短序列读数的进化放置
Evolutionary Placement of Short Sequence Reads
摘要：
我们提出了一个进化放置算法（EPA）的最大似然（ML）模型下的快速分配序列片段（短读段）的分支的一个给定的系统发育树。该算法的准确性进行了评估，在几个现实世界的数据集，并比较放置成对序列比较，使用编辑距离和BLAST。我们测试了两个版本的布局算法，一个是缓慢和更准确的分支长度优化进行每个短读插入和一个更快的版本，其中分支长度近似在插入位置。对于慢速版本，探索了额外的启发式技术，几乎产生与快速版本相同的运行时间，只有一个小的准确性损失。当采用这些额外的算法时，更准确的算法的运行时间与具有大量短查询序列的数据集的简单BLAST搜索的运行时间相当。此外，进化布局算法的准确性显著更高，特别是当参考拓扑的分类单元采样稀疏或不足时。因此，我们的算法已被集成到RAxML中，为BLAST提供了一种同样快速但更准确的替代方案，用于短读段序列数据的可靠性分析。
英文摘要：
We present an Evolutionary Placement Algorithm (EPA) for the rapid assignment of sequence fragments (short reads) to branches of a given phylogenetic tree under the Maximum Likelihood (ML) model. The accuracy of the algorithm is evaluated on several real-world data sets and compared to placement by pair-wise sequence comparison, using edit distances and BLAST. We test two versions of the placement algorithm, one slow and more accurate where branch length optimization is conducted for each short read insertion and a faster version where the branch lengths are approximated at the insertion position. For the slow version, additional heuristic techniques are explored that almost yield the same run time as the fast version, with only a small loss of accuracy. When those additional heuristics are employed the run time of the more accurate algorithm is comparable to that of a simple BLAST search for data sets with a high number of short query sequences. Moreover, the accuracy of the Evolutionary Placement Algorithm is significantly higher, in particular when the taxon sampling of the reference topology is sparse or inadequate. Our algorithm, which has been integrated into RAxML, therefore provides an equally fast but more accurate alternative to BLAST for phylogeny-aware analysis of short-read sequence data.
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