Aitchison, J 1982. The statistical analysis of compositional data. Journal of the Royal Statistical Society Series B-Methodological 44, 139–177.
Anikaev, A, Isaev, A, Korobeinikova, A, Garber, M and Gongadze, G 2016. Role of protein L25 and its contact with protein L16 in maintaining the active state of Escherichia coli ribosomes in vivo. Biochemistry 81, 19–27.
Bolger, AM, Lohse, M and Usadel, B 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120.
Christopherson, MR, Dawson, JA, Stevenson, DM, Cunningham, AC, Bramhacharya, S, Weimer, PJ, Kendziorski, C and Suen, G 2014. Unique aspects of fiber degradation by the ruminal ethanologen Ruminococcus albus 7 revealed by physiological and transcriptomic analysis. BMC Genomics 15, 1066.10.1186/1471-2164-15-1066
Creevey, CJ, Kelly, WJ, Henderson, G and Leahy, SC 2014. Determining the culturability of the rumen bacterial microbiome. Microbial Biotechnology 7, 467–479.
Crews, DH Jr, Shannon, NH, Genswein, BMA, Crews, RE, Johnson, CM and Kendrick, BA 2005. Genetic parameters for net feed efficiency of beef cattle measured during postweaning growing versus finishing periods. Proceedings Western Section American Society of Animal Science 54, 125–128.
Franzosa, EA, Morgan, XC, Segata, N, Waldron, L, Reyes, J, Earl, AM, Giannoukos, G, Boylan, MR, Ciulla, D, Gevers, D, Izard, J, Garrett, WS, Chan, AT and Huttenhower, C 2014. Relating the metatranscriptome and metagenome of the human gut. Proceedings of the National Academy of Sciences 111, E2329–E2338.
Geishauser, T 1993. An Instrument for collection and transfer of ruminal fluid and for administration of water soluble drugs in adult cattle. The Bovine Practitioner 27, 38–42.
Gonzalez, I, Dejean, S, Martin, PGP and Baccini, A 2008. CCA: an R package to extend canonical correlation analysis. Journal of Statistical Software 23, 1–14.
Guan, LL, Nkrumah, JD, Basarab, JA and Moore, SS 2008. Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle’s feed efficiency. FEMS Microbiology Letters 288, 85–91.
Hackmann, TJ, Tao, J, Ngugi, DK and Firkins, JL 2017. Genomes of rumen bacteria encode atypical pathways for fermenting hexoses to short-chain fatty acids. Environmental Microbiology 19, 4670–4683.
Henderson, G, Cox, F, Ganesh, S, Jonker, A, Young, W, Janssen, PH and Global Rumen Census C 2016. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Scientific Reports 6, 14567.
Kanehisa, M, Goto, S, Kawashima, S, Okuno, Y and Hattori, M 2004. The KEGG resource for deciphering the genome. Nucleic Acids Research 32, D277–D280.
Knights, D, Parfrey, LW, Zaneveld, J, Lozupone, C and Knight, R 2011. Human-associated microbial signatures: examining their predictive value. Cell Host & Microbe 10, 292–296.
Kopylova, E, Noé, L and Touzet, H 2012. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics (Oxford, England) 28, 3211–3217.
Larsbrink, J, Tuveng, TR, Pope, PB, Eijsink, VGH, Bulone, V, Brumer, H and McKee, LS 2017. Proteomic data on enzyme secretion and activity in the bacterium Chitinophaga pinensis. Data in Brief 11, 484–490.
Lê Cao, KA, Costello, ME, Lakis, VA, Bartolo, F, Chua, XY, Brazeilles, R and Rondeau, P 2016. MixMC: a multivariate statistical framework to gain insight into microbial communities. Plos One 11, e0160169.
Lee, S, Sung, J, Lee, J and Ko, G 2011. Comparison of the gut microbiotas of healthy adult twins living in South Korea and the United States. Applied and Environmental Microbiology 77, 7433–7437.
Li, DH, Liu, CM, Luo, RB, Sadakane, K and Lam, TW 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676.
Li, F and Guan, LL 2017. Metatranscriptomic profiling reveals linkages between the active rumen microbiome and feed efficiency in beef cattle. Applied and Environmental Microbiology 83, 1–17
Li, F, Hitch, TCA, Chen, Y, Creevey, CJ and Guan, LL 2019. Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle. Microbiome 7, 1–21.
Li, ZP, Wright, ADG, Si, HZ, Wang, XX, Qian, WX, Zhang, ZG and Li, GY 2016. Changes in the rumen microbiome and metabolites reveal the effect of host genetics on hybrid crosses. Environmental Microbiology Reports 8, 1016–1023.
Mandal, S, Van Treuren, W, White, RA, Eggesbo, M, Knight, R and Peddada, SD 2015. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial Ecology in Health and Disease 26, 27663.
Marquez, GC, Speidel, SE, Enns, RM and Garrick, DJ 2010. Genetic diversity and population structure of American Red Angus cattle. Journal of Animal Science 88, 59–68.
McLean, KL and Schmutz, SM 2009. Associations of melanocortin 1 receptor genotype with growth and carcass traits in beef cattle. Canadian Journal of Animal Science 89, 295–300.
Nayfach, S, Bradley, PH, Wyman, SK, Laurent, TJ, Williams, A, Pollard, KS, Sharpton, TJ and Eisen, JA 2015. Automated and accurate estimation of gene family abundance from shotgun metagenomes. Plos Computational Biology 11, e1004573.
Neves, ALA, Li, F, Ghoshal, B, McAllister, T and Guan, LL 2017. Enhancing the resolution of rumen microbial classification from metatranscriptomic data using Kraken and Mothur. Frontiers in Microbiology 8, 1–13.
Pajarillo, EAB, Chae, JP, Balolong, MP, Kim, HB, Kang, D-K and Seo, K-S 2015. Characterization of the fecal microbial communities of duroc pigs using 16S rRNA gene pyrosequencing. Asia Australasia Journal of Animal Science 28, 584–591.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Roehe, R, Dewhurst, RJ, Duthie, CA, Rooke, JA, McKain, N, Ross, DW, Hyslop, JJ, Waterhouse, A, Freeman, TC, Watson, M and Wallace, RJ 2016. Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane emitting and efficiently feed converting hosts based on metagenomic gene abundance. Plos Genetics 12, e1005846.
Rohart, F, Gautier, B, Singh, A and Lê Cao, K-A 2017. mixOmics: an R package for ‘omics feature selection and multiple data integration. Plos Computational Biology 13, e1005752.
Russell, JB and Rychlik, JL 2001. Factors that alter rumen microbial ecology. Science 292, 1119–1122.
Schellenberg, JJ, Verbeke, TJ, Sparling, R, McQueen, P, Krokhin, OV, Wilkins, JA, Zhang, X, Alvare, G, Fristensky, B, Levin, DB, Thallinger, GG and Henrissat, B 2014. Enhanced whole genome sequence and annotation of Clostridium stercorarium DSM8532T using RNA-seq transcriptomics and high-throughput proteomics. BMC Genomics 15, 1–16.
Seshadri, R, Leahy, SC, Attwood, GT, Teh, KH, Lambie, SC, Cookson, AL, Eloe-Fadrosh, EA, Pavlopoulos, GA, Hadjithomas, M, Varghese, NJ, Paez-Espino, D, Perry, R, Henderson, G, Creevey, CJ, Terrapon, N, Lapebie, P, Drula, E, Lombard, V, Rubin, E, Kyrpides, NC, Henrissat, B, Woyke, T, Ivanova, NN and Kelly, WJ 2018. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nature Biotechnology 36, 359–367.
Stewart, RD, Auffret, MD, Warr, A, Wiser, AH, Press, MO, Langford, KW, Liachko, I, Snelling, TJ, Dewhurst, RJ, Walker, AW, Roehe, R and Watson, M 2018. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nature Communications 9, 1–11.
Thompson, S. (2015). The effect of diet type on residual feed intake and the use of infrared thermography as a method to predict efficiency in beef bulls. Master’s thesis, University of Manitoba; Winnipeg, MB.
Wang, L, Hatem, A, Catalyurek, UV, Morrison, M and Yu, Z 2013. Metagenomic insights into the carbohydrate-active enzymes carried by the microorganisms adhering to solid digesta in the rumen of cows. Plos One 8, 1–11.
Wang, Y and McAllister, TA 2002. Rumen microbes, enzymes and feed digestion – A Review. Asian-Australasian Journal of Animal Science 15, 1659–1676.
Weiss, S, Xu, ZZ, Peddada, S, Amir, A, Bittinger, K, Gonzalez, A, Lozupone, C, Zaneveld, JR, Vazquez-Baeza, Y, Birmingham, A, Hyde, ER and Knight, R 2017. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27.
Wimberly, BT, Ramakrishnan, V and White, SW 1997. The structure of ribosomal protein S7 at 1.9 Å resolution reveals a β-hairpin motif that binds double-stranded nucleic acids. Structure 5, 1187–1198.
Wolfger, B, Quinn, C, Torres, GW, Taylor, M and Orsel, K 2016. Comparison of feeding behavior between black and red Angus feeder heifers. Canadian Journal of Animal Science 96, 404–409.
Wood, DE and Salzberg, SL 2014. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biology 15, R46–R46.
Zhou, M, Peng, YJ, Chen, Y, Klinger, CM, Oba, M, Liu, J-X and Guan, LL 2018. Assessment of microbiome changes after rumen transfaunation: implications on improving feed efficiency in beef cattle. Microbiome 6, 1–14.