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Nanhu News Network News (correspondent Li Zhao) Bulked segregant analysis is a time-saving and labor-saving method that is widely used in mining trait genetic loci, and a variety of corresponding algorithms have been developed
Recently, Professor Li Lin's research group from the School of Plant Science and Technology of Huazhong Agricultural University and Professor Chen Hong's research group from the School of Science jointly published a research paper entitled "DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits" in Molecular Plant.
In this study, a set of mixed-pool sequencing scheme of hierarchical mixed-pool was designed first, and then a U-Net deep learning model with residual connection was constructed using the mixed-pool sequencing data of maize plant height
Figure 1.
By analyzing the public data of different mixing pools, DeepBSA can not only identify the results identified by the original method, but also find new potential functional sites
Figure 2.
The previously published mixed-pool sequencing algorithms have certain bioinformatics requirements for users, and the operations are relatively complicated
In addition to providing a new, widely used and more accurate mixed-pool sequencing algorithm, the researchers believe that the developed user-friendly interface can help many researchers easily and quickly obtain the results of mixed-pool sequencing, thereby accelerating the functional locus cloning of complex traits in animals and plants and analysis
Professor Li Lin from the National Key Laboratory of Crop Genetic Improvement and Hubei Hongshan Laboratory and Associate Professor Li Weifu from the Hubei Provincial Key Laboratory of Agricultural Bioinformatics are the corresponding authors of the paper
【English summary】
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.
Paper link:Li Weifu Li Lin