An Application of Random Walk Resampling to Phylogenetic HMM Inference and Learning
An Application of Random Walk Resampling to Phylogenetic HMM Inference and Learning
复制标题
随机游走重采样在系统发育 HMM 推理和学习中的应用
DOI：
10.1109/tnb.2020.2991302
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发表时间：
2020
影响因子：
3.9
通讯作者：
Liu, Kevin J.
中科院分区：
文献类型：
--
作者：
Wang, Wei;Wuyun, Qiqige;Liu, Kevin J.
统计重采样方法广泛用于置信区间的设置，并作为一种数据摄动技术用于统计推断和学习。流行的重采样方法（如标准自举法）的一个重要假设是输入观测值是相同且独立分布的（i.i.d）。然而，在计算生物学和生物信息学领域，许多不同的因素可能导致序列内依赖，例如重组和其他控制序列进化的进化过程。序列重采样（sequence ressampling，“SERES”）框架是先前提出的一种简化输入观测值假设的框架。SERES重采样采取随机漫步的形式，对输入的排列或未排列的生物分子序列。本研究首次介绍了SERES随机漫步在对齐序列输入上的应用，也是首次展示了SERES作为数据扰动技术的实用性，以产生改进的统计估计。我们关注的是重组感知的本地谱系推断的经典问题。我们在一项模拟研究中表明，将SERES重采样和重估计与recHMM（一种基于隐马尔可夫模型的方法）相结合，可以产生局部系谱推断，并在拓扑精度方面取得一致且通常很大的改进。我们使用经验HIV基因组序列数据集进一步评估方法的性能。
Statistical resampling methods are widely used for confidence interval placement and as a data perturbation technique for statistical inference and learning. An important assumption of popular resampling methods such as the standard bootstrap is that input observations are identically and independently distributed (i.i.d.). However, within the area of computational biology and bioinformatics, many different factors can contribute to intra-sequence dependence, such as recombination and other evolutionary processes governing sequence evolution. The SEquential RESampling (“SERES”) framework was previously proposed to relax the simplifying assumption of i.i.d. input observations. SERES resampling takes the form of random walks on an input of either aligned or unaligned biomolecular sequences. This study introduces the first application of SERES random walks on aligned sequence inputs and is also the first to demonstrate the utility of SERES as a data perturbation technique to yield improved statistical estimates. We focus on the classical problem of recombination-aware local genealogical inference. We show in a simulation study that coupling SERES resampling and re-estimation with recHMM, a hidden Markov model-based method, produces local genealogical inferences with consistent and often large improvements in terms of topological accuracy. We further evaluate method performance using empirical HIV genome sequence datasets.
影响因子：
1.5
作者：
Lau, Katherine A.;Wang, Bin;Saksena, Nitin K.
通讯作者：
Saksena, Nitin K.
书评：人类进化遗传学：起源、民族和疾病以及基因谱系、变异和进化：合并理论入门
DOI：
--
发表时间：
2005
期刊：
影响因子：
--
作者：
J. Cooke
通讯作者：
J. Cooke
使用生物分子序列上的 SEequential RESampling 随机游走进行非参数和半参数支持估计
影响因子：
1
作者：
Wang, Wei;Smith, Jack;Hejase, Hussein A.;Liu, Kevin J.
通讯作者：
Liu, Kevin J.