DeepBSA

DeepBSA is a method for bulked segregant analysis (BSA), a technique used for mapping mutations and quantitative trait loci (QTLs) in animals and plants through high-throughput sequencing. Unlike existing complex and potentially error-prone BSA algorithms, DeepBSA leverages deep learning to simplify the process. It is adaptable to different numbers of bulked pools and has demonstrated superior performance in analyzing both simulated and real datasets for animals and plants, outperforming other algorithms in terms of minimizing bias and improving signal-to-noise ratio. DeepBSA can be applied to real-life cases, enhancing its utility for QTL mapping and functional gene cloning.

Topic

Plant biology;Zoology;Mapping

Detail

  • Operation: Genetic mapping;Quantification

  • Software interface: Desktop user interface,Workflow

  • Language: Java

  • License: Not stated

  • Cost: Free

  • Version name: 1.4, 1.5

  • Credit: The National Natural Science Foundation of China , Hainan Yazhou Bay Seed Lab, the major Program of Hubei Hongshan Laboratory, Huazhong Agricultural University Scientific & Technological Self-innovation Foundation, Fundamental Research Funds for the Central Universities of China.

  • Input: -

  • Output: -

  • Contact: Lin Li hzaulilin@mail.hzau.edu.cn, Weifu Li liweifu@mail.hzau.edu.cn

  • Collection: -

  • Maturity: -

Publications

  • DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits.
  • Li Z, et al. DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits. DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits. 2022; 15:1418-1427. doi: 10.1016/j.molp.2022.08.004
  • https://doi.org/10.1016/J.MOLP.2022.08.004
  • PMID: 35996754
  • PMC: -

Download and documentation

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