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Home / Equine Research Bank / Anim Genet / An application of MeSH enrichment analysis in livestock.
Animal genetics2015; 46(4); 381-387; doi: 10.1111/age.12307

An application of MeSH enrichment analysis in livestock.

Abstract: An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers a comprehensive life science vocabulary including additional categories that are not covered by GO. Although MeSH is applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, MeSH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets were employed for this purpose, representing a gene expression study in dairy cattle, the use of SNPs for genome-wide prediction in swine and the identification of genomic regions targeted by selection in horses. We found that several overrepresented MeSH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, MeSH yielded unique annotations, which are not directly provided by GO terms, suggesting that MeSH has the potential to refine and enrich the representation of biological knowledge. We demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that MeSH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.
© 2015 The Authors. Animal Genetics published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.
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Publication Date: 2015-06-02 PubMed ID: 26036323PubMed Central: PMC5032990DOI: 10.1111/age.12307Google Scholar: Lookup
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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 explores the use of Medical Subject Headings (MeSH) in conjunction with Gene Ontology (GO) for overrepresentation analysis (ORA) in functional genomics studies involving livestock, demonstrating its potential for refining and enriching the representation of biological knowledge.

Research Context – Functional Genomics and Overrepresentation Analysis

  • Functional genomics studies involve researching genes and their functions. They usually include assessing the enrichment of specific biological terms in lists of genes that play significant roles in biological phenomena.
  • The overrepresentation analysis (ORA) is instrumental in these studies. ORA contrasts the observed frequency of annotated terms with the frequencies found in a general background to identify statistically significant overrepresented biological terms.
  • Gene Ontology (GO), a tool typically used in ORA, works by providing consistent classifications and annotations of gene products.

MeSH as an Additional Resource

  • The study explores an alternative to Gene Ontology (GO), called Medical Subject Headings (MeSH), as a tool for conducting ORA in functional genomics studies in livestock.
  • While GO is considered the standard, MeSH offers a comprehensive life science vocabulary which includes categories that GO does not cover.
  • The use of MeSH, however, has predominantly been limited to research on humans and model organisms, but the research explores its potential in livestock genetics.

Study Design and Findings

  • The authors used three different published datasets representing various aspects of livestock genetics – a gene expression study in dairy cattle, the use of SNPs for genome-wide prediction in swine, and the identification of genomic regions targeted by selection in horses.
  • Their goal was to evaluate the capability of MeSH for Overrepresentation Analysis (ORA) in discerning the biological properties of identified genes and contrasting them with the results obtained from GO enrichment analysis.
  • The findings show several overrepresented MeSH annotations linked to the gene sets that share similar concepts with those of GO terms, demonstrating overlap in the biological terms identified by both approaches.
  • Importantly, the study revealed that MeSH provided unique annotations not directly given by GO terms, suggesting its potential to refine and enrich the representation of biological knowledge.

Conclusion and Implications

  • MeSH can indeed be regarded as another valid choice of annotation to infer biological insights from genes identified via experimental analyses.
  • When used alongside GO terms, MeSH has the potential to enhance functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.

Cite This Article

APA
Morota G, Peñagaricano F, Petersen JL, Ciobanu DC, Tsuyuzaki K, Nikaido I. (2015). An application of MeSH enrichment analysis in livestock. Anim Genet, 46(4), 381-387. https://doi.org/10.1111/age.12307

Publication

Animal genetics
ISSN: 1365-2052
NlmUniqueID: 8605704
Country: England
Language: English
Volume: 46
Issue: 4
Pages: 381-387

Researcher Affiliations

Morota, G
  • Department of Animal Science, University of Nebraska, Lincoln, NE, USA.
Peñagaricano, F
  • Department of Animal Sciences, University of Florida, Gainesville, FL, USA.
  • University of Florida Genetics Institute, University of Florida, Gainesville, FL, USA.
Petersen, J L
  • Department of Animal Science, University of Nebraska, Lincoln, NE, USA.
Ciobanu, D C
  • Department of Animal Science, University of Nebraska, Lincoln, NE, USA.
Tsuyuzaki, K
  • Department of Medicinal and Life Science, Faculty of Pharmaceutical Sciences, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, Japan.
  • Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan.
Nikaido, I
  • Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan.

MeSH Terms

  • Animals
  • Cattle / genetics
  • Genomics / methods
  • Horses / genetics
  • Livestock / genetics
  • Medical Subject Headings
  • Molecular Sequence Data
  • Polymorphism, Single Nucleotide
  • Quantitative Trait Loci
  • Swine / genetics
  • Terminology as Topic

References

This article includes 32 references
  1. Adams WT, Skopek TR. Statistical test for the comparison of samples from mutational spectra.. J Mol Biol 1987 Apr 5;194(3):391-6.
    pubmed: 3305960doi: 10.1016/0022-2836(87)90669-3google scholar: lookup
  2. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.. Nat Genet 2000 May;25(1):25-9.
    pmc: PMC3037419pubmed: 10802651doi: 10.1038/75556google scholar: lookup
  3. Bhatnagar S, Panguluri SK, Gupta SK, Dahiya S, Lundy RF, Kumar A. Tumor necrosis factor-α regulates distinct molecular pathways and gene networks in cultured skeletal muscle cells.. PLoS One 2010 Oct 12;5(10):e13262.
    pmc: PMC2953497pubmed: 20967264doi: 10.1371/journal.pone.0013262google scholar: lookup
  4. Cheung WA, Ouellette BF, Wasserman WW. Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs).. BMC Bioinformatics 2012 Sep 27;13:249.
    pmc: PMC3564935pubmed: 23017167doi: 10.1186/1471-2105-13-249google scholar: lookup
  5. Coletti MH, Bleich HL. Medical subject headings used to search the biomedical literature.. J Am Med Inform Assoc 2001 Jul-Aug;8(4):317-23.
    pmc: PMC130076pubmed: 11418538doi: 10.1136/jamia.2001.0080317google scholar: lookup
  6. Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA. Global functional profiling of gene expression.. Genomics 2003 Feb;81(2):98-104.
    pubmed: 12620386doi: 10.1016/s0888-7543(02)00021-6google scholar: lookup
  7. Elis S, Coyral-Castel S, Freret S, Cognié J, Desmarchais A, Fatet A, Rame C, Briant E, Maillard V, Dupont J. Expression of adipokine and lipid metabolism genes in adipose tissue of dairy cows differing in a female fertility quantitative trait locus.. J Dairy Sci 2013;96(12):7591-602.
    pubmed: 24119802doi: 10.3168/jds.2013-6615google scholar: lookup
  8. García-Fernández M, Gutiérrez-Gil B, García-Gámez E, Sánchez JP, Arranz JJ. The identification of QTL that affect the fatty acid composition of milk on sheep chromosome 11.. Anim Genet 2010 Jun;41(3):324-8.
    pubmed: 19968648doi: 10.1111/j.1365-2052.2009.02000.xgoogle scholar: lookup
  9. Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes.. Nat Rev Genet 2009 Jun;10(6):381-91.
    pubmed: 19448663doi: 10.1038/nrg2575google scholar: lookup
  10. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.. Nucleic Acids Res 2009 Jan;37(1):1-13.
    pmc: PMC2615629pubmed: 19033363doi: 10.1093/nar/gkn923google scholar: lookup
  11. Jani SD, Argraves GL, Barth JL, Argraves WS. GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms.. BMC Bioinformatics 2010 Apr 1;11:166.
    pmc: PMC3212930pubmed: 20359363doi: 10.1186/1471-2105-11-166google scholar: lookup
  12. Jeong J, Kwon EG, Im SK, Seo KS, Baik M. Expression of fat deposition and fat removal genes is associated with intramuscular fat content in longissimus dorsi muscle of Korean cattle steers.. J Anim Sci 2012 Jun;90(6):2044-53.
    pubmed: 22266990doi: 10.2527/jas.2011-4753google scholar: lookup
  13. Khatib H, Maltecca C, Monson RL, Schutzkus V, Wang X, Rutledge JJ. The fibroblast growth factor 2 gene is associated with embryonic mortality in cattle.. J Anim Sci 2008 Sep;86(9):2063-7.
    pubmed: 18469054doi: 10.2527/jas.2007-0791google scholar: lookup
  14. Khatri P, Drăghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems.. Bioinformatics 2005 Sep 15;21(18):3587-95.
    pmc: PMC2435250pubmed: 15994189doi: 10.1093/bioinformatics/bti565google scholar: lookup
  15. Li X, Barkho BZ, Luo Y, Smrt RD, Santistevan NJ, Liu C, Kuwabara T, Gage FH, Zhao X. Epigenetic regulation of the stem cell mitogen Fgf-2 by Mbd1 in adult neural stem/progenitor cells.. J Biol Chem 2008 Oct 10;283(41):27644-27652.
    pmc: PMC2562066pubmed: 18689796doi: 10.1074/jbc.M804899200google scholar: lookup
  16. Mirina A, Atzmon G, Ye K, Bergman A. Gene size matters.. PLoS One 2012;7(11):e49093.
    pmc: PMC3494661pubmed: 23152854doi: 10.1371/journal.pone.0049093google scholar: lookup
  17. Morota G, Abdollahi-Arpanahi R, Kranis A, Gianola D. Genome-enabled prediction of quantitative traits in chickens using genomic annotation.. BMC Genomics 2014 Feb 7;15:109.
    pmc: PMC3922252pubmed: 24502227doi: 10.1186/1471-2164-15-109google scholar: lookup
  18. Nakazato T, Bono H, Matsuda H, Takagi T. Gendoo: functional profiling of gene and disease features using MeSH vocabulary.. Nucleic Acids Res 2009 Jul;37(Web Server issue):W166-9.
    pmc: PMC2703956pubmed: 19498079doi: 10.1093/nar/gkp483google scholar: lookup
  19. Nakazato T, Takinaka T, Mizuguchi H, Matsuda H, Bono H, Asogawa M. BioCompass: a novel functional inference tool that utilizes MeSH hierarchy to analyze groups of genes.. In Silico Biol 2008;8(1):53-61.
    pubmed: 18430990
  20. Nelson SJ, Schopen M, Savage AG, Schulman JL, Arluk N. The MeSH translation maintenance system: structure, interface design, and implementation.. Stud Health Technol Inform 2004;107(Pt 1):67-9.
    pubmed: 15360776
  21. Osborne JD, Zhu LJ, Lin SM, Kibbe WA. Interpreting microarray results with gene ontology and MeSH.. Methods Mol Biol 2007;377:223-42.
    pubmed: 17634620doi: 10.1007/978-1-59745-390-5_14google scholar: lookup
  22. Pavlidis P, Jensen JD, Stephan W, Stamatakis A. A critical assessment of storytelling: gene ontology categories and the importance of validating genomic scans.. Mol Biol Evol 2012 Oct;29(10):3237-48.
    pubmed: 22617950doi: 10.1093/molbev/mss136google scholar: lookup
  23. Peñagaricano F, Souza AH, Carvalho PD, Driver AM, Gambra R, Kropp J, Hackbart KS, Luchini D, Shaver RD, Wiltbank MC, Khatib H. Effect of maternal methionine supplementation on the transcriptome of bovine preimplantation embryos.. PLoS One 2013;8(8):e72302.
    pmc: PMC3749122pubmed: 23991086doi: 10.1371/journal.pone.0072302google scholar: lookup
  24. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Capomaccio S, Cappelli K, Cothran EG, Distl O, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Swinburne J, Tozaki T, Vaudin M, Wade CM, McCue ME. Genome-wide analysis reveals selection for important traits in domestic horse breeds.. PLoS Genet 2013;9(1):e1003211.
    pmc: PMC3547851pubmed: 23349635doi: 10.1371/journal.pgen.1003211google scholar: lookup
  25. Petersen JL, Valberg SJ, Mickelson JR, McCue ME. Haplotype diversity in the equine myostatin gene with focus on variants associated with race distance propensity and muscle fiber type proportions.. Anim Genet 2014 Dec;45(6):827-35.
    pmc: PMC4211974pubmed: 25160752doi: 10.1111/age.12205google scholar: lookup
  26. Rempel LA, Casas E, Shackelford SD, Wheeler TL. Relationship of polymorphisms within metabolic genes and carcass traits in crossbred beef cattle.. J Anim Sci 2012 Apr;90(4):1311-6.
    pubmed: 22100592doi: 10.2527/jas.2011-4302google scholar: lookup
  27. Rivals I, Personnaz L, Taing L, Potier MC. Enrichment or depletion of a GO category within a class of genes: which test?. Bioinformatics 2007 Feb 15;23(4):401-7.
    pubmed: 17182697doi: 10.1093/bioinformatics/btl633google scholar: lookup
  28. Sedeño-Cortés AE, Pavlidis P. Pitfalls in the application of gene-set analysis to genetics studies.. Trends Genet 2014 Dec;30(12):513-4.
    pmc: PMC5369409pubmed: 25459301doi: 10.1016/j.tig.2014.10.001google scholar: lookup
  29. Sherblom AP, Smagula RM, Moody CE, Anderson GW. Immunosuppression, sialic acid, and sialyltransferase of bovine serum as a function of progesterone concentration.. J Reprod Fertil 1985 Jul;74(2):509-17.
    pubmed: 4045819doi: 10.1530/jrf.0.0740509google scholar: lookup
  30. Soldatos TG, Perdigão N, Brown NP, Sabir KS, O'Donoghue SI. How to learn about gene function: text-mining or ontologies?. Methods 2015 Mar;74:3-15.
    pubmed: 25088781doi: 10.1016/j.ymeth.2014.07.004google scholar: lookup
  31. Tart JK, Johnson RK, Bundy JW, Ferdinand NN, McKnite AM, Wood JR, Miller PS, Rothschild MF, Spangler ML, Garrick DJ, Kachman SD, Ciobanu DC. Genome-wide prediction of age at puberty and reproductive longevity in sows.. Anim Genet 2013 Aug;44(4):387-97.
    pubmed: 23437861doi: 10.1111/age.12028google scholar: lookup
  32. Tsuyuzaki K, Morota G, Ishii M, Nakazato T, Miyazaki S, Nikaido I. MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis.. BMC Bioinformatics 2015 Feb 15;16:45.
    pmc: PMC4343279pubmed: 25887539doi: 10.1186/s12859-015-0453-zgoogle scholar: lookup

Citations

This article has been cited 11 times.
  1. Amorim ST, Tsuyuzaki K, Nikaido I, Morota G. Improved MeSH analysis software tools for farm animals.. Anim Genet 2022 Feb;53(1):171-172.
    doi: 10.1111/age.13159pubmed: 34859479google scholar: lookup
  2. Sigdel A, Abdollahi-Arpanahi R, Aguilar I, Peñagaricano F. Whole Genome Mapping Reveals Novel Genes and Pathways Involved in Milk Production Under Heat Stress in US Holstein Cows.. Front Genet 2019;10:928.
    doi: 10.3389/fgene.2019.00928pubmed: 31636656google scholar: lookup
  3. Lin S, Zhang H, Hou Y, Liu L, Li W, Jiang J, Han B, Zhang S, Sun D. SNV discovery and functional candidate gene identification for milk composition based on whole genome resequencing of Holstein bulls with extremely high and low breeding values.. PLoS One 2019;14(8):e0220629.
    doi: 10.1371/journal.pone.0220629pubmed: 31369641google scholar: lookup
  4. Almeida OAC, Moreira GCM, Rezende FM, Boschiero C, de Oliveira Peixoto J, Ibelli AMG, Ledur MC, de Novais FJ, Coutinho LL. Identification of selection signatures involved in performance traits in a paternal broiler line.. BMC Genomics 2019 Jun 3;20(1):449.
    doi: 10.1186/s12864-019-5811-1pubmed: 31159736google scholar: lookup
  5. Rezende FM, Dietsch GO, Peñagaricano F. Genetic dissection of bull fertility in US Jersey dairy cattle.. Anim Genet 2018 Oct;49(5):393-402.
    doi: 10.1111/age.12710pubmed: 30109710google scholar: lookup
  6. Dado-Senn B, Skibiel AL, Fabris TF, Zhang Y, Dahl GE, Peñagaricano F, Laporta J. RNA-Seq reveals novel genes and pathways involved in bovine mammary involution during the dry period and under environmental heat stress.. Sci Rep 2018 Jul 23;8(1):11096.
    doi: 10.1038/s41598-018-29420-8pubmed: 30038226google scholar: lookup
  7. Júnior GAO, Perez BC, Cole JB, Santana MHA, Silveira J, Mazzoni G, Ventura RV, Júnior MLS, Kadarmideen HN, Garrick DJ, Ferraz JBS. Genomic study and Medical Subject Headings enrichment analysis of early pregnancy rate and antral follicle numbers in Nelore heifers.. J Anim Sci 2017 Nov;95(11):4796-4812.
    doi: 10.2527/jas2017.1752pubmed: 29293733google scholar: lookup
  8. Beissinger TM, Morota G. Medical Subject Heading (MeSH) annotations illuminate maize genetics and evolution.. Plant Methods 2017;13:8.
    doi: 10.1186/s13007-017-0159-5pubmed: 28250803google scholar: lookup
  9. Han Y, Peñagaricano F. Unravelling the genomic architecture of bull fertility in Holstein cattle.. BMC Genet 2016 Nov 14;17(1):143.
    doi: 10.1186/s12863-016-0454-6pubmed: 27842509google scholar: lookup
  10. Morota G, Beissinger TM, Peñagaricano F. MeSH-Informed Enrichment Analysis and MeSH-Guided Semantic Similarity Among Functional Terms and Gene Products in Chicken.. G3 (Bethesda) 2016 Aug 9;6(8):2447-53.
    doi: 10.1534/g3.116.031096pubmed: 27261003google scholar: lookup
  11. Laporta J, Peñagaricano F, Hernandez LL. Transcriptomic Analysis of the Mouse Mammary Gland Reveals New Insights for the Role of Serotonin in Lactation.. PLoS One 2015;10(10):e0140425.
    doi: 10.1371/journal.pone.0140425pubmed: 26470019google scholar: lookup
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