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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Corte. Para informações adicionais entre em contato com cnpgc.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Corte. |
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Data corrente: |
04/01/2023 |
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Data da última atualização: |
04/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SANTANA, T. E. Z.; SILVA, J. C. F.; SILVA, L. O. C. da; ALVARENGA, A. B.; MENEZES, G. R. de O.; TORRES JUNIOR, R. A. de A.; DUARTE, M. de S.; SILVA, F. F. e. |
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Afiliação: |
TALITA ESTEFANI ZUNINO SANTANA, UNIVERSIDADE FEDERAL DE VIÇOSA; JOSE CLEYDSON F. SILVA, UNIVERSIDADE FEDERAL DE VIÇOSA; LUIZ OTAVIO CAMPOS DA SILVA, CNPGC; AMANDA BOTELHO ALVARENGA, PURDUE UNIVERSITY; GILBERTO ROMEIRO DE OLIVEIRA MENEZE, CNPGC; ROBERTO AUGUSTO DE A TORRES JUNIOR, CNPGC; MARCIO DE SOUZA DUARTE, UNIVERSITY GUELPH; FABYANO FONSECA E SILVA, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Genome-enabled classification of stayability in Nellore cattle under a machine learning framework. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Livestock Science, v. 260, article 104935, 2022. |
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ISSN: |
1871-1413 |
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DOI: |
https://doi.org/10.1016/j.livsci.2022.104935 |
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Idioma: |
Inglês |
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Conteúdo: |
Stayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number of SNPs in the genotype dataset was increased (i.e., 1K-, 3K-, or 5K-SNP panel). In short, NB outperformed the other algorithms considering, for example, the balanced accuracy (0.62 ± 0.01) and sensitivity (0.56 ± 0.02) metrics. In conclusion, the use of the 1K-SNP panel allowed efficient genomic classification and the NB algorithm outperformed the other methods as indicated by various classification metrics. To best of our knowledge, this is the first study using ML and genome-enabled classification of STAY in beef cattle. MenosStayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number ... Mostrar Tudo |
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Thesagro: |
Gado de Corte; Gado Nelore; Touro. |
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Thesaurus Nal: |
Beef cattle; Bulls; Daughters; Genome; Genomics; Nellore. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02992naa a2200337 a 4500
001 2150623
005 2023-01-04
008 2022 bl uuuu u00u1 u #d
022 $a1871-1413
024 7 $ahttps://doi.org/10.1016/j.livsci.2022.104935$2DOI
100 1 $aSANTANA, T. E. Z.
245 $aGenome-enabled classification of stayability in Nellore cattle under a machine learning framework.$h[electronic resource]
260 $c2022
520 $aStayability (STAY) is a binary trait with significant value economically. It measures both the cow`s reproductive performance and longevity simultaneously. Thus, STAY is one of the most important female selection criterion in Nellore beef cattle breeding programs. The "success" for STAY is defined as the ability of a cow to stay in the herd up to 76 months of age and to have at least three calve. Despite its importance, STAY has not been investigated under a machine learning (ML) framework, which might allow to intuitively capture linear and nonlinear relationships (e.g., non-additive effects) between a response variable and other predictor variables. In this study, we compared different ML tools using a genome-enabled approach to classify daughters (non-genotyped animals but with STAY records) of genotyped sires. In total, 44,626 STAY records from daughters of 559 bulls genotyped with the 777K SNP panel were available for this study. The genotyped data were subdivided into three SNP sets based on the top-ranked effect on STAY: 1K-, 3K-, and 5K-SNP panels. The following ML algorithms were evaluated: AdaBoost (ADA), Naïve Bayes (NB), Decision Tree (DT), Deep Neural Network (DNN), k-Nearest Neighbors (NN), Multi-Layer Perceptron Neural Network (MLP), and Support Vector Machine (SVM). The analyses were performed using free Scikit-learn for the Python programming language. No relevant improvements in the learning process of the evaluated algorithms were observed when the number of SNPs in the genotype dataset was increased (i.e., 1K-, 3K-, or 5K-SNP panel). In short, NB outperformed the other algorithms considering, for example, the balanced accuracy (0.62 ± 0.01) and sensitivity (0.56 ± 0.02) metrics. In conclusion, the use of the 1K-SNP panel allowed efficient genomic classification and the NB algorithm outperformed the other methods as indicated by various classification metrics. To best of our knowledge, this is the first study using ML and genome-enabled classification of STAY in beef cattle.
650 $aBeef cattle
650 $aBulls
650 $aDaughters
650 $aGenome
650 $aGenomics
650 $aNellore
650 $aGado de Corte
650 $aGado Nelore
650 $aTouro
700 1 $aSILVA, J. C. F.
700 1 $aSILVA, L. O. C. da
700 1 $aALVARENGA, A. B.
700 1 $aMENEZES, G. R. de O.
700 1 $aTORRES JUNIOR, R. A. de A.
700 1 $aDUARTE, M. de S.
700 1 $aSILVA, F. F. e
773 $tLivestock Science$gv. 260, article 104935, 2022.
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Embrapa Gado de Corte (CNPGC) |
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Classificação |
Cutter |
Registro |
Volume |
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Registro Completo
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
10/08/2011 |
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Data da última atualização: |
17/04/2018 |
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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: |
PEDROSA, F. O.; MONTEIRO, R. A.; WASSEM, R.; CRUZ, L. M.; AYUB, R. A.; COLAUTO, N. B.; FERNANDEZ, M. A.; FUNGARO, M. H. P.; GRISARD, E. C.; HUNGRIA, M.; MADEIRA, H. M. F.; NODARI, R. O.; OSAKU, C. A.; PETZL-ERLER, M. L.; TERENZI, H.; VIEIRA, L. G. E.; STEFFENS, M. B. R.; WEISS, V. A.; PEREIRA, L. F. P.; ALMEIDA, M. I. M.; ALVES, L. R.; MARIN, A.; ARAUJO, L. M.; BALSANELLI, E.; BAURA, V. A.; CHUBATSU, L. S.; FAORO, H.; FAVETTI, A.; FRIEDERMANN, G.; GLIENKE, C.; KARP, S.; KAVA-CORDEIRO, V.; RAITTZ, R. T.; RAMOS, H. J. O.; RIBEIRO, E. M. S. F.; RIGO, L. U.; ROCHA, S. N.; SCHWAB, S.; SILVA, A. G.; SOUZA, E. M.; MICHELLE Z. TADRA-SFEIR; TORRES, R. A.; DABUL, A. N. G.; SOARES, M. A. M.; GASQUES, L. S.; GIMENES, C. C. T.; VALLE, J. S.; CIFERRI, R. R.; CORREA, L. C.; MURACE, N. K.; PAMPHILE, J. A.; PATUSSI, E. V.; PRIOLI, A. J.; PRIOLI, S. M. A.; ROCHA, C. L. M. S. C.; ARANTES, O. M. N.; FURLANETO, M. C.; GODOY, L. P.; OLIVEIRA, C. E. C.; SATORI, D.; VILAS-BOAS, L. A.; WATANABE, M. A. E.; DAMBROS, B. P.; GUERRA, M. P.; MATHIONI, S. M.; SANTOS, K. L.; STEINDEL, M.; VERNAL, J.; BARCELLOS, F. G.; CAMPO, R. J.; CHUEIRE, L. M. O.; NICOLÁS, M. F.; PEREIRA-FERRARI, L.; SILVA, J. L. da C.; GIOPPO, N. M. R.; MARGARIDO, V. P.; MENCK-SOARES, M. A.; PINTO, F. G. S.; SIMÃO, R. de C. G.; TAKAHASHI, E. K.; YATES, M. G.; SOUZA, E. M. |
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Afiliação: |
FÁBIO O. PEDROSA, UFPR; ROSE ADELE MONTEIRO, UFPR; ROSELI WASSEM, UFPR; LEONARDO M. CRUZ, UFPR; RICARDO A. AYUB, UEPG; NELSON B. COLAUTO, Universidade Paranaense, Umuarama.; MARIA APARECIDA FERNANDEZ, UEM; MARIA HELENA P. FUNGARO, UEL; EDMUNDO C. GRISARD, UFSC; MARIANGELA HUNGRIA DA CUNHA, CNPSO; HUMBERTO M. F. MADEIRA8,, PUC Curitiba; RUBENS O. NODARI, UFSC; CLARICE A. OSAKU, UNIOESTE; MARIA LUIZA PETZL-ERLER, UFPR; HERNÁN TERENZI, UFSC; LUIZ G. E. VIEIRA, IAPAR; MARIA BERENICE R. STEFFENS, UFPR; VINICIUS A. WEISS, UFPR; LUIZ F. P. PEREIRA, IAPAR; MARINA I. M. ALMEIDA, UFPR; LYSANGELA R. ALVES, UFPR; ANELIS MARIN, UFPR; LUIZA MARIA ARAUJO, UFPR; EDUARDO BALSANELLI, UFPR; VALTER A. BAURA, UFPR; LEDA S. CHUBATSU, UFPR; HELISSON FAORO, UFPR; AUGUSTO FAVETTI, UFPR; GERALDO FRIEDERMANN, UFPR; CHIRLEI GLIENKE, UFPR; SUSAN KARP, UFPR; VANESSA KAVA-CORDEIRO, UFPR; ROBERTO T. RAITTZ, UFPR; HUMBERTO J. O. RAMOS, UFPR; ENILZE MARIA S. F. RIBEIRO, UFPR; LIU UN RIGO, UFPR; SAUL N. ROCHA, UFPR; STEFAN SCHWAB, UFPR; ANILDA G. SILVA, UFPR; ELIEL M. SOUZA, UFPR; TADRA-SFEIR, M. Z., UFPR; RODRIGO A. TORRES, UFPR; AUDREI N. G. DABUL, UEPG; MARIA ALBERTINA M. SOARES, UEPG; LUCIANO S. GASQUES, Universidade Paranaense, Umuarama; CIELA C. T. GIMENES, Universidade Paranaense, Umuarama.; JULIANA S. VALLE, Universidade Paranaense, Umuarama.; RICARDO R. CIFERRI, UEM; LUIZ C. CORREA, UEM; NORMA K. MURACE, UEM; JOÃO A. PAMPHILE, UEM; ELIANA VALÉRIA PATUSSI, UEM; ALBERTO J. PRIOLI, UEM; SONIA MARIA A. PRIOLI, UEM; CARMEM LÚCIA M. S. C. ROCHA, UEM; OLÍVIA MÁRCIA N. ARANTES, UEL; MÁRCIA CRISTINA FURLANETO, UEL; LEANDRO P. GODOY, UEL; CARLOS E. C. OLIVEIRA, UEL; DANIELE SATORI, UEL; LAURIVAL A. VILAS-BOAS, UEL; MARIA ANGÉLICA E. WATANABE, UEL; BIBIANA PAULA DAMBROS, UFSC; MIGUEL P. GUERRA, UFSC; SANDRA MARISA MATHIONI, UFSC; KARINE LOUISE SANTOS, UFSC; MARIO STEINDEL, UFSC; JAVIER VERNAL, UFSC; FERNANDO G. BARCELLOS, CNPSo - Pós-graduando; RUBENS J. CAMPO, CNPSo - Pesquisador aposentado; LIGIA MARIA DE OLIVEIRA CHUEIRE, CNPSO; MARISA FABIANA NICOLÁS, CNPSo - Pós-graduanda; LILIAN PEREIRA-FERRARI, PUC Curitiba-PR; JOSÉ L. DA CONCEICÃO SILVA, UNIOESTE; NEREIDA M. R. GIOPPO, UNIOESTE; VLADIMIR P. MARGARIDO, UNIOESTE; MARIA AMÉLIA MENCK-SOARES, UNIOESTE; FABIANA GISELE S. PINTO, UNIOESTE; RITA DE CÁSSIA G. SIMÃO, UNIOESTE; ELIZABETE K. TAKAHASHI, IAPAR; MARSHALL G. YATES, UFPR; EMANUEL M. SOUZA, UFPR. |
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Título: |
Genome of Herbaspirillum seropedicae Strain SmR1, a specialized diazotrophic endophyte of tropical grasses. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
PLoS Genetics, v. 7, n. 5, p. 1-10, may 2011. |
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DOI: |
10.1371/journal.pgen.1002064 |
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Idioma: |
Português |
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Conteúdo: |
The molecular mechanisms of plant recognition, colonization, and nutrient exchange between diazotrophic endophytes and plants are scarcely known. Herbaspirillum seropedicae is an endophytic bacterium capable of colonizing intercellular spaces of grasses such as rice and sugar cane. The genome of H. seropedicae strain SmR1 was sequenced and annotated by The Paraná State Genome Programme?GENOPAR. The genome is composed of a circular chromosome of 5,513,887 bp and contains a total of 4,804 genes. The genome sequence revealed that H. seropedicae is a highly versatile microorganism with capacity to metabolize a wide range of carbon and nitrogen sources and with possession of four distinct terminal oxidases. The genome contains a multitude of protein secretion systems, including type I, type II, type III, type V, and type VI secretion systems, and type IV pili, suggesting a high potential to interact with host plants. H. seropedicae is able to synthesize indole acetic acid as reflected by the four IAA biosynthetic pathways present. A gene coding for ACC deaminase, which may be involved in modulating the associated plant ethylene-signaling pathway, is also present. Genes for hemagglutinins/hemolysins/adhesins were found and may play a role in plant cell surface adhesion. These features may endow H. seropedicae with the ability to establish an endophytic life-style in a large number of plant species. |
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Palavras-Chave: |
Fixação nitrogênio. |
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Thesagro: |
Genoma; Graminea tropical. |
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Thesaurus NAL: |
Genome; Grasses; Herbaspirillum seropedicae; Nitrogen fixation. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/39544/1/plos-genetics.pdf
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Marc: |
LEADER 04596naa a2201189 a 4500
001 1897676
005 2018-04-17
008 2011 bl uuuu u00u1 u #d
024 7 $a10.1371/journal.pgen.1002064$2DOI
100 1 $aPEDROSA, F. O.
245 $aGenome of Herbaspirillum seropedicae Strain SmR1, a specialized diazotrophic endophyte of tropical grasses.
260 $c2011
520 $aThe molecular mechanisms of plant recognition, colonization, and nutrient exchange between diazotrophic endophytes and plants are scarcely known. Herbaspirillum seropedicae is an endophytic bacterium capable of colonizing intercellular spaces of grasses such as rice and sugar cane. The genome of H. seropedicae strain SmR1 was sequenced and annotated by The Paraná State Genome Programme?GENOPAR. The genome is composed of a circular chromosome of 5,513,887 bp and contains a total of 4,804 genes. The genome sequence revealed that H. seropedicae is a highly versatile microorganism with capacity to metabolize a wide range of carbon and nitrogen sources and with possession of four distinct terminal oxidases. The genome contains a multitude of protein secretion systems, including type I, type II, type III, type V, and type VI secretion systems, and type IV pili, suggesting a high potential to interact with host plants. H. seropedicae is able to synthesize indole acetic acid as reflected by the four IAA biosynthetic pathways present. A gene coding for ACC deaminase, which may be involved in modulating the associated plant ethylene-signaling pathway, is also present. Genes for hemagglutinins/hemolysins/adhesins were found and may play a role in plant cell surface adhesion. These features may endow H. seropedicae with the ability to establish an endophytic life-style in a large number of plant species.
650 $aGenome
650 $aGrasses
650 $aHerbaspirillum seropedicae
650 $aNitrogen fixation
650 $aGenoma
650 $aGraminea tropical
653 $aFixação nitrogênio
700 1 $aMONTEIRO, R. A.
700 1 $aWASSEM, R.
700 1 $aCRUZ, L. M.
700 1 $aAYUB, R. A.
700 1 $aCOLAUTO, N. B.
700 1 $aFERNANDEZ, M. A.
700 1 $aFUNGARO, M. H. P.
700 1 $aGRISARD, E. C.
700 1 $aHUNGRIA, M.
700 1 $aMADEIRA, H. M. F.
700 1 $aNODARI, R. O.
700 1 $aOSAKU, C. A.
700 1 $aPETZL-ERLER, M. L.
700 1 $aTERENZI, H.
700 1 $aVIEIRA, L. G. E.
700 1 $aSTEFFENS, M. B. R.
700 1 $aWEISS, V. A.
700 1 $aPEREIRA, L. F. P.
700 1 $aALMEIDA, M. I. M.
700 1 $aALVES, L. R.
700 1 $aMARIN, A.
700 1 $aARAUJO, L. M.
700 1 $aBALSANELLI, E.
700 1 $aBAURA, V. A.
700 1 $aCHUBATSU, L. S.
700 1 $aFAORO, H.
700 1 $aFAVETTI, A.
700 1 $aFRIEDERMANN, G.
700 1 $aGLIENKE, C.
700 1 $aKARP, S.
700 1 $aKAVA-CORDEIRO, V.
700 1 $aRAITTZ, R. T.
700 1 $aRAMOS, H. J. O.
700 1 $aRIBEIRO, E. M. S. F.
700 1 $aRIGO, L. U.
700 1 $aROCHA, S. N.
700 1 $aSCHWAB, S.
700 1 $aSILVA, A. G.
700 1 $aSOUZA, E. M.
700 1 $aMICHELLE Z. TADRA-SFEIR
700 1 $aTORRES, R. A.
700 1 $aDABUL, A. N. G.
700 1 $aSOARES, M. A. M.
700 1 $aGASQUES, L. S.
700 1 $aGIMENES, C. C. T.
700 1 $aVALLE, J. S.
700 1 $aCIFERRI, R. R.
700 1 $aCORREA, L. C.
700 1 $aMURACE, N. K.
700 1 $aPAMPHILE, J. A.
700 1 $aPATUSSI, E. V.
700 1 $aPRIOLI, A. J.
700 1 $aPRIOLI, S. M. A.
700 1 $aROCHA, C. L. M. S. C.
700 1 $aARANTES, O. M. N.
700 1 $aFURLANETO, M. C.
700 1 $aGODOY, L. P.
700 1 $aOLIVEIRA, C. E. C.
700 1 $aSATORI, D.
700 1 $aVILAS-BOAS, L. A.
700 1 $aWATANABE, M. A. E.
700 1 $aDAMBROS, B. P.
700 1 $aGUERRA, M. P.
700 1 $aMATHIONI, S. M.
700 1 $aSANTOS, K. L.
700 1 $aSTEINDEL, M.
700 1 $aVERNAL, J.
700 1 $aBARCELLOS, F. G.
700 1 $aCAMPO, R. J.
700 1 $aCHUEIRE, L. M. O.
700 1 $aNICOLÁS, M. F.
700 1 $aPEREIRA-FERRARI, L.
700 1 $aSILVA, J. L. da C.
700 1 $aGIOPPO, N. M. R.
700 1 $aMARGARIDO, V. P.
700 1 $aMENCK-SOARES, M. A.
700 1 $aPINTO, F. G. S.
700 1 $aSIMÃO, R. de C. G.
700 1 $aTAKAHASHI, E. K.
700 1 $aYATES, M. G.
700 1 $aSOUZA, E. M.
773 $tPLoS Genetics$gv. 7, n. 5, p. 1-10, may 2011.
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