National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hainan Yazhou Bay Seed Lab, Hainan, China.
College of Science, Huazhong Agricultural University, Wuhan 430070, China.
Mol Plant. 2022 Sep 5;15(9):1418-1427. doi: 10.1016/j.molp.2022.08.004. Epub 2022 Aug 22.
Bulked segregant analysis (BSA) is a rapid, cost-effective method for mapping mutations and quantitative trait loci (QTLs) in animals and plants based on high-throughput sequencing. However, the algorithms currently used for BSA have not been systematically evaluated and are complex and fallible to operate. We developed a BSA method driven by deep learning, DeepBSA, for QTL mapping and functional gene cloning. DeepBSA is compatible with a variable number of bulked pools and performed well with various simulated and real datasets in both animals and plants. DeepBSA outperformed all other algorithms when comparing absolute bias and signal-to-noise ratio. Moreover, we applied DeepBSA to an F segregating maize population of 7160 individuals and uncovered five candidate QTLs, including three well-known plant-height genes. Finally, we developed a user-friendly graphical user interface for DeepBSA, by integrating five widely used BSA algorithms and our two newly developed algorithms, that is easy to operate and can quickly map QTLs and functional genes. The DeepBSA software is freely available to non-commercial users at http://zeasystemsbio.hzau.edu.cn/tools.html and https://github.com/lizhao007/DeepBSA.
基于高通量测序的 bulked segregant analysis（BSA）是一种快速、经济高效的方法，可用于在动植物中定位突变和数量性状基因座（QTL）。然而，目前用于 BSA 的算法尚未得到系统评估，并且操作复杂且容易出错。我们开发了一种基于深度学习的 BSA 方法 DeepBSA，用于 QTL 作图和功能基因克隆。DeepBSA 与可变数量的 bulked 池兼容，并且在动植物中的各种模拟和真实数据集上表现良好。在比较绝对偏差和信噪比时，DeepBSA 优于所有其他算法。此外，我们将 DeepBSA 应用于一个由 7160 个个体组成的 F 分离玉米群体，发现了五个候选 QTL，包括三个著名的植物高度基因。最后，我们通过整合五个广泛使用的 BSA 算法和我们的两个新开发的算法，为 DeepBSA 开发了一个用户友好的图形用户界面，该界面易于操作，可以快速定位 QTL 和功能基因。DeepBSA 软件可在非商业用户免费使用，网址为 http://zeasystemsbio.hzau.edu.cn/tools.html 和 https://github.com/lizhao007/DeepBSA。
