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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
14/02/2022 |
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Actualizado : |
14/02/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
COSTA, A.; CORALLO, B.; AMARELLE, V.; STEWART, S.; PAN, D.; TISCORNIA, S.; FABIANO, E. |
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Afiliación : |
ANDRÉS COSTA, Biochemistry and Microbial Genomics Department, Instituto de Investigaciones Biológicas Clemente Estable, Ministerio de Educación y Cultura, Montevideo, Uruguay; BELÉN CORALLO, Sección Micología, Facultad de Ciencias-Universidad de la República, Montevideo, Uruguay; VANESA AMARELLE, Biochemistry and Microbial Genomics Department, Instituto de Investigaciones Biológicas Clemente Estable, Ministerio de Educación y Cultura, Montevideo, Uruguay; SILVINA MARIA STEWART SONEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; DINORAH PAN, Sección Micología, Facultad de Ciencias-Universidad de la República, Montevideo, Uruguay; SUSANA TISCORNIA, Sección Micología, Facultad de Ciencias-Universidad de la República, Montevideo, Uruguay; ELENA FABIANO, Biochemistry and Microbial Genomics Department, Instituto de Investigaciones Biológicas Clemente Estable, Ministerio de Educación y Cultura, Montevideo, Uruguay. |
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Título : |
Paenibacillus sp. strain UY79, isolated from a root nodule of Arachis villosa, displays a broad spectrum of antifungal activity. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Applied and Environmental Microbiology, 2022, Volume 88. Issue 2, e01645-21. doi: https://doi.org/10.1128/AEM.01645-21 |
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ISSN : |
0099-2240 |
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DOI : |
10.1128/AEM.01645-21 |
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Idioma : |
Inglés |
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Notas : |
Article history: |
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Contenido : |
ABSTRACT.- A nodule-inhabiting Paenibacillus sp. strain (UY79) isolated from wild peanut (Arachis villosa) was screened for its antagonistic activity against diverse fungi and oomycetes (Botrytis cinerea, Fusarium verticillioides, Fusarium oxysporum, Fusarium graminearum, Fusarium semitectum, Macrophomina phaseolina, Phomopsis longicolla, Pythium ultimum, Phytophthora sojae, Rhizoctonia solani, Sclerotium rolfsii, and Trichoderma atroviride). The results obtained show that Paenibacillus sp. UY79 was able to antagonize these fungi/oomycetes and that agar-diffusible compounds and volatile compounds (different from HCN) participate in the antagonism exerted. Acetoin, 2, 3-butanediol, and 2-methyl- 1-butanol were identified among the volatile compounds produced by strain UY79 with possible antagonistic activity against fungi/oomycetes. Paenibacillus sp. strain UY79 did not affect symbiotic association or growth promotion of alfalfa plants when coinoculated with rhizobia. By whole-genome sequence analysis, we determined that strain UY79 is a new species of Paenibacillus within the Paenibacillus polymyxa complex. Diverse genes putatively involved in biocontrol activity were identified in the UY79 genome. Furthermore, according to genome mining and antibiosis assays, strain UY79 would have the capability to modulate the growth of bacteria commonly found in soil/plant communities.
© 2022 American Society for Microbiology. All Rights Reserved. |
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Palabras claves : |
Biocontrol; Fusaricidin; Nodule-inhabiting bacteria; Paenibacillus; Volatile compounds; Volatile metabolites. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 02417naa a2200301 a 4500
001 1062745
005 2022-02-14
008 2022 bl uuuu u00u1 u #d
022 $a0099-2240
024 7 $a10.1128/AEM.01645-21$2DOI
100 1 $aCOSTA, A.
245 $aPaenibacillus sp. strain UY79, isolated from a root nodule of Arachis villosa, displays a broad spectrum of antifungal activity.$h[electronic resource]
260 $c2022
500 $aArticle history:
520 $aABSTRACT.- A nodule-inhabiting Paenibacillus sp. strain (UY79) isolated from wild peanut (Arachis villosa) was screened for its antagonistic activity against diverse fungi and oomycetes (Botrytis cinerea, Fusarium verticillioides, Fusarium oxysporum, Fusarium graminearum, Fusarium semitectum, Macrophomina phaseolina, Phomopsis longicolla, Pythium ultimum, Phytophthora sojae, Rhizoctonia solani, Sclerotium rolfsii, and Trichoderma atroviride). The results obtained show that Paenibacillus sp. UY79 was able to antagonize these fungi/oomycetes and that agar-diffusible compounds and volatile compounds (different from HCN) participate in the antagonism exerted. Acetoin, 2, 3-butanediol, and 2-methyl- 1-butanol were identified among the volatile compounds produced by strain UY79 with possible antagonistic activity against fungi/oomycetes. Paenibacillus sp. strain UY79 did not affect symbiotic association or growth promotion of alfalfa plants when coinoculated with rhizobia. By whole-genome sequence analysis, we determined that strain UY79 is a new species of Paenibacillus within the Paenibacillus polymyxa complex. Diverse genes putatively involved in biocontrol activity were identified in the UY79 genome. Furthermore, according to genome mining and antibiosis assays, strain UY79 would have the capability to modulate the growth of bacteria commonly found in soil/plant communities. © 2022 American Society for Microbiology. All Rights Reserved.
653 $aBiocontrol
653 $aFusaricidin
653 $aNodule-inhabiting bacteria
653 $aPaenibacillus
653 $aVolatile compounds
653 $aVolatile metabolites
700 1 $aCORALLO, B.
700 1 $aAMARELLE, V.
700 1 $aSTEWART, S.
700 1 $aPAN, D.
700 1 $aTISCORNIA, S.
700 1 $aFABIANO, E.
773 $tApplied and Environmental Microbiology, 2022, Volume 88. Issue 2, e01645-21. doi: https://doi.org/10.1128/AEM.01645-21
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
17/11/2023 |
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Actualizado : |
17/11/2023 |
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Tipo de producción científica : |
Poster |
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Autor : |
FARIELLO, M.I.; ARBOLEYA, L.; BELZARENA, D.; DE LOS SANTOS, L.; ELENTER, J.; ETCHEBARNE, G.; HOUNIE, I.; CIAPPESONI, G.; NAVAJAS, E.; LECUMBERRY, F. |
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Afiliación : |
MARIA INÉS FARIELLO, Facultad de Ingeniería, Universidad de la República, Uruguay; Institut Pasteur de Montevideo, Uruguay; LUCÍA ARBOLEYA, Facultad de Ingeniería, Universidad de la República, Uruguay; DIEGO BELZARENA, Facultad de Ingeniería, Universidad de la República, Uruguay; LEONARDO DE LOS SANTOS, Facultad de Ingeniería, Universidad de la República, Uruguay; JUAN ELENTER, Facultad de Ingeniería, Universidad de la República, Uruguay; GUILLERMO ETCHEBARNE, Facultad de Ingeniería, Universidad de la República, Uruguay; IGNACIO HOUNIE, Facultad de Ingeniería, Universidad de la República, Uruguay; CARLOS GABRIEL CIAPPESONI SCARONE, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ELLY ANA NAVAJAS VALENTINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FEDERICO LECUMBERRY, Facultad de Ingeniería, Universidad de la República, Uruguay; Institut Pasteur de Montevideo, Uruguay. |
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Título : |
Something old, something new, something borrowed : Evaluation of different neural network architectures for genomic prediction. [poster] |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
En: Plant & Animal Genome Conference : PAG 30, San Diego, California, USA, 13-18 january 2023. |
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Descripción física : |
Editorial: Plant and Animal Genome Conference (PAG). |
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Idioma : |
Inglés |
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Notas : |
Este trabajo fue parcialmente financiado por la Universidad de la República y el proyecto ANII FDA 1_2018_1_154364. -- LICENCIA: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0). |
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Contenido : |
ABSTRACT.- Genome enabled prediction of complex traits aims to predict a measurable characteristic of an organism using their genetic information. In the present work we address diverse traits and organisms including Yeast growth, Wheat yield, Jersey bull fertility and various Holstein cattle milk-related traits. We benchmark several popular Machine Learning models: Bayesian and penalized linear regressions, kernel methods, and Decision Tree ensembles. Through exhaustive hyperparameter tuning we outperform state-of-the-art results in most datasets. We also evaluate two codification techniques for input data and perform ablation studies to assess robustness to genetic markers - i.e input features- elimination. We also explore different Deep Learning architectures for this task. We propose and evaluate Convolutional Neural Network (CNN) architectures, showing that using residual connections improves performance but that in some cases Fully Connected Networks outperform CNNs. We link this to the fact that absolute positions are relevant in genomes, and thus, CNN's translational equivariance may not be an adequate inductive bias for tackling this problem. We evaluate Graph Neural Network (GNN) architectures by formulating trait prediction as a node regression problem on a population graph, where each node represents an individual, and edges association between their genetic information. We evaluate the transferability of these graphical models and find that the extent to which they exploit neighborhood information is limited. By combining CNN and GNN architectures, we could outperform all other models for predicting milk yield in Holstein cattle.The methods that are based on neural networks can be computationally demanding when used on high density chips or sequence data, even more when fully connected layers are used. To overcome this problem, we propose to obtain a new representation of the input vector by using the intermediate representation (code) of an Autoencoder (AE). Currently we are evaluating the performance benchmarks. Another common issue when using these databases is the missing data or the combination of chips of different SNP's numbers. Again, we propose to use AE for imputing the missing values. One of the main focuses of this work was to explore the feasibility of employing modern deep learning architectures in Genomic Prediction. In this regard, it was possible to train highly over-parameterized architectures and still obtain good generalization. For some datasets and traits, these models outperform all others. However, this did not hold for all the models, traits and datasets studied. Besides, whether the gains in performance outweigh the increase in model size and thus its training and inference computational cost, and lack of interpretability, calls for further discussion. MenosABSTRACT.- Genome enabled prediction of complex traits aims to predict a measurable characteristic of an organism using their genetic information. In the present work we address diverse traits and organisms including Yeast growth, Wheat yield, Jersey bull fertility and various Holstein cattle milk-related traits. We benchmark several popular Machine Learning models: Bayesian and penalized linear regressions, kernel methods, and Decision Tree ensembles. Through exhaustive hyperparameter tuning we outperform state-of-the-art results in most datasets. We also evaluate two codification techniques for input data and perform ablation studies to assess robustness to genetic markers - i.e input features- elimination. We also explore different Deep Learning architectures for this task. We propose and evaluate Convolutional Neural Network (CNN) architectures, showing that using residual connections improves performance but that in some cases Fully Connected Networks outperform CNNs. We link this to the fact that absolute positions are relevant in genomes, and thus, CNN's translational equivariance may not be an adequate inductive bias for tackling this problem. We evaluate Graph Neural Network (GNN) architectures by formulating trait prediction as a node regression problem on a population graph, where each node represents an individual, and edges association between their genetic information. We evaluate the transferability of these graphical models and find that the extent to which t... Presentar Todo |
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Palabras claves : |
Deep learning; Predicción genómica; Signal processing. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/17417/1/Fariello-PAG-2023-FABDEEHCNL23.pdf
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Marc : |
LEADER 03979nam a2200277 a 4500
001 1064372
005 2023-11-17
008 2023 bl uuuu u00u1 u #d
100 1 $aFARIELLO, M.I.
245 $aSomething old, something new, something borrowed$bEvaluation of different neural network architectures for genomic prediction. [poster]$h[electronic resource]
260 $aEn: Plant & Animal Genome Conference : PAG 30, San Diego, California, USA, 13-18 january 2023.$c2023
300 $cEditorial: Plant and Animal Genome Conference (PAG).
500 $aEste trabajo fue parcialmente financiado por la Universidad de la República y el proyecto ANII FDA 1_2018_1_154364. -- LICENCIA: Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0).
520 $aABSTRACT.- Genome enabled prediction of complex traits aims to predict a measurable characteristic of an organism using their genetic information. In the present work we address diverse traits and organisms including Yeast growth, Wheat yield, Jersey bull fertility and various Holstein cattle milk-related traits. We benchmark several popular Machine Learning models: Bayesian and penalized linear regressions, kernel methods, and Decision Tree ensembles. Through exhaustive hyperparameter tuning we outperform state-of-the-art results in most datasets. We also evaluate two codification techniques for input data and perform ablation studies to assess robustness to genetic markers - i.e input features- elimination. We also explore different Deep Learning architectures for this task. We propose and evaluate Convolutional Neural Network (CNN) architectures, showing that using residual connections improves performance but that in some cases Fully Connected Networks outperform CNNs. We link this to the fact that absolute positions are relevant in genomes, and thus, CNN's translational equivariance may not be an adequate inductive bias for tackling this problem. We evaluate Graph Neural Network (GNN) architectures by formulating trait prediction as a node regression problem on a population graph, where each node represents an individual, and edges association between their genetic information. We evaluate the transferability of these graphical models and find that the extent to which they exploit neighborhood information is limited. By combining CNN and GNN architectures, we could outperform all other models for predicting milk yield in Holstein cattle.The methods that are based on neural networks can be computationally demanding when used on high density chips or sequence data, even more when fully connected layers are used. To overcome this problem, we propose to obtain a new representation of the input vector by using the intermediate representation (code) of an Autoencoder (AE). Currently we are evaluating the performance benchmarks. Another common issue when using these databases is the missing data or the combination of chips of different SNP's numbers. Again, we propose to use AE for imputing the missing values. One of the main focuses of this work was to explore the feasibility of employing modern deep learning architectures in Genomic Prediction. In this regard, it was possible to train highly over-parameterized architectures and still obtain good generalization. For some datasets and traits, these models outperform all others. However, this did not hold for all the models, traits and datasets studied. Besides, whether the gains in performance outweigh the increase in model size and thus its training and inference computational cost, and lack of interpretability, calls for further discussion.
653 $aDeep learning
653 $aPredicción genómica
653 $aSignal processing
700 1 $aARBOLEYA, L.
700 1 $aBELZARENA, D.
700 1 $aDE LOS SANTOS, L.
700 1 $aELENTER, J.
700 1 $aETCHEBARNE, G.
700 1 $aHOUNIE, I.
700 1 $aCIAPPESONI, G.
700 1 $aNAVAJAS, E.
700 1 $aLECUMBERRY, F.
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