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gmmDenoise:一种新的方法和R包,用于环境DNA片分析中的高保证度序列变异过

Yusuke Koseki¹, Hirohiko Takeshima^2,3, Ryuji Yoneda²

¹Department of Life Design, Faculty of Home Economics, Otsuma Women's University, Chiyoda-ku, Tokyo, Japan.

Molecular ecology resources

|August 4, 2025

概括

这项研究介绍了gmmDenoise,一种用于过环境DNA (eDNA) 元编码中的虚假序列的新方法,改进了自然种群中的遗传多样性监测.

科学领域:

生态学和进化生物学.
遗传学和基因组学遗传学和基因组学
生物信息学和计算生物学

背景情况:

监测遗传多样性对于理解人口生态和进化至关重要.
在野生物种中,用于基因分析的传统组织采样往往很困难.
环境DNA (eDNA) 元编码提供了一个有希望的替代方案,但受到虚假序列的阻碍.

研究的目的:

开发一种新的安普利康过方法,以消除eDNA元编码数据中的虚假安普利康序列变异 (ASV).
提高基因多样性评估和从eDNA数据中推断人口基因的可靠性.

主要方法:

模拟了eDNA元编码过程,以分析真实ASV和PCR生成文物的读数分布.
开发了基于高斯混合模型的方法来估计丰富分布,并确定真和假阳性ASV之间的统计值.
实现了这种方法作为一个名为gmmDenoise的R包.

主要成果:

该gmmDenoise方法有效地从eDNA元编码数据中消除了虚假的ASV.
使用已知真实ASV的单个物种数据集进行评估,证明可靠的识别.
应用于社区元编码数据集,使得可靠的物种内部多样性估计和人口遗传推断成为可能.

结论:

gmmDenoise显著提高了用于人口遗传研究的eDNA元编码的准确性.
对于研究自然种群中的遗传多样性研究的研究人员来说,R包提供了一个有价值的工具.
这种方法克服了eDNA分析的关键挑战,为更可靠的生物多样性监测铺平了道路.

关键词:

安普利康序列变异的变体电子DNA元编码元编码.随机类型的分类.种内特异性多样性种内特异性多样性人口遗传学人口遗传学序列过的过方法

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