The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization

Jaramillo-Botero, Andrés; Colorado, Julián; Quimbaya, Mauricio; Rebolledo, María Camila; Lorieux, Mathias; Ghneim-Herrera, Thaura; Arango, Carlos A.; Tobon, Luis; Finke, Jorge; Rocha, Camilo; Muñoz, Fernándo; Riascos, John J.; Silva, Fernando; Chirinda, Ngonidzashe; Cáccamo, Mario; Vandepoele, Klaas; Goddard, William A.

doi:10.3389/fpls.2022.992663

Cited by 6 publications

(2 citation statements)

References 62 publications

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“…Various initiatives are in place to improve rice productivity and sustainability, including promoting high-yielding varieties and providing technical assistance and training to farmers [4][5][6]. One topic of interest is plant breeding, where, through the selection of favorable genes and higher-level omics characterization [7], optimal rice varieties are obtained for cultivation. To identify the important genetic characteristics expressed in the environment, phenotyping is performed in two directions: biomass accumulation and nitrogen content [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

Duque

Patino

Colorado

et al. 2023

Sensors

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The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

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Section: Introductionmentioning

confidence: 99%

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

Duque

Patino

Colorado

et al. 2023

Sensors

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“…For instance, by correlating genomic data with transcriptomic, proteomic, or metabolomic data, researchers can identify how changes in the DNA sequence impact gene expression, protein production, and metabolite levels. This information can illuminate the mechanisms through which genetic variations contribute to observable traits [84][85][86].…”

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confidence: 99%

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

Cembrowska-Lech,

Krzemińska,

Miller

et al. 2023

Biology

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This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

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Sequencing vs. amplification for the estimation of allele dosages in sugarcane (Saccharum spp.)

Jaimes,

Londoño,

Saavedra‐Diaz

et al. 2024

Appl Plant Sci

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PremiseDetecting single‐nucleotide polymorphisms (SNPs) in a cost‐effective way is fundamental in any plant breeding pipeline. Here, we compare three genotyping techniques for their ability to reproduce the allele dosage of SNPs of interest in sugarcane (Saccharum spp.).MethodsTo identify a reproducible technique to estimate allele dosage for the validation of SNP markers, the correlation between Flex‐Seq, kompetitive allele‐specific PCR (KASP), and genotyping‐by‐sequencing and restriction site–associated DNA sequencing (GBS+RADseq) was determined for a set of 76 SNPs. To find alternative methodologies for allele dosage estimation, the KASP and Flex‐Seq techniques were compared for the same set of SNPs. For the three techniques, a population of 53 genotypes from the diverse sugarcane panel of the Centro de Investigación de la Caña de Azúcar (Cenicaña), Colombia, was selected.ResultsThe average Pearson correlation coefficients between GBS+RADseq and Flex‐Seq, GBS+RADseq and KASP, and Flex‐Seq and KASP were 0.62 ± 0.27, 0.38 ± 0.27, and 0.38 ± 0.30, respectively.DiscussionFlex‐Seq reproduced the allele dosages determined using GBS+RADseq with good levels of precision because of its depth of sequencing and ability to target specific positions in the genome. Additionally, Flex‐Seq outperformed KASP by allowing the conversion of a higher number of SNPs and a more accurate estimation of the allele dosage. Flex‐Seq has therefore become the genotyping methodology of choice for marker validation at Cenicaña.

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The ÓMICAS alliance, an international research program on multi-omics for crop breeding optimization

Cited by 6 publications

References 62 publications

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

Sequencing vs. amplification for the estimation of allele dosages in sugarcane (Saccharum spp.)

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