(Press-News.org)
Advancements in whole-genome sequencing have revolutionized plant species characterization, providing a wealth of genotypic data for analysis. The combination of genomic selection and neural networks, especially deep learning and autoencoders, has emerged as a promising method for predicting complex traits from this data. Despite the success in applications like plant phenotyping, challenges remain in accurately translating visual information from images into measurable data for genomic studies.
In November 2023, Plant Phenomics published a research article entitled by “GenoDrawing: An Autoencoder Framework for Image Prediction from SNP Markers”.
The study introduces an innovative approach using an autoencoder network and embedding predictor to simplify apple images into 64 dimensions and predict fruit shapes from molecular data (SNPs). This method, known as GenoDrawing, involves training the autoencoder with a large dataset of apple images. The generated embeddings, along with SNP data, are then used to predict and reconstruct apple shapes. The method showed that targeted SNPs (tSNPs) consistently outperformed randomly selected SNPs (rSNPs) in predicting image embeddings, resulting in more accurate fruit shape predictions. The best models using tSNPs achieved lower mean absolute errors (MAEs) and produced distributions closer to the original data compared to rSNPs. Additionally, the tSNP-based version predicted a wider range of fruit shapes, demonstrating its effectiveness in capturing the diversity of apple phenotypes. However, the study revealed limitations, including the model's inability to accurately capture certain fruit features and the influence of environmental factors on apple phenotypes. Despite these challenges, the approach represents a significant advancement in genomic prediction, demonstrating the potential of merging image analysis with molecular data to comprehend complex traits in crops.
In summary, the findings suggest that selecting relevant SNPs is crucial for accurate predictions and that GenoDrawing can effectively learn to predict fruit shapes when given the appropriate markers. This research lays the groundwork for future studies aiming to enhance the accuracy and applicability of genomic prediction models by incorporating image data and improving SNP selection strategies.
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References
Authors
Federico Jurado-Ruiz1, David Rousseau2, Juan A. Botía3, and Maria José Aranzana1,4*
Affiliations
1Center for Research in Agricultural Genomics (CRAG), 08193 Barcelona, Cerdanyola, Spain.
2Université d’Angers, LARIS, INRAe UMR IRHS, 49000 Angers, France
3Department of Information and Communi-cation Engineering, University of Murcia, 30071 Murcia, Spain
4IRTA (Institut de Recerca i Tecnologia Agroalimentàries), Barcelona, Spain
About Maria José Aranzana
She is a researcher at the Institute of Agrifood Research and Technology (IRTA), Barcelona, Spain. She is working on the research program of Plant and Animal Genomics.
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Erin Overstreet has been selected as the new executive director of the USC Stevens Center for Innovation where she will oversee the university’s commercialization of USC-driven intellectual property.
Overstreet’s expertise and experience embody technology transfer and innovation across the academic, educational, and venture capital sectors; such experience is critical for bridging USC research to a broadened, national technology transfer ecosystem, said Ishwar Puri, senior vice president of the Office of Research and Innovation.
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The work entitled “Can Topography ...
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Peer reviewed – observational study - humans
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A new study published today reveals that the technique detects new variants almost a week more quickly than traditional whole genome sequencing methods.
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Importantly, it helped to implement ...
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