从高通量测序数据组装的病毒基因组中检测单个核酸多态:测试序列分析策略的大规模性能测试
Johan Rollin1, Rachelle Bester2,3, Yves Brostaux4
1Laboratory of Plant Pathology-TERRA-Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium.
PeerJ
|August 21, 2023
概括
高通量测序 (HTS) 和生物信息学工具对于病毒发现至关重要. 这项研究揭示了使用HTS数据在植物病毒中准确检测单核酸多态 (SNP) 的关键参数.
科学领域:
- 病毒学 病毒学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 高通量测序 (HTS) 已经彻底改变了病毒的发现和监测.
- 生物信息学管道对于重建病毒基因组和检测单核酸多态 (SNP) 是必不可少的.
- 使用HTS数据进行SNP检测的生物信息学分析中的潜在偏差需要进一步调查.
研究的目的:
- 评估不同生物信息管道对植物病毒SNP检测精度的影响.
- 从HTS数据中确定影响变量调用可靠性的关键参数.
- 为改善植物病毒研究中SNP预测提供建议.
主要方法:
- 一项涉及14个植物病毒学实验室的大规模性能测试 (PT) 研究.
- 利用了三组人工设计的数据集,其中包含已知花马赛克病毒 (PepMV) 变体.
- 在三个关键步骤中分析生物信息学管道:读取预处理,病毒隔离识别和变异调用.
主要成果:
- 在不同生物信息管道中观察到SNP检测准确度的显著变化.
- 选择最接近的参考基因组,映射参数和手动验证对SNP检测成功产生了重大影响.
- 性能测试确定了影响变异调用结果的特定生物信息步骤和参数.
结论:
- 生物信息学分析步骤,特别是参考选择和映射,对于精确检测植物病毒中的SNP至关重要.
- 生物信息学管道的标准化和验证是必要的,以最大限度地减少偏见并提高可靠性.
- 建议提议提高从植物病毒学中HTS数据中SNP的预测准确度.
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