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Breedbase:一个现代化植物育种的数字生态系统。

Breedbase: a digital ecosystem for modern plant breeding.

机构信息

Boyce Thompson Institute, Ithaca, NY 14853, USA.

Cornell University, Ithaca, NY 14853, USA.

出版信息

G3 (Bethesda). 2022 Jul 6;12(7). doi: 10.1093/g3journal/jkac078.

DOI:10.1093/g3journal/jkac078
PMID:35385099
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9258556/
Abstract

Modern breeding methods integrate next-generation sequencing and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to (1) track breeding materials, (2) store experimental evaluations, (3) record phenotypic measurements using consistent ontologies, (4) store genotypic information, and (5) implement algorithms for analysis, prediction, and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava.org/, last accessed 4/18/2022), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.

摘要

现代育种方法将下一代测序和表型组学相结合,以识别具有最佳特性和最大遗传优势的植物,作为后续育种周期的亲本,最终创造出能够持续获得农民高采用率的改良品种。这种数据驱动的方法取决于数据管理、质量控制和分析的坚实基础。至关重要的是一个中央数据库,该数据库能够:(1)跟踪育种材料;(2)存储实验评估;(3)使用一致的本体记录表型测量;(4)存储基因型信息;(5)实施分析、预测和选择决策算法。由于育种过程的复杂性,育种数据库也往往比较复杂、难以实现和维护。在这里,我们介绍了一个育种数据库系统,即 Breedbase(https://breedbase.org/,最后访问日期为 2022 年 4 月 18 日)。它最初是作为 NextGen Cassava 项目的 Cassavabase(https://cassavabase.org/,最后访问日期为 2022 年 4 月 18 日)启动的,后来发展成为一个适用于多种作物的通用系统,目前被数十个不同的作物和项目使用。该系统基于网络,提供开源软件。它可以在 GitHub(https://github.com/solgenomics/,最后访问日期为 2022 年 4 月 18 日)上获取,并以 Docker 镜像的形式提供部署(https://hub.docker.com/u/breedbase,最后访问日期为 2022 年 4 月 18 日)。Breedbase 系统使育种计划能够更好地管理和利用其数据,以便在完全集成的数字生态系统中做出决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d535/9258556/5828c477fa75/jkac078f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d535/9258556/926e83c881ad/jkac078f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d535/9258556/5828c477fa75/jkac078f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d535/9258556/926e83c881ad/jkac078f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d535/9258556/5828c477fa75/jkac078f2.jpg

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