遗传算法和Lasso检验的混合方法在叶绿体核心基因系统树构建中的应用
Hybrid Genetic Algorithm and Lasso Test Approach for Inferring Well Supported Phylogenetic Trees based on Subsets of Chloroplastic Core Genes
摘要：
全序列叶绿体基因组的数量每天都在迅速增加，这使得建立大规模的植物物种系统发育树成为可能。考虑到根据其叶绿体定义的近缘植物物种的子集，可以通过其核心基因推断的系统发育树不一定得到很好的支持，这是由于可能出现“问题”基因（即，同源性、不完全谱系分选、水平基因转移等）这可能会模糊系统发育信号。然而，如果有问题的基因数量较少，仍然可以获得值得信赖的系统发育树，问题是确定产生最佳支持树的核心基因的最大子集。为了丢弃有问题的基因，并由于压倒性的数量可能的组合，我们提出了一种混合的方法，嵌入了遗传算法和统计测试。给定一组生物体，其结果是许多阶段的流水线，用于生成支持良好的系统发育树。该建议已被应用到不同的情况下，植物科，导致这些家庭令人鼓舞的结果。
英文摘要：
The amount of completely sequenced chloroplast genomes increases rapidly every day, leading to the possibility to build large scale phylogenetic trees of plant species. Considering a subset of close plant species defined according to their chloroplasts, the phylogenetic tree that can be inferred by their core genes is not necessarily well supported, due to the possible occurrence of "problematic" genes (i.e., homoplasy, incomplete lineage sorting, horizontal gene transfers, etc.) which may blur phylogenetic signal. However, a trustworthy phylogenetic tree can still be obtained if the number of problematic genes is low, the problem being to determine the largest subset of core genes that produces the best supported tree. To discard problematic genes and due to the overwhelming number of possible combinations, we propose an hybrid approach that embeds both genetic algorithms and statistical tests. Given a set of organisms, the result is a pipeline of many stages for the production of well supported phylogenetic trees. The proposal has been applied to different cases of plant families, leading to encouraging results for these families.
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