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Registros recuperados : 29 | |
1. | | VIGNATO, B. S.; COUTINHO, L. L.; CESAR, A. S. M.; POLETI, M. D.; REGITANO, L. C. de A.; BALIEIRO, J. C. de C. Comparative muscle transcriptome associated with carcass traits of Nellore cattle. BMC Genomics, v. 18, n. 506, p. 1-13, 2017. Biblioteca(s): Embrapa Pecuária Sudeste. |
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2. | | OLIVEIRA, G. B.; KAPPELER, B. G.; CESAR, A. S. M.; POLETI, M. D.; REGITANO, L. C. de A.; COUTINHO, L. L. Micrornas expression profile and functional enrichment of Longissimus Dorsi muscle in Nellore cattle. In: CONGRESSO BRASILEIRO DE GENÉTICA, 61., 2015, Águas de Lindóia. Anais...Águas de Lindóia: Sociedade Brasileira de Genética, 2015. Biblioteca(s): Embrapa Pecuária Sudeste. |
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3. | | KAPPELER, B. I. G.; REGITANO, L. C. de A.; POLETI, M. D.; CESAR, A. S. M.; MOREIRA, G. C. M.; GASPARIN, G.; COUTINHO, L. L. MiRNAs differentially expressed in skeletal muscle of animals with divergent estimated breeding values for beef tenderness. Molecular Biology, v. 20, n. 1, p. 2-11, 2019. Biblioteca(s): Embrapa Pecuária Sudeste. |
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4. | | VIGNATO, B. S.; REGITANO, L. C. de A.; COUTINHO, L. L.; CESAR, A. S. M.; POLETI, M. D.; BALIEIRO, J. C. de C. NEDD4: a putative candidate gene for ribeye area in Nellore steers. In: WORKSHOP ON OMICS STRATEGIES APPLIED TO LIVESTOCK SCIENCE, 1., 2017, Piracicaba, SP. Proceedings... São Carlos, SP: Embrapa Pecuária Sudeste, 2017. p. 15. (Embrapa Pecuária Sudeste. Documentos, 125) Editores: Luiz Lehmann Coutinho, ESALQ/USP; Luciana Correia de Almeida Regitano, Embrapa Pecuária Sudeste; Gerson Barreto Mourão, ESALQ/USP; Aline Silva Mello Cesar, ESALQ/USP; Bárbara Silva Vignato, FZEA/USP; Mirele Daiana Poleti,... Biblioteca(s): Embrapa Pecuária Sudeste. |
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5. | | COUTINHO, L. L.; MOROSINI, N. S.; CESAR, A. S. M.; POLETI, M. D.; REGITANO, L. C. de A.; MARGARIDO, G. R. A. Identification and characterization of euchromatic regions associated with gene expression and intramuscular fat in Nelore cattle. Journal of Animal Science, v. 96, suppl. S3, p. 233-234, 2019. Biblioteca(s): Embrapa Pecuária Sudeste. |
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6. | | PÉRTILLE, F.; IBELLI, A. M. G.; SHARIF, M. E.; POLETI, M. D.; FRÖHLICH, A. S.; REZAEI, S.; LEDUR, M. C.; JENSEN, P.; GUERRERO-BOSAGNA, C.; COUTINHO. L. L. Putative epigenetic biomarkers of stress in red blood cells of chickens reared across different biomes. Frontiers in Genetics, 20 Nov 2020. Biblioteca(s): Embrapa Suínos e Aves. |
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7. | | OLIVEIRA, G. B. de; CESAR, A. S. M.; FELÍCIO, A. M.; KAPPELER, B. I. G.; POLETI, M. D.; REGITANO, L. C. de A.; COUTINHO, L. L. Evidence of miRNA regulation of intramuscular fat deposition in beef cattle. In: PLANT AND ANIMAL GENOME CONFERENCE, 24., 2016, San Diego. Anais... San Diego: PAG, 2016. Biblioteca(s): Embrapa Pecuária Sudeste. |
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8. | | VIGNATO, B. S.; COUTINHO, L. L.; CESAR, A. S. M.; POLETI, M. D.; REGITANO, L. C. de A.; BALIEIRO, J. C. de C. Gene co-expression network analysis associated with carcass traits in Nellore steers. In: ANNUAL CONFERENCE OF AUSTRALIAN MARINE SCIENCES ASSOCIATION, 57., 2017, Darwin, Australia. Proceedings... Darwin, Australia: AMSA, 2017. Biblioteca(s): Embrapa Pecuária Sudeste. |
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9. | | VIGNATO B. S.; COUTINHO, L. L.; POLETI, M. D.; CESAR, A. S. M.; MONCAU, C. T.; REGITANO, L. C. de A.; BALIEIRO, J. C. C. Gene co-expression networks associated with carcass traits reveal new pathways for muscle and fat deposition in Nelore cattle. Genomics, v. 20, n. 32, p. 2-13, 2019. Biblioteca(s): Embrapa Pecuária Sudeste. |
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10. | | OLIVEIRA, G. B. de; CESAR, A. S. M.; FELICIO, A. M.; POLETI, M. D.; REGITANO, L. C. de A.; COUTINHO, L. L. Gene network regulated by microRNAs suggests modulation of fat deposition in cattle. Journal of Animal Science, v. 94, e-suppl. 5; Journal of Dairy Science, v. 99, e-suppl. 1, p. 159, jul. 2016. Biblioteca(s): Embrapa Pecuária Sudeste. |
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11. | | POLETI, M. D.; SIMAS, R. C.; CESAR, A. S. M.; ANDRADE, S. C. S.; SOUZA, G. H. M. F.; CAMERON, L. C.; REGITANO, L. C. de A.; COUTINHO, L. L. Label-free MSE proteomic analysis of the bovine skeletal muscle: new approach for meat tenderness evaluation. J. Anim. Sci Vol. 94, E-Suppl. 5/J. Dairy Sci. Vol. 99, E-Suppl. 1, 2016. Biblioteca(s): Embrapa Pecuária Sudeste. |
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12. | | VIGNATO, B. S.; CESAR, A. S. M.; AFONSO, J.; MOREIRA, G. C. M.; POLETI, M. D.; PETRINI, J.; GARCIA, I. S.; CLEMENTE, L. G.; MOURÃO, G. B.; REGITANO, L. C. de A.; COUTINHO, L. L. Integrative analysis between genome-wide association study and expression quantitative trait Loci reveals bovine muscle gene expression regulatory polymorphisms associated with intramuscular fat and backfat thickness. Frontiers in Genetics, v. 13, 935238, aug. 2022. 15 p. Biblioteca(s): Embrapa Pecuária Sudeste. |
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13. | | OLIVEIRA, G. B.; REGITANO, L. C. de A.; CESAR, A. S. M.; REECY, J. M.; DEGAKI, K. Y.; POLETI, M. D.; FELICIO, A. M.; KOLTES, J. E.; COUTINHO, L. L. Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle. BMC Genomics, v. 19, n. 126, p. 2-16, 2018. Biblioteca(s): Embrapa Pecuária Sudeste. |
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14. | | CÔNSOLO, N. R. B.; BUARQUE, V. L. M.; SILVA, J.; POLETI, M. D.; BARBOSA, L. C. G. S.; HIGUERA-PADILLA, A.; GOMEZ, J. F. M.; COLNAGO, L. A.; GERRARD, D. E.; SARAN NETO, A.; SILVA, S. L. Muscle and liver metabolomic signatures associated with residual feed intake in Nellore cattle. Animal Feed Science and Technology, v. 271, 114757, 2021. Biblioteca(s): Embrapa Instrumentação. |
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15. | | NOVAIS, F. J. DE; YU, H.; CESAR, A. S. M.; MOMEN, M.; POLETI, M. D.; PETRY, B.; MOURÃO, G. B.; REGITANO, L. C. de A.; MOROTA, G.; COUTINHO, L. L. Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle. Frontiers in Genetics, v. 13, 948240, oct. 2022. 14 p. Biblioteca(s): Embrapa Pecuária Sudeste. |
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16. | | OLIVEIRA, P. S. N. de; CESAR, A. S. M.; OLIVEIRA, G. B. de; TIZIOTO, P. C.; POLETI, M. D.; DINIZ, W. J. da S.; LIMA, A. O. de; REECY, J. M.; COUTINHO, L. L.; REGITANO, L. C. de A. miRNAs related to fatty acids composition in Nellore cattle. Journal of Animal Science, v. 94, e-suppl. 5; Journal of Dairy Science, v. 99, e-suppl. 1, p. 159, jul. 2016. p. 160. Biblioteca(s): Embrapa Pecuária Sudeste. |
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17. | | DEFANT, H.; MEIRA, A. N.; COUTINHO, L. L.; REGITANO, L. C. de A.; POLETI, M. D.; MOREIRA, G. C. M.; PADUAN, M.; MARIANI, P.; ZERLOTINI NETO, A.; MOURÃO, G. B.; CESAR, A. S. M. Novel polymorphisms in the PLIN2 gene of Nellore cattle. Genetics and Molecular Research, v. 18, n. 3, 2019. Não paginado. Na publicação: Luciana CA Regitano, Adhemar Zerlotini. gmr16039963. Biblioteca(s): Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
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18. | | POLETI, M. D.; REGITANO, L. C. de A.; SOUSA, G. H. M. F.; CESAR, A. S. M.; SIMAS, R. C.; SILVA-VIGNATO, B.; OLIVEIRA, G. B.; ANDRADE, S. C. S.; CAMERON, L. C.; COUTINHO, L. L. Longissimus dorsi muscle label-free quantitative proteomic reveals biological mechanisms associated with intramuscular fat deposition. Journal of Proteomics, v. 179, p. 30-41, 2018. Biblioteca(s): Embrapa Pecuária Sudeste. |
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19. | | CARVALHO, M. E.; GASPARIN, G.; POLETI, M. D.; ROSA, A. F.; BALIEIRO, J. C. C.; LABATE, C. A.; NASSU, R. T.; TULLIO, R. R.; REGITANO, L. C. de A.; MOURÃO, G. B.; COUTINHO, L. L. Heat shock and structural proteins associated with meat tenderness in Nellore beef cattle, a Bos indicus breed. Meat Science, v. 96, n. 3, p. 1318-1324, mar. 2014. Biblioteca(s): Embrapa Pecuária Sudeste. |
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20. | | POLETI, M. D.; REGITANO, L. C. de A.; SOUZA, G. H. M. F.; CESAR, A. S. M.; SIMAS, R. C.; SILVA-VIGNATO, B.; OLIVEIRA, G. B.; ANDRADE, S. C. S; CAMERON, L. C.; COUTINHO, L. L. Data from proteomic analysis of bovine Longissimus dorsi muscle associated with intramuscular fat content. Data in Brief, v. 19, p. 1314-1317, 2018. Biblioteca(s): Embrapa Pecuária Sudeste. |
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Registros recuperados : 29 | |
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Registro Completo
Biblioteca(s): |
Embrapa Pecuária Sudeste. |
Data corrente: |
17/11/2022 |
Data da última atualização: |
17/11/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
NOVAIS, F. J. DE; YU, H.; CESAR, A. S. M.; MOMEN, M.; POLETI, M. D.; PETRY, B.; MOURÃO, G. B.; REGITANO, L. C. de A.; MOROTA, G.; COUTINHO, L. L. |
Afiliação: |
FRANCISCO JOSÉ DE NOVAIS, Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil; HAIPENG YU, Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States; ALINE SILVA MELLO CESAR, Department of Agri-Food Industry, Food and Nutrition, University of São Paulo, Piracicaba, Brazil; MEHDI MOMEN, Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States; MIRELE DAIANA POLETI, Department of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, Brazil; BRUNA PETRY, Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil; GERSON BARRETO MOURÃO, Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE; GOTA MOROTA, Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States; LUIZ LEHMANN COUTINHO, Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil. |
Título: |
Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Frontiers in Genetics, v. 13, 948240, oct. 2022. |
Páginas: |
14 p. |
DOI: |
https://doi.org/10.3389/fgene.2022.948240 |
Idioma: |
Inglês |
Conteúdo: |
Data integration using hierarchical analysis based on the central dogma or common pathway enrichment analysis may not reveal non-obvious relationships among omic data. Here, we applied factor analysis (FA) and Bayesian network (BN) modeling to integrate different omic data and complex traits by latent variables (production, carcass, and meat quality traits). A total of 14 latent variables were identified: five for phenotype, three for miRNA, four for protein, and two for mRNA data. Pearson correlation coefficients showed negative correlations between latent variables miRNA 1 (mirna1) and miRNA 2 (mirna2) (-0.47), ribeye area (REA) and protein 4 (prot4) (-0.33), REA and protein 2 (prot2) (-0.3), carcass and prot4 (-0.31), carcass and prot2 (-0.28), and backfat thickness (BFT) and miRNA 3 (mirna3) (?0.25). Positive correlations were observed among the four protein factors (0.45?0.83): between meat quality and fat content (0.71), fat content and carcass (0.74), fat content and REA (0.76), and REA and carcass (0.99). BN presented arcs from the carcass, meat quality, prot2, and prot4 latent variables to REA; from meat quality, REA, mirna2, and gene expression mRNA1 to fat content; from protein 1 (prot1) and mirna2 to protein 5 (prot5); and from prot5 and carcass to prot2. The relations of protein latent variables suggest new hypotheses about the impact of these proteins on REA. The network also showed relationships among miRNAs and nebulin proteins. REA seems to be the central node in the network, influencing carcass, prot2, prot4, mRNA1, and meat quality, suggesting that REA is a good indicator of meat quality. The connection among miRNA latent variables, BFT, and fat content relates to the influence of miRNAs on lipid metabolism. The relationship between mirna1 and prot5 composed of isoforms of nebulin needs further investigation. The FA identified latent variables, decreasing the dimensionality and complexity of the data. The BN was capable of generating interrelationships among latent variables from different types of data, allowing the integration of omics and complex traits and identifying conditional independencies. Our framework based on FA and BN is capable of generating new hypotheses for molecular research, by integrating different types of data and exploring non-obvious relationships. MenosData integration using hierarchical analysis based on the central dogma or common pathway enrichment analysis may not reveal non-obvious relationships among omic data. Here, we applied factor analysis (FA) and Bayesian network (BN) modeling to integrate different omic data and complex traits by latent variables (production, carcass, and meat quality traits). A total of 14 latent variables were identified: five for phenotype, three for miRNA, four for protein, and two for mRNA data. Pearson correlation coefficients showed negative correlations between latent variables miRNA 1 (mirna1) and miRNA 2 (mirna2) (-0.47), ribeye area (REA) and protein 4 (prot4) (-0.33), REA and protein 2 (prot2) (-0.3), carcass and prot4 (-0.31), carcass and prot2 (-0.28), and backfat thickness (BFT) and miRNA 3 (mirna3) (?0.25). Positive correlations were observed among the four protein factors (0.45?0.83): between meat quality and fat content (0.71), fat content and carcass (0.74), fat content and REA (0.76), and REA and carcass (0.99). BN presented arcs from the carcass, meat quality, prot2, and prot4 latent variables to REA; from meat quality, REA, mirna2, and gene expression mRNA1 to fat content; from protein 1 (prot1) and mirna2 to protein 5 (prot5); and from prot5 and carcass to prot2. The relations of protein latent variables suggest new hypotheses about the impact of these proteins on REA. The network also showed relationships among miRNAs and nebulin proteins. REA seems to be the central no... Mostrar Tudo |
Palavras-Chave: |
Bayesian network; Latent variables; Omics data. |
Thesaurus NAL: |
Factor analysis; Meat quality. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1148406/1/MultiOmicDataIntegration.pdf
|
Marc: |
LEADER 03242naa a2200313 a 4500 001 2148406 005 2022-11-17 008 2022 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.3389/fgene.2022.948240$2DOI 100 1 $aNOVAIS, F. J. DE 245 $aMulti-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle.$h[electronic resource] 260 $c2022 300 $a14 p. 520 $aData integration using hierarchical analysis based on the central dogma or common pathway enrichment analysis may not reveal non-obvious relationships among omic data. Here, we applied factor analysis (FA) and Bayesian network (BN) modeling to integrate different omic data and complex traits by latent variables (production, carcass, and meat quality traits). A total of 14 latent variables were identified: five for phenotype, three for miRNA, four for protein, and two for mRNA data. Pearson correlation coefficients showed negative correlations between latent variables miRNA 1 (mirna1) and miRNA 2 (mirna2) (-0.47), ribeye area (REA) and protein 4 (prot4) (-0.33), REA and protein 2 (prot2) (-0.3), carcass and prot4 (-0.31), carcass and prot2 (-0.28), and backfat thickness (BFT) and miRNA 3 (mirna3) (?0.25). Positive correlations were observed among the four protein factors (0.45?0.83): between meat quality and fat content (0.71), fat content and carcass (0.74), fat content and REA (0.76), and REA and carcass (0.99). BN presented arcs from the carcass, meat quality, prot2, and prot4 latent variables to REA; from meat quality, REA, mirna2, and gene expression mRNA1 to fat content; from protein 1 (prot1) and mirna2 to protein 5 (prot5); and from prot5 and carcass to prot2. The relations of protein latent variables suggest new hypotheses about the impact of these proteins on REA. The network also showed relationships among miRNAs and nebulin proteins. REA seems to be the central node in the network, influencing carcass, prot2, prot4, mRNA1, and meat quality, suggesting that REA is a good indicator of meat quality. The connection among miRNA latent variables, BFT, and fat content relates to the influence of miRNAs on lipid metabolism. The relationship between mirna1 and prot5 composed of isoforms of nebulin needs further investigation. The FA identified latent variables, decreasing the dimensionality and complexity of the data. The BN was capable of generating interrelationships among latent variables from different types of data, allowing the integration of omics and complex traits and identifying conditional independencies. Our framework based on FA and BN is capable of generating new hypotheses for molecular research, by integrating different types of data and exploring non-obvious relationships. 650 $aFactor analysis 650 $aMeat quality 653 $aBayesian network 653 $aLatent variables 653 $aOmics data 700 1 $aYU, H. 700 1 $aCESAR, A. S. M. 700 1 $aMOMEN, M. 700 1 $aPOLETI, M. D. 700 1 $aPETRY, B. 700 1 $aMOURÃO, G. B. 700 1 $aREGITANO, L. C. de A. 700 1 $aMOROTA, G. 700 1 $aCOUTINHO, L. L. 773 $tFrontiers in Genetics$gv. 13, 948240, oct. 2022.
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