Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results
Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results
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DOI：
10.1093/bioinformatics/btv377
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发表时间：
2015-11-15
期刊：
影响因子：
5.8
通讯作者：
Piccolo, Stephen R.
中科院分区：
文献类型：
--
作者：
Rahman, Mumtahena;Jackson, Laurie K.;Piccolo, Stephen R.
动机：癌症基因组图谱 (TCGA) RNA 测序数据广泛用于研究。 TCGA 提供“3 级”数据，这些数据已使用特定于该资源的管道进行处理。然而，我们使用实验得出的数据发现，该管道产生的基因表达值在生物重复中差异很大。此外，一些 RNA 测序分析工具需要基于整数的读取计数，而 3 级数据不提供这些计数。作为替代方案，我们使用 Rsubread 软件包重新处理了 24 种癌症类型的 9264 个肿瘤样本和 741 个正常样本的数据。我们还整理了这些样本相应的临床数据。我们将这些数据作为社区资源提供。结果：我们比较了使用任一管道处理的 TCGA 样本，发现与 TCGA 管道相比，Rsubread 管道在重复样本中产生的零表达基因更少，且表达水平更一致。此外，我们使用基因组特征方法来估计 662 个乳腺肿瘤样本的 HER2 (ERBB2) 激活状态，发现 Rsubread 数据可以对 HER2 通路活性做出更强的预测。最后，我们使用两个管道的数据根据组织学类型对 575 个肺癌样本进行分类。该分析确定了可能影响肺癌组织学的各种非编码 RNA。
Motivation: The Cancer Genome Atlas (TCGA) RNA-Sequencing data are used widely for research. TCGA provides 'Level 3' data, which have been processed using a pipeline specific to that resource. However, we have found using experimentally derived data that this pipeline produces gene-expression values that vary considerably across biological replicates. In addition, some RNA-Sequencing analysis tools require integer-based read counts, which are not provided with the Level 3 data. As an alternative, we have reprocessed the data for 9264 tumor and 741 normal samples across 24 cancer types using the Rsubread package. We have also collated corresponding clinical data for these samples. We provide these data as a community resource.Results: We compared TCGA samples processed using either pipeline and found that the Rsubread pipeline produced fewer zero-expression genes and more consistent expression levels across replicate samples than the TCGA pipeline. Additionally, we used a genomic-signature approach to estimate HER2 (ERBB2) activation status for 662 breast-tumor samples and found that the Rsubread data resulted in stronger predictions of HER2 pathway activity. Finally, we used data from both pipelines to classify 575 lung cancer samples based on histological type. This analysis identified various non-coding RNA that may influence lung-cancer histology.