Analysis of high-depth sequence data for studying viral diversity: a comparison of next generation sequencing platforms using Segminator II.
Analysis of high-depth sequence data for studying viral diversity: a comparison of next generation sequencing platforms using Segminator II.
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DOI：
10.1186/1471-2105-13-47
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发表时间：
2012-03-23
影响因子：
3
通讯作者：
Robertson DL
中科院分区：
文献类型：
--
作者：
Archer J;Baillie G;Watson SJ;Kellam P;Rambaut A;Robertson DL
下一代测序提供了对病毒种群中存在的变异的详细见解，引入了反应性和预测性治疗策略的可能性。然而，目前的软件工具需要扩大规模，以适应高深度的病毒数据集，这些数据集通常是在时间或空间上联系的。此外，由于新的测序平台和化学试剂的发展，每个都有隐含的优势和劣势，这将有助于研究人员能够定期比较和组合来自不同平台/化学试剂的数据集。特别是，与特定测序过程相关的误差必须量化，以便识别真正的生物学变异。Segminator II的开发是为了允许对来自不同来源的数据集进行有效比较。我们通过比较在454 Life Sciences和Illumina平台上测序的12个H1N1流感样本的大数据集来证明其使用，允许对平台误差进行量化。对于不匹配，中位数错误率分别为0.10和0.12%，表明两个平台的表现相似。454个数据中的插入和删除中位错误率（分别为0.3和0.2%）显著高于Illumina数据中的插入和删除中位错误率（分别为0.004和0.006%）。与先前的观察结果一致，这些较高的速率与454平台上的均聚物拉伸密切相关。在这些区域之外，两个平台具有相似的indel错误配置文件。此外，我们将我们的软件应用于低频变体的识别。我们已经证明，使用Segminator II，可以使用来自两个不同平台的数据区分平台特定误差和生物变异。我们已经使用这种方法来量化454和Illumina平台中与基因组位置以及读取位置相关的错误数量。鉴于下一代数据在耐药性分析和疫苗试验中越来越重要，该软件将对病原体研究界有用。包含源代码和jar文件的zip文件可从http://www.bioinf.manchester.ac.uk/segminator/免费下载。
Next generation sequencing provides detailed insight into the variation present within viral populations, introducing the possibility of treatment strategies that are both reactive and predictive. Current software tools, however, need to be scaled up to accommodate for high-depth viral data sets, which are often temporally or spatially linked. In addition, due to the development of novel sequencing platforms and chemistries, each with implicit strengths and weaknesses, it will be helpful for researchers to be able to routinely compare and combine data sets from different platforms/chemistries. In particular, error associated with a specific sequencing process must be quantified so that true biological variation may be identified. Segminator II was developed to allow for the efficient comparison of data sets derived from different sources. We demonstrate its usage by comparing large data sets from 12 influenza H1N1 samples sequenced on both the 454 Life Sciences and Illumina platforms, permitting quantification of platform error. For mismatches median error rates at 0.10 and 0.12%, respectively, suggested that both platforms performed similarly. For insertions and deletions median error rates within the 454 data (at 0.3 and 0.2%, respectively) were significantly higher than those within the Illumina data (0.004 and 0.006%, respectively). In agreement with previous observations these higher rates were strongly associated with homopolymeric stretches on the 454 platform. Outside of such regions both platforms had similar indel error profiles. Additionally, we apply our software to the identification of low frequency variants. We have demonstrated, using Segminator II, that it is possible to distinguish platform specific error from biological variation using data derived from two different platforms. We have used this approach to quantify the amount of error present within the 454 and Illumina platforms in relation to genomic location as well as location on the read. Given that next generation data is increasingly important in the analysis of drug-resistance and vaccine trials, this software will be useful to the pathogen research community. A zip file containing the source code and jar file is freely available for download from http://www.bioinf.manchester.ac.uk/segminator/.
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