A novel post hoc method for detecting index switching finds no evidence for increased switching on the Illumina HiSeq X
A novel post hoc method for detecting index switching finds no evidence for increased switching on the Illumina HiSeq X
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DOI：
10.1111/1755-0998.12713
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发表时间：
2018-01-01
影响因子：
7.7
通讯作者：
Rieseberg, Loren H.
中科院分区：
文献类型：
--
作者：
Owens, Gregory L.;Todesco, Marco;Rieseberg, Loren H.
使用Illumina HiSeq平台的高通量测序是一种普遍和关键的分子生态学资源，并为许多最新进展提供了数据基础。最近的一项研究表明，在新版HiSeq平台中，“索引切换”（即读数被错误地归因于错误的样本）的发生率可能更高。这有可能使该领域已发表的和正在进行的工作无效。在这里，我们在一个范例全基因组霰弹枪数据集中测试了索引切换的证据，该数据集在Illumina HiSeq 2500和Illumina HiSeq X上测序，这些数据集应该没有问题，而Illumina HiSeq X可能有问题。我们利用了不平衡的杂合子，这可能是由指数转换产生的，并询问未测序的等位基因是否比基于等位基因频率的预期更有可能在同一通道的其他样本中被发现。虽然我们通过模拟验证了该方法的灵敏度，但我们发现HiSeq 2500和HiSeq X都没有指数切换的证据。这表明，值得庆幸的是，索引切换可能不是HiSeq X序列数据中普遍存在的问题。最后，我们提供了应用我们方法的脚本，以便可以在其他数据集中测试索引切换。
High-throughput sequencing using the Illumina HiSeq platform is a pervasive and critical molecular ecology resource, and has provided the data underlying many recent advances. A recent study has suggested that "index switching," where reads are misattributed to the wrong sample, may be higher in new versions of the HiSeq platform. This has the potential to invalidate both published and in-progress work across the field. Here, we test for evidence of index switching in an exemplar whole-genome shotgun data set sequenced on both the Illumina HiSeq 2500, which should not have the problem, and the Illumina HiSeq X, which may. We leverage unbalanced heterozygotes, which may be produced by index switching, and ask whether the undersequenced allele is more likely to be found in other samples in the same lane than expected based on the allele frequency. Although we validate the sensitivity of this method using simulations, we find that neither the HiSeq 2500 nor the HiSeq X has evidence of index switching. This suggests that, thankfully, index switching may not be a ubiquitous problem in HiSeq X sequence data. Lastly, we provide scripts for applying our method so that index switching can be tested for in other data sets.