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Testing of Alignment Parameters for Ancient Samples: Evaluating and Optimizing Mapping Parameters for Ancient Samples Using the TAPAS Tool

机译:测试古代样本的比对参数:使用TAPAS工具评估和优化古代样本的映射参数

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High-throughput sequence data retrieved from ancient or other degraded samples has led to unprecedented insights into the evolutionary history of many species, but the analysis of such sequences also poses specific computational challenges. The most commonly used approach involves mapping sequence reads to a reference genome. However, this process becomes increasingly challenging with an elevated genetic distance between target and reference or with the presence of contaminant sequences with high sequence similarity to the target species. The evaluation and testing of mapping efficiency and stringency are thus paramount for the reliable identification and analysis of ancient sequences. In this paper, we present ‘TAPAS’, (Testing of Alignment Parameters for Ancient Samples), a computational tool that enables the systematic testing of mapping tools for ancient data by simulating sequence data reflecting the properties of an ancient dataset and performing test runs using the mapping software and parameter settings of interest. We showcase TAPAS by using it to assess and improve mapping strategy for a degraded sample from a banded linsang ( Prionodon linsang ), for which no closely related reference is currently available. This enables a 1.8-fold increase of the number of mapped reads without sacrificing mapping specificity. The increase of mapped reads effectively reduces the need for additional sequencing, thus making more economical use of time, resources, and sample material.
机译:从古代或其他退化样本中获取的高通量序列数据已导致对许多物种进化史的空前洞察,但对此类序列的分析也带来了特定的计算挑战。最常用的方法涉及将序列读数映射到参考基因组。然而,随着靶标与参照之间遗传距离的增加或存在与靶标物种具有高度序列相似性的污染物序列,该过程变得越来越具有挑战性。因此,对映射效率和严格性的评估和测试对于可靠地识别和分析古代序列至关重要。在本文中,我们介绍了“ TAPAS”(古代样品的比对参数测试),该计算工具可通过模拟反映古代数据集属性的序列数据并执行测试运行来对古代数据的制图工具进行系统测试所需的映射软件和参数设置。我们通过使用TAPAS评估和改进带状临(Prionodon linsang)的降解样品的制图策略来展示TAPAS,目前尚无密切相关的参考资料。这样可以在不牺牲映射特异性的前提下,将映射的读取数增加1.8倍。映射读段的增加有效地减少了对额外测序的需求,从而更经济地利用了时间,资源和样品材料。

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