minSNPs:一个R包,用于从微生物基因组数据中推导解析度优化的SNP集
Kian Soon Hoon1, Deborah C Holt1,2, Sarah Auburn1,3,4
1Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia.
PeerJ
|May 30, 2023
概括
R包minSNPs有效地识别了微生物基因组多样性分析的最佳单核酸多态 (SNP) 集. 它快速挖掘SNP数据,以区分细菌菌株和病原体菌株,有助于监测和进化研究.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 单核酸多态 (SNP) 是理解微生物遗传多样性和进化的关键标记.
- 现有的识别最佳SNP集的方法可能是计算密集的,缺乏灵活性.
- 需要有效的工具来挖掘大型SNP数据集,用于特定的歧视性或多样性最大化目的.
研究的目的:
- 介绍minSNPs,一个R包,旨在组装单核酸多态度 (SNP) 的解析度优化的集合.
- 提供灵活快速的工具来挖掘从序列对齐和正则SNP矩阵中的SNP数据.
- 为了使SNP集合的推导能够优化,以区分特定序列或最大限度地提高整体多样性.
主要方法:
- 开发了minSNPs R包,这是一个Java应用程序的重新实现.
- 使用序列对齐和全基因组正统SNP矩阵作为输入数据.
- 实现基于用户定义的歧视或多样性标准优化SNP集选择的算法.
主要成果:
- MinSNPs证明了输入数据量和SNP设置规范的线性可扩展性.
- 在 * Staphylococcus aureus * 和 * Plasmodium vivax * 数据集上进行测试,minSNPs 有效地导出了歧视性的 SNP 集.
- 一组五个SNP确定了东南亚*P. vivax*分离物的原产国,展示了它在流行病监测中的实用性.
结论:
- MinSNPs提供了一种强大而高效的解决方案,用于组装全面的SNP矩阵并挖掘它们以获得优化的标记集.
- 该方案促进了快速灵活的SNP发现,这对于微生物基因组学,病原体监测和进化研究至关重要.
- 微型SNP有效捕捉微生物基因组多样性,并使识别可用于各种应用的信息性SNP标记器成为可能.
关键词:
基因组 基因组 基因组 基因组 基因组基因组对齐 基因组对齐微生物微生物的存在流感病毒 (Plasmodium) 是一种病毒.优化了分辨率的优化通过SNP基因定型进行SNP基因定型在SNP矩阵中.在SNP采矿方面,SNP采矿国家统一计划 (SNP) 是一个国家统一计划.这种病原体是葡萄球菌.更多相关视频
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