y Fast hierarchical Bayesian analysis of population structure
y Fast hierarchical Bayesian analysis of population structure
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DOI：
10.1093/nar/gkz361
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发表时间：
2019-06-20
影响因子：
14.9
通讯作者：
Corander, Jukka
中科院分区：
文献类型：
--
作者：
Tonkin-Hill, Gerry;Lees, John A.;Corander, Jukka
我们提出fastbaps，一个快速的解决方案，遗传聚类问题。Fastbaps快速识别一个近似拟合的狄利克雷过程混合模型（Dirichlet process mixture model，Dirichlet process mixture model，Dirichlet process mixture model）用于聚类多位点基因型数据。我们高效的基于模型的聚类方法能够聚类比现有基于模型的方法大10-100倍的数据集，我们通过分析超过110 000个HIV-1 pol基因序列的比对来证明这一点。我们还提供了一种方法，用于快速划分现有的层次结构，以最大限度地提高系统发育模型的边际似然，使我们能够分裂成分支和子分支的进化树使用人口基因组模型。对模拟数据以及各种真实的细菌和病毒数据集的广泛测试表明，fastbaps提供了与以前基于模型的方法相当或改进的解决方案，同时速度明显更快。该方法在开源MIT许可下作为易于使用的R包在https://github.com/gtonkinhill/fastbaps上免费提供。
We present fastbaps, a fast solution to the genetic clustering problem. Fastbaps rapidly identifies an approximate fit to a Dirichlet process mixture model (DPM) for clustering multilocus genotype data. Our efficient model-based clustering approach is able to cluster datasets 10-100 times larger than the existing model-based methods, which we demonstrate by analyzing an alignment of over 110 000 sequences of HIV-1 pol genes. We also provide a method for rapidly partitioning an existing hierarchy in order to maximize the DPM model marginal likelihood, allowing us to split phylogenetic trees into clades and sub-clades using a population genomic model. Extensive tests on simulated data as well as a diverse set of real bacterial and viral datasets show that fastbaps provides comparable or improved solutions to previous model-based methods, while being significantly faster. The method is made freely available under an open source MIT licence as an easy to use R package at https://github.com/gtonkinhill/fastbaps.