References
- Anders, S. and W. Huber. 2010. Differential expression analysis for sequence count data. Genome Biol. 11:R106. https://doi.org/10.1186/gb-2010-11-10-r106
- Arthur, P. F., J. A. Archer, D. J. Johnston, R. M. Herd, E. C. Richardson, and P. F. Parnell. 2001. Genetic and phenotypic variance and covariance components for feed intake, feed efficiency, and other postweaning traits in Angus cattle. J. Anim. Sci. 79:2805-2811. https://doi.org/10.2527/2001.79112805x
- Chen, Y., C. Gondro, K. Quinn, R. M. Herd, P. F. Parnell, and B. Vanselow. 2011. Global gene expression profiling reveals genes expressed differentially in cattle with high and low residual feed intake. Anim. Genet. 42:475-490. https://doi.org/10.1111/j.1365-2052.2011.02182.x
- Donoghue, K. A., P. F. Arthur, J. F. Wilkins, and R. M. Herd. 2011. Onset of puberty and early-life reproduction in Angus females divergently selected for post-weaning residual feed intake. Anim. Prod. Sci. 51:183-190. https://doi.org/10.1071/AN10097
- Fatima, A., D. J. Lynn, P. O'Boyle, C. Seoighe, and D. Morris. 2014. The miRNAome of the postpartum dairy cow liver in negative energy balance. BMC Genomics 15:279. https://doi.org/10.1186/1471-2164-15-279
- Gardner, P. P., J. Daub, J. G. Tate, E. P. Nawrocki, D. L. Kolbe, S. Lindgreen, A. C. Wilkinson, R. D. Finn, S. Griffiths-Jones, S. R. Eddy, and A. Bateman. 2009. Rfam: updates to the RNA families database. Nucl. Acids Res. 37:D136-D140. https://doi.org/10.1093/nar/gkn766
- Kozomara, A. and S. Griffiths-Jones. 2014. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucl. Acids. Res. 42:D68-D73. https://doi.org/10.1093/nar/gkt1181
- Gu, Z., S. Eleswarapu, and H. Jiang. 2007. Identification and characterization of microRNAs from the bovine adipose tissue and mammary gland. FEBS Lett. 581:981-988. https://doi.org/10.1016/j.febslet.2007.01.081
- Hackenberg, M., N. Rodriguez-Ezpeleta, and A. M. Aransay. 2011. miRanalyzer: An update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucl. Acids Res. 39:W132-W138. https://doi.org/10.1093/nar/gkr247
- Hu, J., Y. Xu, J. Hao, S. Wang, C. Li, and S. Meng. 2012. MiR-122 in hepatic function and liver diseases. Protein Cell. 3:364-371. https://doi.org/10.1007/s13238-012-2036-3
- Jin, W., J. R. Grant, P. Stothard, S. S. Moore, and L. L. Guan. 2009. Characterization of bovine miRNAs by sequencing and bioinformatics analysis. BMC Mol. Biol. 10:90. https://doi.org/10.1186/1471-2199-10-90
- Jordan, S. D., M. Kruger, D. M. Willmes, N. Redemann, F. T. Wunderlich, H. S. Bronneke, C. Merkwirth, H. Kashkar, V. M. Olkkonen, T. Bottger, T. Braun, J. Seibler, and J. C. Bruning. 2011. Obesity-induced overexpression of miRNA-143 inhibits insulin-stimulated AKT activation and impairs glucose metabolism. Nat. Cell Biol. 13:434-446. https://doi.org/10.1038/ncb2211
- Koch, R. M., L. A. Swiger, D. Chambers, and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486-494. https://doi.org/10.2527/jas1963.222486x
- Kornfeld, J. W., C. Baitzel, A. C. Konner, H. T. Nicholls, M. C. Vogt, K. Herrmanns, L. Scheja, C. Haumaitre, A. M. Wolf, U. Knippschild, J. Seibler, S. Cereghini, J. Heeren, M. Stoffel, and J. C. Bruning. 2013. Obesity-induced overexpression of miR-802 impairs glucose metabolism through silencing of Hnf1b. Nature 494:111-115. https://doi.org/10.1038/nature11793
- Kozomara, A. and S. Griffiths-Jones. 2011. miRBase: integrating microRNA annotation and deep-sequencing data. Nucl. Acids Res. 39:D152-D157. https://doi.org/10.1093/nar/gkq1027
- Langmead, B., C. Trapnell, M. Pop, and S. L. Salzberg. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10:R25. https://doi.org/10.1186/gb-2009-10-3-r25
- Lawless, N., A. B. Foroushani, M. S. McCabe, C. O'Farrelly, and D. J. Lynn. 2013. Next Generation sequencing reveals the expression of a unique miRNA profile in response to a grampositive bacterial infection. PLoS One. 8:e57543. https://doi.org/10.1371/journal.pone.0057543
- Lewis, A. P. and C. L. Jopling. 2010. Regulation and biological function of the liver-specific miR-122. Biochem. Soc. Trans. 38:1553-1557. https://doi.org/10.1042/BST0381553
- Liu, H. C., J. A. Hicks, N. Trakooljul, and S. H. Zhao. 2010. Current knowledge of microRNA characterization in agricultural animals. Anim. Genet. 41:225-231. https://doi.org/10.1111/j.1365-2052.2009.01995.x
- Lu, J., G. Getz, E. A. Miska, E. Alvarez-Saavedra, J. Lamb, D. Peck, A. Sweet-Cordero, B. L. Ebert, R. H. Mak, A. A. Ferrando, J. R. Downing, T. Jacks, H. R. Horvitz, and T. R. Golub. 2005. MicroRNA expression profiles classify human cancers. Nature 435:834-838. https://doi.org/10.1038/nature03702
- Miles J. R., T. G. McDaneld, R. T. Wiedmann, R. A. Cushman, S. E. Echternkamp, J. L. Vallet, and T. P. L. Smith. 2012. MicroRNA expression profile in bovine cumulus-oocyte complexes: Possible role of let-7 and miR-106a in the development of bovine oocytes. Anim. Reprod. Sci. 130:16-26. https://doi.org/10.1016/j.anireprosci.2011.12.021
- Pandey, A. K., G. Verma, S. Vig, S. Srivastava, A. K. Srivastava, and M. Datta. 2011. miR-29a levels are elevated in the db/db mice liver and its overexpression leads to attenuation of insulin action on PEPCK gene expression in HepG2 cells. Mol. Cell. Endocrinol. 332:125-133. https://doi.org/10.1016/j.mce.2010.10.004
- Romao, J. M., W. Jin, M. He, T. McAllister, and L. L. Guan. 2012. Altered MicroRNA expression in bovine subcutaneous and visceral adipose tissues from cattle under different diet. PLoS One. 7:e40605. https://doi.org/10.1371/journal.pone.0040605
- Rottiers, V. and A. M. Naar. 2012. MicroRNAs in metabolism and metabolic disorders. Nat. Rev. Mol. Cell Biol. 13:239-250. https://doi.org/10.1038/nrm3313
- Sherman, E. L., J. D. Nkrumah, C. Li, R. Bartusiak, B. Murdoch, and S. S. Moore. 2009. Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle. J. Anim. Sci. 87:37-45. https://doi.org/10.2527/jas.2008-0876
- Tripurani, S. K., C. Xiao, M. Salem, and J. Yao. 2010. Cloning and analysis of fetal ovary microRNAs in cattle. Anim. Reprod. Sci. 120:16-22. https://doi.org/10.1016/j.anireprosci.2010.03.001
- Vejnar, C. E. and E. M. Zdobnov. 2012. miRmap: Comprehensive prediction of microRNA target repression strength. Nucl. Acids Res. 40:11673-11683. https://doi.org/10.1093/nar/gks901
- Wen, J. and J. R. Friedman. 2012. miR-122 regulates hepatic lipid metabolism and tumor suppression. J. Clin. Invest. 122:2773-2776. https://doi.org/10.1172/JCI63966
- Yang, J. S., M. D. Phillips, D. Betel, P. Mu, A. Ventura, A. C. Siepel, K. C. Chen, and E. C. Lai. 2011. Widespread regulatory activity of vertebrate microRNA* species. RNA. 17:312-326. https://doi.org/10.1261/rna.2537911
- Yu, Z., Z. Jian, S. H. Shen, E. Purisima, and E. Wang. 2007. Global analysis of microRNA target gene expression reveals that miRNA targets are lower expressed in mature mouse and Drosophila tissues than in the embryos. Nucl. Acids Res. 35:152-164. https://doi.org/10.1093/nar/gkl1032
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