未经证实:基因组略读是植物系统基因组学中一种流行的方法,不包括有偏差的富集步骤,依靠基因组总DNA的随机浅测序。根据这些数据,通常很容易组装,构成了这些研究中产生的大部分系统发育信息。尽管有几次尝试使用基因组清洗来恢复低拷贝核基因座以直接进行系统发育,这种努力仍然被忽视。原因可能包括具有少量读取的文库和具有大基因组的物种之间的权衡(即,低覆盖率导致的数据缺失),但也可能与缺乏数据组装管道有关。
UNASSIGNED:提出了一种管道及其配套的R包,旨在自动从基因组略读文库中回收低拷贝核标记。此外,一系列旨在评估关键装配参数影响的分析,提供了参考选择和缺失数据。
UNASSIGNED:组装了大量推定的低拷贝核基因座,并证明可用于在测试的文库中建立系统发育推断的基础(比以前组装的来自相同文库的质体的数据多4至11倍)。
UNASSIGNED:从基因组中组装低拷贝核标记的关键方面包括要使用的序列的最小覆盖率和深度。这些参数的更严格的值减少了组装的数据量,并增加了缺失数据的相对量,这可能会损害系统发育推断,反过来,放松相同的参数可能会增加序列误差。文中讨论了这些问题,使用此管道时,强烈建议通过多次比较跟踪其对支持和一致性的影响进行参数调整。skimmingLoci管道(https://github.com/mreginato/skimmingLoci)可能会刺激使用基因组skims来恢复核基因座以直接系统发育使用,增加基因组略读数据解决系统发育关系的能力,同时减少通常浪费的测序DNA的数量。
Genome skimming is a popular method in plant phylogenomics that do not include a biased enrichment step, relying on random shallow sequencing of total genomic DNA. From these data the plastome is usually readily assembled and constitutes the bulk of phylogenetic information generated in these studies. Despite a few attempts to use genome skims to recover low copy nuclear loci for direct phylogenetic use, such endeavor remains neglected. Causes might include the trade-off between libraries with few reads and species with large genomes (i.e., missing data caused by low coverage), but also might relate to the lack of pipelines for data assembling.
A pipeline and its companion R package designed to automate the recovery of low copy nuclear markers from genome skimming libraries are presented. Additionally, a series of analyses aiming to evaluate the impact of key assembling parameters, reference selection and missing data are presented.
A substantial amount of putative low copy nuclear loci was assembled and proved useful to base phylogenetic inference across the libraries tested (4 to 11 times more data than previously assembled plastomes from the same libraries).
Critical aspects of assembling low copy nuclear markers from genome skims include the minimum coverage and depth of a sequence to be used. More stringent values of these parameters reduces the amount of assembled data and increases the relative amount of missing data, which can compromise phylogenetic inference, in turn relaxing the same parameters might increase sequence error. These issues are discussed in the text, and parameter tuning through multiple comparisons tracking their effects on support and congruence is highly recommended when using this pipeline. The skimmingLoci pipeline (https://github.com/mreginato/skimmingLoci) might stimulate the use of genome skims to recover nuclear loci for direct phylogenetic use, increasing the power of genome skimming data to resolve phylogenetic relationships, while reducing the amount of sequenced DNA that is commonly wasted.