AR083897A1 - Prediccion de fenotipos y rasgos en base al metaboloma - Google Patents
Prediccion de fenotipos y rasgos en base al metabolomaInfo
- Publication number
- AR083897A1 AR083897A1 ARP110104281A ARP110104281A AR083897A1 AR 083897 A1 AR083897 A1 AR 083897A1 AR P110104281 A ARP110104281 A AR P110104281A AR P110104281 A ARP110104281 A AR P110104281A AR 083897 A1 AR083897 A1 AR 083897A1
- Authority
- AR
- Argentina
- Prior art keywords
- plants
- application
- profiles
- metabolic
- groups
- Prior art date
Links
Classifications
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01H—NEW PLANTS OR NON-TRANSGENIC PROCESSES FOR OBTAINING THEM; PLANT REPRODUCTION BY TISSUE CULTURE TECHNIQUES
- A01H3/00—Processes for modifying phenotypes, e.g. symbiosis with bacteria
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D15/00—Separating processes involving the treatment of liquids with solid sorbents; Apparatus therefor
- B01D15/08—Selective adsorption, e.g. chromatography
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/483—Physical analysis of biological material
- G01N33/4833—Physical analysis of biological material of solid biological material, e.g. tissue samples, cell cultures
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/50—Mutagenesis
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B40/20—Supervised data analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B40/30—Unsupervised data analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Chemical & Material Sciences (AREA)
- Theoretical Computer Science (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Biotechnology (AREA)
- Analytical Chemistry (AREA)
- Molecular Biology (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Genetics & Genomics (AREA)
- Epidemiology (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioethics (AREA)
- Public Health (AREA)
- Artificial Intelligence (AREA)
- Pathology (AREA)
- Immunology (AREA)
- General Physics & Mathematics (AREA)
- Biochemistry (AREA)
- Food Science & Technology (AREA)
- Urology & Nephrology (AREA)
- Medicinal Chemistry (AREA)
- Hematology (AREA)
- Optics & Photonics (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Physiology (AREA)
- Environmental Sciences (AREA)
Abstract
La solicitud provee métodos de caracterización de perfiles metabólicos, perfiles fenotípicos y perfiles de rasgos en plantas o grupos de plantas. Además, la solicitud también provee métodos para establecer un modelo objetivo entre un perfil fenotípico y un perfil metabólico, o entre un perfil de rasgo y perfil metabólico. Además, también se proveen métodos de uso de tales modelos objetivo para predecir con exactitud el desarrollo un fenotipo de interés o un rasgo de interés en una planta independiente, inmadura. En una realización de la solicitud, un modelo objetivo se establece usando los perfiles fenotípicos y perfiles metabólicos de por lo menos dos grupos de plantas, donde los grupos de plantas exhiben diferentes fenotipos o crecen bajo condiciones ambientales distintas. Alternativamente, un modelo objetivo puede ser establecido usando los perfiles de rasgos y perfiles metabólicos de por lo menos dos grupos de plantas, donde los grupos de plantas exhiben diferentes rasgos o crecen bajo condiciones ambientales distintas. En tales realizaciones, los modelos objetivo de la solicitud pueden determinarse usando varias combinaciones de análisis de cuadrados mínimos parciales, análisis discriminantes de cuadrados mínimos parciales, análisis de componentes principales, inter-validación, importancia variable para cálculos de proyección, máquinas de vectores de soporte y redes neurales. Los perfiles metabólicos de las plantas o grupos de plantas contemplados por la solicitud pueden caracterizarse, por ejemplo, usando técnicas cromatográficas y de espectroscopía de masa. En una realización específica, los fragmentos masa-carga detectados por espectrometría de masa, que comprenden los perfiles metabólicos de la solicitud no son identificados, caracterizados o de otro modo objetivados antes del análisis estadístico. De este modo, los perfiles metabólicos de la solicitud comprenden el conjunto completo de metabolitos detectados. Además se proveen métodos para predecir el desarrollo de un fenotipo o rasgo de interés en una planta no utilizada para establecer el modelo objetivo de la solicitud, es decir, en una planta independiente. En una realización, los modelos objetivos de la solicitud se aplican al perfil metabólico de una planta inmadura, independiente con el fin de predecir el desarrollo de un fenotipo o rasgo de interés en la planta. En otra realización, se seleccionan plantas inmaduras a utilizar en base al desarrollo estimado de un fenotipo o rasgo de interés.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US41464510P | 2010-11-17 | 2010-11-17 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AR083897A1 true AR083897A1 (es) | 2013-04-10 |
Family
ID=45094266
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| ARP110104281A AR083897A1 (es) | 2010-11-17 | 2011-11-16 | Prediccion de fenotipos y rasgos en base al metaboloma |
Country Status (9)
| Country | Link |
|---|---|
| US (1) | US9465911B2 (es) |
| EP (1) | EP2641205B1 (es) |
| AR (1) | AR083897A1 (es) |
| AU (1) | AU2011328963B2 (es) |
| BR (1) | BR112013012068B1 (es) |
| CA (1) | CA2817241C (es) |
| CL (1) | CL2013001399A1 (es) |
| ES (1) | ES2865728T3 (es) |
| WO (1) | WO2012068217A2 (es) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9465911B2 (en) | 2010-11-17 | 2016-10-11 | Pioneer Hi-Bred International, Inc. | Prediction of phenotypes and traits based on the metabolome |
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| CN102930158B (zh) * | 2012-10-31 | 2016-01-20 | 哈尔滨工业大学 | 基于偏最小二乘的变量选择方法 |
| DE102013200058B3 (de) * | 2013-01-04 | 2014-06-26 | Siemens Aktiengesellschaft | Automatisierte Auswertung der Rohdaten eines MR-Spektrums |
| US10438581B2 (en) | 2013-07-31 | 2019-10-08 | Google Llc | Speech recognition using neural networks |
| EP3173782A1 (de) * | 2015-11-26 | 2017-05-31 | Karlsruher Institut für Technologie | Verfahren zur steuerung kontinuierlicher chromatographie und multisäulen-chromatographie-anordnung |
| CN106018626A (zh) * | 2016-07-29 | 2016-10-12 | 云南省烟草农业科学研究院 | 一种基于气相色谱质谱的烟草柱头代谢组学分析方法 |
| CN106018627A (zh) * | 2016-07-29 | 2016-10-12 | 云南省烟草农业科学研究院 | 一种基于气相色谱质谱的烟草花粉代谢组学分析方法 |
| CN106338569A (zh) * | 2016-07-29 | 2017-01-18 | 云南省烟草农业科学研究院 | 一种基于气相色谱质谱的烟草茎部代谢组学分析方法 |
| CN106018654A (zh) * | 2016-07-29 | 2016-10-12 | 云南省烟草农业科学研究院 | 一种基于气相色谱质谱的烟草花柱代谢组学分析方法 |
| CN106404971B (zh) * | 2016-11-29 | 2018-07-03 | 河南工业大学 | 气相色谱法鉴定大米加工精度的方法 |
| WO2018110490A1 (ja) * | 2016-12-12 | 2018-06-21 | 日本電気株式会社 | 情報処理装置、遺伝情報作成方法及びプログラム |
| CN106897913B (zh) * | 2017-01-22 | 2020-10-27 | 华南理工大学 | 一种精确的注塑机选型方法 |
| US20180239866A1 (en) * | 2017-02-21 | 2018-08-23 | International Business Machines Corporation | Prediction of genetic trait expression using data analytics |
| CN109283278B (zh) * | 2018-11-30 | 2021-10-01 | 北京林业大学 | 一种微量油茶种仁油脂含量和脂肪酸同时测定的方法 |
| WO2020188114A1 (en) * | 2019-03-21 | 2020-09-24 | Basf Se | Method for predicting yield performance of a crop plant |
| EP3711478A1 (en) * | 2019-03-21 | 2020-09-23 | Basf Se | Method for predicting yield loss of a crop plant |
| JP2020174668A (ja) * | 2019-04-16 | 2020-10-29 | 花王株式会社 | ダイズの収量予測方法 |
| BR112021020760A2 (pt) * | 2019-04-16 | 2021-12-14 | Kao Corp | Método de predição de produtividade de soja |
| JP7244338B2 (ja) * | 2019-04-16 | 2023-03-22 | 花王株式会社 | ダイズの収量予測方法 |
| CN111883214B (zh) * | 2019-07-05 | 2023-06-16 | 深圳数字生命研究院 | 构建诱饵库、构建目标-诱饵库、代谢组fdr鉴定的方法及装置 |
| WO2021252514A1 (en) * | 2020-06-09 | 2021-12-16 | Zymergen Inc. | Metabolite fingerprinting |
| CN113376283B (zh) * | 2021-06-10 | 2023-06-20 | 中国科学院成都生物研究所 | 基于小数据集的可用于非靶向危害物筛查的色谱条件快速开发方法 |
| CN113516176A (zh) * | 2021-06-21 | 2021-10-19 | 中国农业大学 | 基于光谱纹理特征和k近邻法的小麦倒伏区域识别方法 |
| WO2024013240A1 (en) | 2022-07-13 | 2024-01-18 | Roche Diagnostics Gmbh | A method for quality check of at least one lc-ms measurement |
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| BRPI1012177A2 (pt) * | 2009-05-14 | 2016-04-05 | Pioneer Hi Bred Int | métodos e sistema para estimar uma característica de planta, métodos de predição de tolerância a seca em uma planta, de predição do teor de um analito-alvo em uma planta, de predição de um teor de introgressão do genoma de um experimento de retrocruzamento. |
| US8835361B2 (en) * | 2010-06-01 | 2014-09-16 | The Curators Of The University Of Missouri | High-throughput quantitation of crop seed proteins |
| BR112013012068B1 (pt) | 2010-11-17 | 2020-12-01 | Pioneer Hi-Bred International, Inc. | método imparcial para prever o fenótipo ou traço de pelo menos uma planta independente |
-
2011
- 2011-11-16 BR BR112013012068-1A patent/BR112013012068B1/pt active IP Right Grant
- 2011-11-16 EP EP11791143.8A patent/EP2641205B1/en active Active
- 2011-11-16 ES ES11791143T patent/ES2865728T3/es active Active
- 2011-11-16 AU AU2011328963A patent/AU2011328963B2/en active Active
- 2011-11-16 AR ARP110104281A patent/AR083897A1/es not_active Application Discontinuation
- 2011-11-16 US US13/297,584 patent/US9465911B2/en active Active
- 2011-11-16 WO PCT/US2011/060936 patent/WO2012068217A2/en active Application Filing
- 2011-11-16 CA CA2817241A patent/CA2817241C/en active Active
-
2013
- 2013-05-16 CL CL2013001399A patent/CL2013001399A1/es unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9465911B2 (en) | 2010-11-17 | 2016-10-11 | Pioneer Hi-Bred International, Inc. | Prediction of phenotypes and traits based on the metabolome |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2012068217A2 (en) | 2012-05-24 |
| ES2865728T3 (es) | 2021-10-15 |
| BR112013012068B1 (pt) | 2020-12-01 |
| EP2641205B1 (en) | 2021-03-17 |
| EP2641205A2 (en) | 2013-09-25 |
| US20120119080A1 (en) | 2012-05-17 |
| AU2011328963A1 (en) | 2013-05-30 |
| CL2013001399A1 (es) | 2014-02-21 |
| US9465911B2 (en) | 2016-10-11 |
| CA2817241A1 (en) | 2012-05-24 |
| CA2817241C (en) | 2018-10-02 |
| AU2011328963B2 (en) | 2016-12-08 |
| BR112013012068A2 (pt) | 2016-08-09 |
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