: 10.1038/nature12716pmc: PMC3954713pubmed: 24213634google scholar: lookup
  46. Oti M, Falck J, Huynen MA, Zhou H. CTCF-mediated chromatin loops enclose inducible gene regulatory domains.. BMC Genomics 2016 Mar 22;17:252.
    doi: 10.1186/s12864-016-2516-6pmc: PMC4804521pubmed: 27004515google scholar: lookup
  47. Zwillinger D, Kokoska S. CRC standard probability and statistics tables and formulae. 2000.
  48. Kern C, Wang Y, Xu X, Pan Z, Halstead M, Chanthavixay G, Saelao P, Waters S, Xiang R, Chamberlain A, Korf I, Delany ME, Cheng HH, Medrano JF, Van Eenennaam AL, Tuggle CK, Ernst C, Flicek P, Quon G, Ross P, Zhou H. Functional annotations of three domestic animal genomes provide vital resources for comparative and agricultural research.. Nat Commun 2021 Mar 23;12(1):1821.
    doi: 10.1038/s41467-021-22100-8pmc: PMC7988148pubmed: 33758196google scholar: lookup
  49. Coleman SJ, Zeng Z, Wang K, Luo S, Khrebtukova I, Mienaltowski MJ, Schroth GP, Liu J, MacLeod JN. Structural annotation of equine protein-coding genes determined by mRNA sequencing.. Anim Genet 2010 Dec;41 Suppl 2:121-30.
  50. Hestand MS, Kalbfleisch TS, Coleman SJ, Zeng Z, Liu J, Orlando L, MacLeod JN. Annotation of the Protein Coding Regions of the Equine Genome.. PLoS One 2015;10(6):e0124375.
  51. Mansour TA, Scott EY, Finno CJ, Bellone RR, Mienaltowski MJ, Penedo MC, Ross PJ, Valberg SJ, Murray JD, Brown CT. Tissue resolved, gene structure refined equine transcriptome.. BMC Genomics 2017 Jan 20;18(1):103.
    doi: 10.1186/s12864-016-3451-2pmc: PMC5251313pubmed: 28107812google scholar: lookup
  52. Halstead MM, Kern C, Saelao P, Wang Y, Chanthavixay G, Medrano JF, Van Eenennaam AL, Korf I, Tuggle CK, Ernst CW, Zhou H, Ross PJ. A comparative analysis of chromatin accessibility in cattle, pig, and mouse tissues.. BMC Genomics 2020 Oct 7;21(1):698.
    doi: 10.1186/s12864-020-07078-9pmc: PMC7541309pubmed: 33028202google scholar: lookup
  53. Ko JY, Oh S, Yoo KH. Functional Enhancers As Master Regulators of Tissue-Specific Gene Regulation and Cancer Development.. Mol Cells 2017 Mar;40(3):169-177.
    doi: 10.14348/molcells.2017.0033pmc: PMC5386954pubmed: 28359147google scholar: lookup
  54. Xia H, Dufour CR, Giguère V. ERRα as a Bridge Between Transcription and Function: Role in Liver Metabolism and Disease.. Front Endocrinol (Lausanne) 2019;10:206.
    pmc: PMC6459935pubmed: 31024446doi: 10.3389/fendo.2019.00206google scholar: lookup
  55. Stevanovic M, Drakulic D, Lazic A, Ninkovic DS, Schwirtlich M, Mojsin M. SOX Transcription Factors as Important Regulators of Neuronal and Glial Differentiation During Nervous System Development and Adult Neurogenesis.. Front Mol Neurosci 2021;14:654031.
    doi: 10.3389/fnmol.2021.654031pmc: PMC8044450pubmed: 33867936google scholar: lookup
  56. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, Amin V, Whitaker JW, Schultz MD, Ward LD, Sarkar A, Quon G, Sandstrom RS, Eaton ML, Wu YC, Pfenning AR, Wang X, Claussnitzer M, Liu Y, Coarfa C, Harris RA, Shoresh N, Epstein CB, Gjoneska E, Leung D, Xie W, Hawkins RD, Lister R, Hong C, Gascard P, Mungall AJ, Moore R, Chuah E, Tam A, Canfield TK, Hansen RS, Kaul R, Sabo PJ, Bansal MS, Carles A, Dixon JR, Farh KH, Feizi S, Karlic R, Kim AR, Kulkarni A, Li D, Lowdon R, Elliott G, Mercer TR, Neph SJ, Onuchic V, Polak P, Rajagopal N, Ray P, Sallari RC, Siebenthall KT, Sinnott-Armstrong NA, Stevens M, Thurman RE, Wu J, Zhang B, Zhou X, Beaudet AE, Boyer LA, De Jager PL, Farnham PJ, Fisher SJ, Haussler D, Jones SJ, Li W, Marra MA, McManus MT, Sunyaev S, Thomson JA, Tlsty TD, Tsai LH, Wang W, Waterland RA, Zhang MQ, Chadwick LH, Bernstein BE, Costello JF, Ecker JR, Hirst M, Meissner A, Milosavljevic A, Ren B, Stamatoyannopoulos JA, Wang T, Kellis M. Integrative analysis of 111 reference human epigenomes.. Nature 2015 Feb 19;518(7539):317-30.
    doi: 10.1038/nature14248pmc: PMC4530010pubmed: 25693563google scholar: lookup
  57. Khoury A, Achinger-Kawecka J, Bert SA, Smith GC, French HJ, Luu PL, Peters TJ, Du Q, Parry AJ, Valdes-Mora F, Taberlay PC, Stirzaker C, Statham AL, Clark SJ. Constitutively bound CTCF sites maintain 3D chromatin architecture and long-range epigenetically regulated domains.. Nat Commun 2020 Jan 7;11(1):54.
    doi: 10.1038/s41467-019-13753-7pmc: PMC6946690pubmed: 31911579google scholar: lookup
  58. Kubo N, Ishii H, Xiong X, Bianco S, Meitinger F, Hu R, Hocker JD, Conte M, Gorkin D, Yu M, Li B, Dixon JR, Hu M, Nicodemi M, Zhao H, Ren B. Promoter-proximal CTCF binding promotes distal enhancer-dependent gene activation.. Nat Struct Mol Biol 2021 Feb;28(2):152-161.
    doi: 10.1038/s41594-020-00539-5pmc: PMC7913465pubmed: 33398174google scholar: lookup
  59. Franco MM, Prickett AR, Oakey RJ. The role of CCCTC-binding factor (CTCF) in genomic imprinting, development, and reproduction.. Biol Reprod 2014 Nov;91(5):125.
    doi: 10.1095/biolreprod.114.122945pubmed: 25297545google scholar: lookup
  60. Li H. New strategies to improve minimap2 alignment accuracy.. Bioinformatics 2021 Dec 7;37(23):4572-4574.
  61. Tardaguila M, de la Fuente L, Marti C, Pereira C, Pardo-Palacios FJ, Del Risco H, Ferrell M, Mellado M, Macchietto M, Verheggen K, Edelmann M, Ezkurdia I, Vazquez J, Tress M, Mortazavi A, Martens L, Rodriguez-Navarro S, Moreno-Manzano V, Conesa A. SQANTI: extensive characterization of long-read transcript sequences for quality control in full-length transcriptome identification and quantification.. Genome Res 2018 Feb 9;28(3):396-411.
    pmc: PMC5848618pubmed: 29440222doi: 10.1101/gr.222976.117google scholar: lookup
  62. Reback J, McKinney W, brockmendel J, Bossche JVD, Augspurger T, Cloud P. Pandas 1.1.3. 2020.
  63. Caswell TA, Droettboom M, Lee A, Hunter J, Firing E, Stansby D. matplotlib v3.1.3. 2020.
  64. Waskom M. seaborn: statistical data visualization. JOSS 2021 Apr 6;6(60):3021.
  65. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat İ, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P. SciPy 1.0: fundamental algorithms for scientific computing in Python.. Nat Methods 2020 Mar;17(3):261-272.
    doi: 10.1038/s41592-019-0686-2pmc: PMC7056644pubmed: 32015543google scholar: lookup
  66. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 2011;12(85):2825–30.
  67. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j 2011 May 2;17(1):10.
  68. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010.
  69. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report.. Bioinformatics 2016 Oct 1;32(19):3047-8.
  70. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner.. Bioinformatics 2013 Jan 1;29(1):15-21.
  71. Tarasov A, Vilella AJ, Cuppen E, Nijman IJ, Prins P. Sambamba: fast processing of NGS alignment formats.. Bioinformatics 2015 Jun 15;31(12):2032-4.
  72. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The Sequence Alignment/Map format and SAMtools.. Bioinformatics 2009 Aug 15;25(16):2078-9.
  73. Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dündar F, Manke T. deepTools2: a next generation web server for deep-sequencing data analysis.. Nucleic Acids Res 2016 Jul 8;44(W1):W160-5.
    doi: 10.1093/nar/gkw257pmc: PMC4987876pubmed: 27079975google scholar: lookup
  74. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.. Bioinformatics 2009 Jul 15;25(14):1754-60.
  75. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features.. Bioinformatics 2010 Mar 15;26(6):841-2.
  76. Grandi FC, Modi H, Kampman L, Corces MR. Chromatin accessibility profiling by ATAC-seq.. Nat Protoc 2022 Jun;17(6):1518-1552.
    doi: 10.1038/s41596-022-00692-9pmc: PMC9189070pubmed: 35478247google scholar: lookup
  77. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.. Genome Biol 2014;15(12):550.
    doi: 10.1186/s13059-014-0550-8pmc: PMC4302049pubmed: 25516281google scholar: lookup
  78. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.. Mol Cell 2010 May 28;38(4):576-89.
  79. Mi H, Ebert D, Muruganujan A, Mills C, Albou LP, Mushayamaha T, Thomas PD. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API.. Nucleic Acids Res 2021 Jan 8;49(D1):D394-D403.
    doi: 10.1093/nar/gkaa1106pmc: PMC7778891pubmed: 33290554google scholar: lookup
  80. Barber A. Annotating Gene Expression and Regulatory Elements in Tissues from Healthy Thoroughbred Horses and Identifying Candidate Mutations Associated with Perosomus Elumbis in an Angus Calf. Theses and Dissertations in Animal Science 2022 Apr;233:143.
  81. Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization.. Nat Methods 2012 Feb 28;9(3):215-6.
    doi: 10.1038/nmeth.1906pmc: PMC3577932pubmed: 22373907google scholar: lookup
  82. Bailey TL, Johnson J, Grant CE, Noble WS. The MEME Suite.. Nucleic Acids Res 2015 Jul 1;43(W1):W39-49.
    doi: 10.1093/nar/gkv416pmc: PMC4489269pubmed: 25953851google scholar: lookup

Citations

This article has been cited 1 times.
  1. Foury A, Mach N, Ruet A, Lansade L, Moisan MP. Transcriptomic signature related to poor welfare of sport horses.. Compr Psychoneuroendocrinol 2023 Nov;16:100201.
    doi: 10.1016/j.cpnec.2023.100201pubmed: 37655309google scholar: lookup