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| Registros recuperados : 157 | |
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| 83. | | KLAI, A.; INARRA, F.; MARTINS, F. M.; VILELA, F. A.; VANNUCI, F. S.; PIRES, R. P.; NUNES, V. P.; MOURÃO, G. B.; MATTOS, C. M.; PEIXOTO JÚNIOR, K. C. Avaliação do desempenho de caprinos castrados alimentados com e sem Aguapé (Eichhornia crassipes). In: CONGRESSO LATINOAMERICANO DE BUIATRIA, 11.; CONGRESSO BRASILEIRO DE BUIATRIA, 5.; CONGRESSO NORDESTINO DE BUIATRIA, 3., 2003, Salvador. Sanidade, base da economia pecuária: programa final: livro de resumos. Salvador: Associação Brasileira de Buiatria, 2003. p. 83-84. ref. 193.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 84. | | PEIXOTO JÚNIOR, K. C.; KLAI, A.; INARRA, F.; MARTINS, F. M.; VILELA, F. A.; VANNUCI, F. S.; PIRES, R. P.; NUNES, V. P.; MOURÃO, G. B.; MATTOS, C. M. Avaliação do desempenho de caprinos inteiros alimentados com níveis crescentes de Aguapé (Eichhornia crassipes). In: CONGRESSO LATINOAMERICANO DE BUIATRIA, 11.; CONGRESSO BRASILEIRO DE BUIATRIA, 5.; CONGRESSO NORDESTINO DE BUIATRIA, 3., 2003, Salvador. Sanidade, base da economia pecuária: programa final: livro de resumos. Salvador: Associação Brasileira de Buiatria, 2003. p. 84. ref. 194.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 85. | | MEIRA, A. N.; MORIEIRA, G. C. M.; COUTINHO, L. L.; MOURAO, G. B.; AZEVEDO, H. C.; MUNIZ, E. N.; MACHADO, A. L.; SOUSA JUNIOR, L. P.; PEDROSA, V. B.; PINTO, L. F. B. Carcass and commercial cut yield of Santa Ines sheep affected by polymorphisms of the LEP gene. Small Ruminant Research, v. 166, p. 121-128, Sep. 2018.| Biblioteca(s): Embrapa Tabuleiros Costeiros. |
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| 86. | | MALHEIROS, J. M.; CORREIA, B. S. B.; CERIBELI, C.; CARDOSO, D. R.; COLNAGO, L. A.; BOGUSZ JUNIOR, S.; REECY, J. M.; MOURÃO, G. B.; COUTINHO, L. L.; PALHARES, J. C. P.; BERNDT, A.; REGITANO, L. C. de A. Comparative untargeted metabolome analysis of ruminal fluid and feces of Nelore steers (Bos indicus). Scientific Reports, v.11, n.1, e12752, 2021. 13 p.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 87. | | MALHEIROS, J. M.; CORREIA, B. S. B.; CERIBELI, C.; CARDOSO, D. R.; COLNAGO, L. A.; BOGUSZ JUNIOR, S.; REECY, J. M.; MOURÃO, G. B.; COUTINHO, L. L.; PALHARES, J. C. P.; BERNDT, A.; REGITANO, L. C. de A. Comparative untargeted metabolome analysis of ruminal fuid and feces of Nelore steers (Bos indicus). Scientific Reports, v. 11, n. 1, e12752, 2021.| Biblioteca(s): Embrapa Instrumentação; Embrapa Pecuária Sudeste. |
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| 88. | | 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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| 89. | | SALEM, M. M. I.; MOURÃO, G. B.; PACKER, I. U.; PINTO, L. F. B.; MACHADO, M. A.; GASPARIN, G.; AZEVEDO, A. L. S.; VERNEQUE, R. da S.; REGITANO, L. C. de A. Identification of quantitative trait loci for growth curve in crossbred dairy cattle population. In: WORLD CONGRESS ON GENETICS APPLIED TO LIVESTOCK PRODUCTION, 9., 2010, Leipzig. Proceedings... Leipzig: German Society fo Animal Science, 2010. 1 CD-ROM.| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| Registros recuperados : 157 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
17/11/2022 |
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Data da última atualização: |
17/11/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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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. |
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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. |
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Título: |
Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Frontiers in Genetics, v. 13, 948240, oct. 2022. |
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Páginas: |
14 p. |
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DOI: |
https://doi.org/10.3389/fgene.2022.948240 |
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Idioma: |
Inglês |
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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 |
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Palavras-Chave: |
Bayesian network; Latent variables; Omics data. |
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Thesaurus NAL: |
Factor analysis; Meat quality. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1148406/1/MultiOmicDataIntegration.pdf
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Marc: |
LEADER 03242naa a2200313 a 4500
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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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