高级检索
    邓继忠, 林伟森, 甘四明, 黄华盛, 李梅, 金济, 何明昊. 一种二倍体片段测序中SNP检测系统的构建[J]. 华南农业大学学报, 2016, 37(3): 115-120. DOI: 10.7671/j.issn.1001-411X.2016.03.018
    引用本文: 邓继忠, 林伟森, 甘四明, 黄华盛, 李梅, 金济, 何明昊. 一种二倍体片段测序中SNP检测系统的构建[J]. 华南农业大学学报, 2016, 37(3): 115-120. DOI: 10.7671/j.issn.1001-411X.2016.03.018
    shu
    DENG Jizhong, LIN Weisen, GAN Siming, HUANG Huasheng, LI Mei, JIN Ji, HE Minghao. Development of an automatic system for SNP detection in diploid fragment sequencing[J]. Journal of South China Agricultural University, 2016, 37(3): 115-120. DOI: 10.7671/j.issn.1001-411X.2016.03.018
    Citation: DENG Jizhong, LIN Weisen, GAN Siming, HUANG Huasheng, LI Mei, JIN Ji, HE Minghao. Development of an automatic system for SNP detection in diploid fragment sequencing[J]. Journal of South China Agricultural University, 2016, 37(3): 115-120. DOI: 10.7671/j.issn.1001-411X.2016.03.018
    shu

    一种二倍体片段测序中SNP检测系统的构建

    Development of an automatic system for SNP detection in diploid fragment sequencing

      摘要
    • 摘要:
      目的 开发基于模式识别方法的二倍体片段测序中单核苷酸多态性(Single nucleotide polymorphism，SNP)自动检测系统，提高检测的准确性。
      方法 采用LabWindows/CVI 9.0开发平台，结合Matlab函数库编程，以二倍体PCR片段测序的.ab1或.scf格式文件作为源数据，首先分离出碱基G、A、T和C，进行一维离散小波滤波，再对各碱基处的波形进行典型特征提取，最后运用基于反向传播神经网络的分类器完成SNP识别和判断。
      结果 系统界面友好、运行稳定。SNP等级分为6级，允许用户对可疑的SNP进行人工修正，对尾叶桉Eucalyptus urophylla的26个测序序列143个SNP的测试中检测准确率、假阳性率和假阴性率均明显优于之前的类似软件。
      结论 本文所构建的SNP自动检测系统准确性高，不需参考序列，可用于二倍体PCR片段测序中SNP的高效检测。

       

      Abstract:
      Objective This study aims to develop a pattern-recognition based system for automatic single nucleotide polymorphism (SNP)detection in diploid fragment sequencing and improve the detection accuracy.
      Method The LabWindows/CVI 9.0 platform and Matlab environment were combined for analyzing.ab1 or.scf files generated in diploid PCR fragment sequencing. Firstly, four bases G, A, T and C were separated for eliminating noise through one-dimensional discrete wavelet filtering, following with extraction of typical features of each base position (peak) from a fluorescence curve. A classifier based on back-propagation neural network was then used for SNP recognition and diagnosis.
      Result This established system was characterized by friendly interface, stable operation and manual modification accessibility. It classified the SNP reliability into six grades. Performance test with 143 SNPs of 26 sequencing fragments from Eucalyptus urophylla demonstrated that our system outperformed three previously reported software packages in detecting accuracy, false positive and false negative rates.
      Conclusion Our system has a high rate of accuracy without the need for a reference sequence. It could be used for efficient SNP detection in diploid PCR fragment sequencing.

       

      • HTML全文
      • 参考文献(16)
      • 相关文章
      • 施引文献
    • 资源附件(0)

    /

    返回文章
    返回