Department of Computer Science, Brigham Young University, Provo, UT 84602, USA.
BMC Bioinformatics. 2012;13 Suppl 13(Suppl 13):S8. doi: 10.1186/1471-2105-13-S13-S8. Epub 2012 Aug 24.
Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques.
When compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at http://dna.cs.byu.edu/psoda
The use of Partial Tree Mixing in a partition based tree space allows the algorithm to quickly converge on near optimal tree regions. These regions can then be searched in a methodical way to determine the overall optimal phylogenetic solution.
测序技术的最新进展产生了大量可用于进行系统发育推断的数据。目前的研究受到限制，因为在合理数量的个体上进行树搜索所需的时间非常长。这项研究开发了新的系统发育算法，通过几种创新的搜索技术，可以在合理的时间内对成千上万的物种进行操作。
与流行的系统发育搜索算法相比，对于大型数据集，可以更快地找到更好的树。这些算法被整合到可在 http://dna.cs.byu.edu/psoda 获得的 PSODA 应用程序中。
在基于分区的树空间中使用部分树混合允许算法快速收敛到接近最优的树区域。然后可以以有条不紊的方式搜索这些区域，以确定整体最优的系统发育解决方案。
