Similarity thresholds used in DNA sequence assembly from short reads can reduce the comparability of population histories across species.
Similarity thresholds used in DNA sequence assembly from short reads can reduce the comparability of population histories across species.
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作者：
Harvey MG;Judy CD;Seeholzer GF;Maley JM;Graves GR;Brumfield RT
比较使用短读长测序生成的数据集之间的推论可以深入了解生物体之间的分歧、基因流和选择的协同影响，但由于数据集组装过程中引入的偏差，比较变得复杂。序列相似性阈值允许将短读段从头组装成代表不同基因座的等位基因簇，但所得数据集对所使用的相似性阈值和所研究的生物体中自然存在的变异都很敏感。组装读数之间需要高度序列相似性的阈值（严格阈值）以及高度可变的物种可能会导致数据集中不同的等位基因丢失或分成单独的基因座（“过度分裂”），而宽松的阈值会增加旁系同源基因座被组合成单个基因座的风险（“分裂不足”）。因此，如果应用不同的相似性阈值或者物种的谱系内遗传变异水平不同，则数据集或物种之间的比较可能存在偏差。我们研究了一系列相似性阈值对来自具有不同遗传差异水平的四种不同非模型鸟类谱系（物种或物种对）的经验短读数据集的组装的影响。我们发现，在所有物种中，严格的相似性阈值比更宽松的阈值导致每个位点的等位基因更少，这似乎是高水平过度分裂的结果。相反，假定的分裂不足的频率在所有阈值上都很低。推断的个体之间的遗传距离、基因树深度以及祖先突变缩放的有效种群大小 (θ) 的估计会根据所应用的相似性阈值而有所不同。即使应用相同的阈值，跨物种推论的相对差异也会有所不同，但当比较在不同阈值下组装的数据集时，可能会显着不同。这些差异不仅使物种间的比较变得复杂，而且妨碍了标准突变率用于参数校准的应用。我们建议一些组装短读数据以最大化可比性的最佳实践，例如使用更自由的阈值并检查不同阈值对每个数据集的影响。
Comparing inferences among datasets generated using short read sequencing may provide insight into the concerted impacts of divergence, gene flow and selection across organisms, but comparisons are complicated by biases introduced during dataset assembly. Sequence similarity thresholds allow the de novo assembly of short reads into clusters of alleles representing different loci, but the resulting datasets are sensitive to both the similarity threshold used and to the variation naturally present in the organism under study. Thresholds that require high sequence similarity among reads for assembly (stringent thresholds) as well as highly variable species may result in datasets in which divergent alleles are lost or divided into separate loci (‘over-splitting’), whereas liberal thresholds increase the risk of paralogous loci being combined into a single locus (‘under-splitting’). Comparisons among datasets or species are therefore potentially biased if different similarity thresholds are applied or if the species differ in levels of within-lineage genetic variation. We examine the impact of a range of similarity thresholds on assembly of empirical short read datasets from populations of four different non-model bird lineages (species or species pairs) with different levels of genetic divergence. We find that, in all species, stringent similarity thresholds result in fewer alleles per locus than more liberal thresholds, which appears to be the result of high levels of over-splitting. The frequency of putative under-splitting, conversely, is low at all thresholds. Inferred genetic distances between individuals, gene tree depths, and estimates of the ancestral mutation-scaled effective population size (θ) differ depending upon the similarity threshold applied. Relative differences in inferences across species differ even when the same threshold is applied, but may be dramatically different when datasets assembled under different thresholds are compared. These differences not only complicate comparisons across species, but also preclude the application of standard mutation rates for parameter calibration. We suggest some best practices for assembling short read data to maximize comparability, such as using more liberal thresholds and examining the impact of different thresholds on each dataset.
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