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SeedExtractor: An Open-Source GUI for Seed Image Analysis

Accurate measurement of seed size parameters is essential for both breeding efforts aimed at enhancing yields and basic research focused on discovering genetic components that regulate seed size. To address this need, we have developed an open-source graphical user interface (GUI) software, SeedExtr...

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Detalles Bibliográficos
Autores principales: Zhu, Feiyu, Paul, Puneet, Hussain, Waseem, Wallman, Kyle, Dhatt, Balpreet K., Sandhu, Jaspreet, Irvin, Larissa, Morota, Gota, Yu, Hongfeng, Walia, Harkamal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882627/
https://www.ncbi.nlm.nih.gov/pubmed/33597957
http://dx.doi.org/10.3389/fpls.2020.581546
Descripción
Sumario:Accurate measurement of seed size parameters is essential for both breeding efforts aimed at enhancing yields and basic research focused on discovering genetic components that regulate seed size. To address this need, we have developed an open-source graphical user interface (GUI) software, SeedExtractor that determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color with capability to process a large number of images in a time-efficient manner. In this context, our application takes ∼2 s for analyzing an image, i.e., significantly less compared to the other tools. As this software is open-source, it can be modified by users to serve more specific needs. The adaptability of SeedExtractor was demonstrated by analyzing scanned seeds from multiple crops. We further validated the utility of this application by analyzing mature-rice seeds from 231 accessions in Rice Diversity Panel 1. The derived seed-size traits, such as seed length, width, were used for genome-wide association analysis. We identified known loci for regulating seed length (GS3) and width (qSW5/GW5) in rice, which demonstrates the accuracy of this application to extract seed phenotypes and accelerate trait discovery. In summary, we present a publicly available application that can be used to determine key yield-related traits in crops.