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For: Horodyska J, Wimmers K, Reyer H, Trakooljul N, Mullen AM, Lawlor PG, Hamill RM. RNA-seq of muscle from pigs divergent in feed efficiency and product quality identifies differences in immune response, growth, and macronutrient and connective tissue metabolism. BMC Genomics 2018;19:791. [PMID: 30384851 DOI: 10.1186/s12864-018-5175-y] [Cited by in Crossref: 25] [Cited by in F6Publishing: 27] [Article Influence: 6.3] [Reference Citation Analysis]
Number Citing Articles
1 Mármol-Sánchez E, Cirera S, Zingaretti LM, Jacobsen MJ, Ramayo-Caldas Y, Jørgensen CB, Fredholm M, Cardoso TF, Quintanilla R, Amills M. Modeling microRNA-driven post-transcriptional regulation using exon-intron split analysis in pigs. Anim Genet 2022. [PMID: 35811409 DOI: 10.1111/age.13238] [Reference Citation Analysis]
2 Davoudi P, Do DN, Colombo SM, Rathgeber B, Miar Y. Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency. Front Genet 2022;13:903733. [PMID: 35754793 DOI: 10.3389/fgene.2022.903733] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Kaewsatuan P, Poompramun C, Kubota S, Yongsawatdigul J, Molee W, Uimari P, Molee A. Comparative proteomics revealed duodenal metabolic function associated with feed efficiency in slow-growing chicken. Poultry Science 2022. [DOI: 10.1016/j.psj.2022.101824] [Reference Citation Analysis]
4 Cai Z, Christensen OF, Lund MS, Ostersen T, Sahana G. Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans. BMC Genomics 2022;23:133. [PMID: 35168569 DOI: 10.1186/s12864-022-08373-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Sinpru P, Riou C, Kubota S, Poompramun C, Molee W, Molee A. Jejunal Transcriptomic Profiling for Differences in Feed Conversion Ratio in Slow-Growing Chickens. Animals (Basel) 2021;11:2606. [PMID: 34573572 DOI: 10.3390/ani11092606] [Reference Citation Analysis]
6 Esmaeili M, Carter CG, Wilson R, Walker SP, Miller MR, Bridle A, Symonds JE. Proteomic investigation of liver and white muscle in efficient and inefficient Chinook salmon (Oncorhynchus tshawytscha): Fatty acid metabolism and protein turnover drive feed efficiency. Aquaculture 2021;542:736855. [DOI: 10.1016/j.aquaculture.2021.736855] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 8.0] [Reference Citation Analysis]
7 Lee JB, Kang YJ, Kim SG, Woo JH, Shin MC, Park NG, Yang BC, Han SH, Han KM, Lim HT, Ryu YC, Park HB, Cho IC. GWAS and Post-GWAS High-Resolution Mapping Analyses Identify Strong Novel Candidate Genes Influencing the Fatty Acid Composition of the Longissimus dorsi Muscle in Pigs. Genes (Basel) 2021;12:1323. [PMID: 34573305 DOI: 10.3390/genes12091323] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 Zhu Y, Gagaoua M, Mullen AM, Viala D, Rai DK, Kelly AL, Sheehan D, Hamill RM. Shotgun proteomics for the preliminary identification of biomarkers of beef sensory tenderness, juiciness and chewiness from plasma and muscle of young Limousin-sired bulls. Meat Science 2021;176:108488. [DOI: 10.1016/j.meatsci.2021.108488] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Giráldez FJ, Mateo J, Carballo DE, Caro I, Andrés S. Divergent values in feed efficiency promote changes on meat quality of fattening lambs. Small Ruminant Research 2021;198:106353. [DOI: 10.1016/j.smallrumres.2021.106353] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Fang C, Guo F, Zhao X, Zhang Z, Lu J, Pan H, Xu T, Li W, Yang M, Huang Y, Zhao Y, Zhao S. Biological mechanisms of growth performance and meat quality in porcine muscle tissue. Anim Biotechnol 2021;:1-9. [PMID: 33704018 DOI: 10.1080/10495398.2021.1886939] [Reference Citation Analysis]
11 Jang KB, Kim JH, Purvis JM, Chen J, Ren P, Vazquez-Anon M, Kim SW. Effects of mineral methionine hydroxy analog chelate in sow diets on epigenetic modification and growth of progeny. J Anim Sci 2020;98:skaa271. [PMID: 32841352 DOI: 10.1093/jas/skaa271] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
12 Xu C, Wang X, Zhou S, Wu J, Geng Q, Ruan D, Qiu Y, Quan J, Ding R, Cai G, Wu Z, Zheng E, Yang J. Brain Transcriptome Analysis Reveals Potential Transcription Factors and Biological Pathways Associated with Feed Efficiency in Commercial DLY Pigs. DNA Cell Biol 2021;40:272-82. [PMID: 33297854 DOI: 10.1089/dna.2020.6071] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
13 Dorji J, MacLeod IM, Chamberlain AJ, Vander Jagt CJ, Ho PN, Khansefid M, Mason BA, Prowse-Wilkins CP, Marett LC, Wales WJ, Cocks BG, Pryce JE, Daetwyler HD. Mitochondrial protein gene expression and the oxidative phosphorylation pathway associated with feed efficiency and energy balance in dairy cattle. J Dairy Sci 2021;104:575-87. [PMID: 33162069 DOI: 10.3168/jds.2020-18503] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
14 Fernández-Barroso MÁ, Caraballo C, Silió L, Rodríguez C, Nuñez Y, Sánchez-Esquiliche F, Matos G, García-Casco JM, Muñoz M. Differences in the Loin Tenderness of Iberian Pigs Explained through Dissimilarities in Their Transcriptome Expression Profile. Animals (Basel) 2020;10:E1715. [PMID: 32971875 DOI: 10.3390/ani10091715] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
15 Carmelo VAO, Kadarmideen HN. Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs. PLoS One 2020;15:e0239143. [PMID: 32941478 DOI: 10.1371/journal.pone.0239143] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Lee J, Park N, Lee D, Kim J. Evolutionary and Functional Analysis of Korean Native Pig Using Single Nucleotide Polymorphisms. Mol Cells 2020;43:728-38. [PMID: 32868490 DOI: 10.14348/molcells.2020.0040] [Reference Citation Analysis]
17 Carmelo VAO, Kadarmideen HN. Genome Regulation and Gene Interaction Networks Inferred From Muscle Transcriptome Underlying Feed Efficiency in Pigs. Front Genet 2020;11:650. [PMID: 32655625 DOI: 10.3389/fgene.2020.00650] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
18 Yang L, He T, Xiong F, Chen X, Fan X, Jin S, Geng Z. Identification of key genes and pathways associated with feed efficiency of native chickens based on transcriptome data via bioinformatics analysis. BMC Genomics 2020;21:292. [PMID: 32272881 DOI: 10.1186/s12864-020-6713-y] [Cited by in Crossref: 3] [Cited by in F6Publishing: 12] [Article Influence: 1.5] [Reference Citation Analysis]
19 de Lima AO, Koltes JE, Diniz WJS, de Oliveira PSN, Cesar ASM, Tizioto PC, Afonso J, de Souza MM, Petrini J, Rocha MIP, Cardoso TF, Neto AZ, Coutinho LL, Mourão GB, Regitano LCA. Potential Biomarkers for Feed Efficiency-Related Traits in Nelore Cattle Identified by Co-expression Network and Integrative Genomics Analyses. Front Genet 2020;11:189. [PMID: 32194642 DOI: 10.3389/fgene.2020.00189] [Cited by in Crossref: 5] [Cited by in F6Publishing: 10] [Article Influence: 2.5] [Reference Citation Analysis]
20 Li X, Fu X, Yang G, Du M. Review: Enhancing intramuscular fat development via targeting fibro-adipogenic progenitor cells in meat animals. Animal 2020;14:312-21. [PMID: 31581971 DOI: 10.1017/S175173111900209X] [Cited by in Crossref: 12] [Cited by in F6Publishing: 25] [Article Influence: 4.0] [Reference Citation Analysis]
21 Ramayo-Caldas Y, Mármol-Sánchez E, Ballester M, Sánchez JP, González-Prendes R, Amills M, Quintanilla R. Integrating genome-wide co-association and gene expression to identify putative regulators and predictors of feed efficiency in pigs. Genet Sel Evol 2019;51:48. [PMID: 31477014 DOI: 10.1186/s12711-019-0490-6] [Cited by in Crossref: 10] [Cited by in F6Publishing: 16] [Article Influence: 3.3] [Reference Citation Analysis]
22 Messad F, Louveau I, Koffi B, Gilbert H, Gondret F. Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs. BMC Genomics 2019;20:659. [PMID: 31419934 DOI: 10.1186/s12864-019-6010-9] [Cited by in Crossref: 14] [Cited by in F6Publishing: 13] [Article Influence: 4.7] [Reference Citation Analysis]
23 Lu Z, Chu M, Li Q, Jin M, Fei X, Ma L, Zhang L, Wei C. Transcriptomic Analysis Provides Novel Insights into Heat Stress Responses in Sheep. Animals (Basel) 2019;9:E387. [PMID: 31238576 DOI: 10.3390/ani9060387] [Cited by in Crossref: 8] [Cited by in F6Publishing: 11] [Article Influence: 2.7] [Reference Citation Analysis]
24 Vigors S, O'Doherty JV, Bryan K, Sweeney T. A comparative analysis of the transcriptome profiles of liver and muscle tissue in pigs divergent for feed efficiency. BMC Genomics 2019;20:461. [PMID: 31170913 DOI: 10.1186/s12864-019-5740-z] [Cited by in Crossref: 12] [Cited by in F6Publishing: 17] [Article Influence: 4.0] [Reference Citation Analysis]
25 Piles M, Fernandez-Lozano C, Velasco-Galilea M, González-Rodríguez O, Sánchez JP, Torrallardona D, Ballester M, Quintanilla R. Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs. Genet Sel Evol 2019;51:10. [PMID: 30866799 DOI: 10.1186/s12711-019-0453-y] [Cited by in Crossref: 10] [Cited by in F6Publishing: 14] [Article Influence: 3.3] [Reference Citation Analysis]
26 Horodyska J, Hamill RM, Reyer H, Trakooljul N, Lawlor PG, McCormack UM, Wimmers K. RNA-Seq of Liver From Pigs Divergent in Feed Efficiency Highlights Shifts in Macronutrient Metabolism, Hepatic Growth and Immune Response. Front Genet 2019;10:117. [PMID: 30838035 DOI: 10.3389/fgene.2019.00117] [Cited by in Crossref: 10] [Cited by in F6Publishing: 17] [Article Influence: 3.3] [Reference Citation Analysis]
27 Horodyska J, Reyer H, Wimmers K, Trakooljul N, Lawlor PG, Hamill RM. Transcriptome analysis of adipose tissue from pigs divergent in feed efficiency reveals alteration in gene networks related to adipose growth, lipid metabolism, extracellular matrix, and immune response. Mol Genet Genomics 2019;294:395-408. [PMID: 30483895 DOI: 10.1007/s00438-018-1515-5] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]