Strategies for achieving high sequencing accuracy for low diversity samples and avoiding sample bleeding using illumina platform.
Strategies for achieving high sequencing accuracy for low diversity samples and avoiding sample bleeding using illumina platform.
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DOI：
10.1371/journal.pone.0120520
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Rowicka M
中科院分区：
文献类型：
--
作者：
Mitra A;Skrzypczak M;Ginalski K;Rowicka M
测序microRNA、简化代表性测序、Hi-C技术和任何需要使用内部条形码的方法都会产生具有低初始序列多样性的测序文库。在Illumina平台上对这样的数据进行测序通常会产生低质量的数据，这是由于Illumina聚类调用算法的限制。此外，即使在不同样品的情况下，这些限制也会导致多重样品分配（样品渗出）的严重不准确性。这种不准确性在临床应用中以及在一些其他领域（例如，检测罕见变体）中是不可接受的。在这里，我们讨论了低多样性样品的质量和样品渗出的问题是如何在初始测序循环期间由流动池上的簇的不正确检测引起的。我们提出了简单的软件修改（长模板协议），克服了这个问题。我们目前的实验结果表明，我们的长模板协议显着提高低多样性样品的数据质量，与标准的分析协议相比，它也大大减少了所有样品的样品出血。为了全面性，我们还讨论和比较实验结果的替代方法来测序低多样性样本。首先，我们讨论了如何低多样性的问题，如果造成的条形码，可以完全避免在条形码设计阶段。第二和第三，我们提出了修改后的准则，这是更严格的比制造商的，混合低多样性的样品与不同的样品和降低集群密度，这在我们的经验一贯产生高质量的数据，从低多样性的样品。第四和第五，我们提出了当测序导致低质量数据以及没有更多生物材料可用时可以应用的救援策略。在这种情况下，我们建议使用我们的长模板方案再次对流通池进行重新杂交和测序。或者，我们讨论如何使用长模板协议从保存的测序图像中重复分析以提高准确性。
Sequencing microRNA, reduced representation sequencing, Hi-C technology and any method requiring the use of in-house barcodes result in sequencing libraries with low initial sequence diversity. Sequencing such data on the Illumina platform typically produces low quality data due to the limitations of the Illumina cluster calling algorithm. Moreover, even in the case of diverse samples, these limitations are causing substantial inaccuracies in multiplexed sample assignment (sample bleeding). Such inaccuracies are unacceptable in clinical applications, and in some other fields (e.g. detection of rare variants). Here, we discuss how both problems with quality of low-diversity samples and sample bleeding are caused by incorrect detection of clusters on the flowcell during initial sequencing cycles. We propose simple software modifications (Long Template Protocol) that overcome this problem. We present experimental results showing that our Long Template Protocol remarkably increases data quality for low diversity samples, as compared with the standard analysis protocol; it also substantially reduces sample bleeding for all samples. For comprehensiveness, we also discuss and compare experimental results from alternative approaches to sequencing low diversity samples. First, we discuss how the low diversity problem, if caused by barcodes, can be avoided altogether at the barcode design stage. Second and third, we present modified guidelines, which are more stringent than the manufacturer’s, for mixing low diversity samples with diverse samples and lowering cluster density, which in our experience consistently produces high quality data from low diversity samples. Fourth and fifth, we present rescue strategies that can be applied when sequencing results in low quality data and when there is no more biological material available. In such cases, we propose that the flowcell be re-hybridized and sequenced again using our Long Template Protocol. Alternatively, we discuss how analysis can be repeated from saved sequencing images using the Long Template Protocol to increase accuracy.
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影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
48
作者：
Crosetto, Nicola;Mitra, Abhishek;Silva, Maria Joao;Bienko, Magda;Dojer, Norbert;Wang, Qi;Karaca, Elif;Chiarle, Roberto;Skrzypczak, Magdalena;Ginalski, Krzysztof;Pasero, Philippe;Rowicka, Maga;Dikic, Ivan
通讯作者：
Dikic, Ivan
影响因子：
3.7
作者：
Krueger, Felix;Andrews, Simon R.;Osborne, Cameron S.
通讯作者：
Osborne, Cameron S.
影响因子：
64.8
作者：
Altshuler, D;Pollara, VJ;Lander, ES
通讯作者：
Lander, ES
DOI：
10.1093/bioinformatics/btp383
发表时间：
2009-09-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Whiteford N;Skelly T;Curtis C;Ritchie ME;Löhr A;Zaranek AW;Abnizova I;Brown C
通讯作者：
Brown C