A new approach to bias correction in RNA-Seq.
A new approach to bias correction in RNA-Seq.
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DOI：
10.1093/bioinformatics/bts055
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发表时间：
2012-04-01
期刊：
影响因子：
--
通讯作者：
Katze MG
中科院分区：
文献类型：
--
作者：
Jones DC;Ruzzo WL;Peng X;Katze MG
动机：RNA-Seq实验中序列丰度的定量经常被方案特异性序列偏倚混淆。偏倚的确切来源是未知的，但可能受到聚合酶链反应扩增或不同引物亲和力和混合物的影响。其结果是在许多应用中降低了准确性，例如从头基因注释和转录物定量。结果：我们提出了一种新的方法来衡量和纠正这些影响，使用一个简单的图形模型。我们的模型不依赖于现有的基因注释，模型选择是自动执行的，使其适用于很少的假设。我们在多个数据集上评估了我们的方法，并通过多个标准，证明它有效地降低了偏差并提高了均匀性。此外，我们提供的理论和实证结果表明，该方法是不可能有任何影响的无偏数据，这表明它可以应用的虚假调整的风险很小。可用性：该方法在seqbias R/Bioconductor软件包中实现，可在LGPL许可下从www.example.com免费获得dcjones@cs.washington.edu方式：http://bioconductor.org补充信息：补充数据可在生物信息学在线获得。
Motivation: Quantification of sequence abundance in RNA-Seq experiments is often conflated by protocol-specific sequence bias. The exact sources of the bias are unknown, but may be influenced by polymerase chain reaction amplification, or differing primer affinities and mixtures, for example. The result is decreased accuracy in many applications, such as de novo gene annotation and transcript quantification. Results: We present a new method to measure and correct for these influences using a simple graphical model. Our model does not rely on existing gene annotations, and model selection is performed automatically making it applicable with few assumptions. We evaluate our method on several datasets, and by multiple criteria, demonstrating that it effectively decreases bias and increases uniformity. Additionally, we provide theoretical and empirical results showing that the method is unlikely to have any effect on unbiased data, suggesting it can be applied with little risk of spurious adjustment. Availability: The method is implemented in the seqbias R/Bioconductor package, available freely under the LGPL license from http://bioconductor.org Contact: dcjones@cs.washington.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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