GemSIM: general, error-model based simulator of next-generation sequencing data.
GemSIM: general, error-model based simulator of next-generation sequencing data.
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DOI：
10.1186/1471-2164-13-74
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发表时间：
2012-02-15
期刊：
影响因子：
4.4
通讯作者：
Thomas T
中科院分区：
文献类型：
--
作者：
McElroy KE;Luciani F;Thomas T
GemSIM，或通用错误模型模拟器，是下一代测序模拟器，能够生成与通用格式SAM和FASTQ（包括Illumina和Roche/454）兼容的任何测序技术的单端或成对端读取。GemSIM创建并使用经验推导的、基于序列上下文的误差模型来真实地模拟单个测序运行和/或技术。还使用了经验片段长度和质量评分分布。Reads可以从一个或多个基因组或单倍型集合中提取，促进深度测序，宏基因组和重测序项目的模拟。我们通过从两个不同的Illumina测序运行和一个Roche/454运行中推导误差模型，并比较和对比每次运行的结果误差曲线，来证明GemSIM的价值。总体错误率在单个Illumina运行之间，在每对的第一次和第二次读取之间，以及在Illumina和Roche/454技术的数据集之间都存在显着差异。在Roche/454中，Indels明显比Illumina更频繁，而且两种技术在每次读取结束时的错误率都有所增加。通过分析细菌单倍型混合物的模拟测序数据，研究了这些不同谱对低频snp呼叫准确性的影响。一般来说，使用VarScan进行snp调用仅对频率为bbbb3 %的snp准确，与使用哪种误差模型模拟数据无关。错误配置文件之间的差异与VarScan的“最小平均质量”参数相互作用强烈，导致不同测序运行的不同最佳设置。下一代测序在评估遗传多样性方面具有前所未有的潜力，然而，分析是复杂的，因为即使在同一技术的不同运行之间，误差谱也可能存在显着差异。GemSIM的模拟可以帮助克服这个问题，通过提供对单个测序运行的错误概况的见解，并允许研究人员评估这些错误对下游数据分析的影响。
GemSIM, or General Error-Model based SIMulator, is a next-generation sequencing simulator capable of generating single or paired-end reads for any sequencing technology compatible with the generic formats SAM and FASTQ (including Illumina and Roche/454). GemSIM creates and uses empirically derived, sequence-context based error models to realistically emulate individual sequencing runs and/or technologies. Empirical fragment length and quality score distributions are also used. Reads may be drawn from one or more genomes or haplotype sets, facilitating simulation of deep sequencing, metagenomic, and resequencing projects. We demonstrate GemSIM's value by deriving error models from two different Illumina sequencing runs and one Roche/454 run, and comparing and contrasting the resulting error profiles of each run. Overall error rates varied dramatically, both between individual Illumina runs, between the first and second reads in each pair, and between datasets from Illumina and Roche/454 technologies. Indels were markedly more frequent in Roche/454 than Illumina and both technologies suffered from an increase in error rates near the end of each read. The effects of these different profiles on low-frequency SNP-calling accuracy were investigated by analysing simulated sequencing data for a mixture of bacterial haplotypes. In general, SNP-calling using VarScan was only accurate for SNPs with frequency > 3%, independent of which error model was used to simulate the data. Variation between error profiles interacted strongly with VarScan's 'minumum average quality' parameter, resulting in different optimal settings for different sequencing runs. Next-generation sequencing has unprecedented potential for assessing genetic diversity, however analysis is complicated as error profiles can vary noticeably even between different runs of the same technology. Simulation with GemSIM can help overcome this problem, by providing insights into the error profiles of individual sequencing runs and allowing researchers to assess the effects of these errors on downstream data analysis.
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