Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB

Nucleic Acids Research

Volumn 47, Issue D1, 2019, Pages D701-D710

  Hu, Zhi Liang ^a   Park, Carissa A ^a   Reecy, James M ^a  

^a IOWA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DATA MINING; GENETIC ANALYSIS; HERITABILITY; HUMAN; LIVESTOCK; NONHUMAN; ANIMAL; GENETIC DATABASE; GENETICS; GENOME-WIDE ASSOCIATION STUDY; KNOWLEDGE BASE; POULTRY; PROCEDURES; QUANTITATIVE TRAIT LOCUS;

ANIMALS; DATABASES, GENETIC; GENOME-WIDE ASSOCIATION STUDY; KNOWLEDGE BASES; LIVESTOCK; POULTRY; QUANTITATIVE TRAIT LOCI;

EID: 85059796742     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gky1084     Document Type: Article

References (24)

1. Elsik, C.G., Tellam, R.L., Worley, K.C., Gibbs, R.A., Muzny, D.M., Weinstock, G.M., Adelson, D.L., Eichler, E.E., Elnitski, L., Guigó, R. et al. (2009) The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science, 324, 522-528.
2. Zimin, A.V., Delcher, A.L., Florea, L., Kelley, D.R., Schatz, M.C., Puiu, D., Hanrahan, F., Pertea, G., Van Tassell, C.P., Sonstegard, T.S. et al. (2009) A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol., 10, R42.
3. International Chicken Genome Sequencing Consortium (2004) Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature, 432, 695-716.
4. Groenen, M.A., Archibald, A.L., Uenishi, H., Tuggle, C.K., Takeuchi, Y., Rothschild, M.F., Rogel-Gaillard, C., Park, C., Milan, D., Megens, H.J. et al. (2012) Analyses of pig genomes provide insight into porcine demography and evolution. Nature, 491, 393-398.
5. Jiang, Y., Xie, M., Chen, W., Talbot, R., Maddox, J.F., Faraut, T., Wu, C., Muzny, D.M., Li, Y., Zhang, W. et al. (2014) The sheep genome illuminates biology of the rumen and lipid metabolism. Science, 344, 1168-1173.
6. Wade, C.M., Giulotto, E., Sigurdsson, S., Zoli, M., Gnerre, S., Imsland, F., Lear, T.L., Adelson, D.L., Bailey, E., Bellone, R.R. et al. (2009) Genome sequence, comparative analysis, and population genetics of the domestic horse. Science, 326, 865-867.
7. Liu, Z., Liu, S., Yao, J., Bao, L., Zhang, J., Li, Y., Jiang, C., Sun, L., Wang, R., Zhang, Y. et al. (2016) The channel catfish genome sequence provides insights into the evolution of scale formation in teleosts. Nat. Commun., 7, 11757.
8. Berthelot, C., Brunet, F., Chalopin, D., Juanchich, A., Bernard, M., Noël, B., Bento, P., Da Silva, C., Labadie, K., Alberti, A. et al. (2014) The rainbow trout genome provides novel insights into evolution after whole-genome duplication in vertebrates. Nat. Commun., 5, 3657.
9. Andersson, L., Archibald, A.L., Bottema, C.D., Brauning, R., Burgess, S.C., Burt, D.W., Casas, E., Cheng, H.H., Clarke, L., Couldrey, C. et al. (2015) Coordinated international action to accelerate genome-to-phenome with FAANG, the Functional Annotation of Animal Genomes project. Genome Biol., 16, 57.
10. Sharma, A., Lee, J.S., Dang, C.G., Sudrajad, P., Kim, H.C., Yeon, S.H., Kang, H.S. and Lee, S.H. (2015) Stories and challenges of genome wide association studies in livestock - A Review. Asian-Australas. J. Anim. Sci., 28, 1371-1379.
11. Hu, Z.L., Dracheva, S., Jang, W., Maglott, D., Bastiaansen, J., Rothschild, M.F., Reecy, J.M. et al. (2005) A QTL resource and comparison tool for pigs: PigQTLDB. Mamm. Genome, 16, 792-800.
12. Hu, Z.L. and Reecy, J.M. (2007) Animal QTLdb: Beyond a repository. A public platform for QTL comparisons and integration with diverse types of structural genomic information. Mamm. Genome, 18, 1-4.
13. Hu, Z.L., Park, C.A., Wu, X.L. and Reecy, J.M. (2013) Animal QTLdb: An improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Res., 41, D871-D879.
14. Hu, Z.L., Park, C.A. and Reecy, J.M. (2016) Developmental progress and current status of the animal QTLdb. Nucleic Acids Res., 44, D827-D833.
15. Wu, X.-L., Gianola, D., Hu, Z.-L. and Reecy, J.M. (2011) Meta-analysis of quantitative trait association and mapping studies using parametric and non-parametric models. J. Biometrics Biostat., doi:10.4172/2155-6180.S1-001.
16. Hu, Z.-L., Park, C.A. and Reecy, J.M. (2018) Development of animal QTLdb and CorrDB: resynthesizing big data to improve meta-analysis of genetic and genomic information. In: The 11th World Congress on Genetics Applied to Livestock Production (WCGALP). New Zealand.
17. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T. et al. (2000) Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet., 25, 25-29.
18. The Gene Ontology Consortium. (2017) Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res., 45, D331-D338.
19. Huang, D.W., Sherman, B.T. and Lempicki, R.A. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37, 1-13.
20. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S. et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A., 102, 15545-15550.
21. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B, 57, 289-300.
22. Park, C.A., Bello, S.M., Smith, C.L., Hu, Z.-L., Munzenmaier, D.H., Nigam, R., Smith, J.R., Shimoyama, M., Eppig, J.T. and Reecy, J.M. (2013) The vertebrate trait ontology: A controlled vocabulary for the annotation of trait data across species. J. Biomed. Semantics, 4, 13.
23. Smith, J.R., Park, C.A., Nigam, R., Laulederkind, S.J., Hayman, G.T., Wang, S.J., Lowry, T.F., Petri, V., Pons, J.D., Tutaj, M. et al. (2013) The clinical measurement, measurement method and experimental condition ontologies: eExpansion, improvements and new applications. J. Biomed. Semantics, 4, 26.
24. Zhu, H. and Madnick, S.E. (2007) Scalable interoperability through the use of COIN lightweight ontology. In: Collard, M (ed). Ontologies-Based Databases and Information Systems. Springer, Berlin, Heidelberg, 37-50.