Chen Chun, Le Huong, Goudar Chetan T
Drug Substance Technologies, Process Development, Amgen Inc., 1 Amgen Center Drive, Thousand Oaks, California, 91320.
Biotechnol Bioeng. 2017 Jul;114(7):1603-1613. doi: 10.1002/bit.26290. Epub 2017 Apr 18.
RNA-Seq is a powerful transcriptomics tool for mammalian cell culture process development. Successful RNA-Seq data analysis requires a high quality reference for read mapping and gene expression quantification. Currently, there are two public genome references for Chinese hamster ovary (CHO) cells, the predominant mammalian cell line in the biopharmaceutical industry. In this study, we compared these two references by analyzing 60 RNA-Seq samples from a variety of CHO cell culture conditions. Among the 20,891 common genes in both references, we observed that 31.5% have more than 7.1% quantification differences, implying gene definition differences in the two references. We propose a framework to quantify this difference using two metrics, Consistency and Stringency, which account for the average quantification difference between the two references over all samples, and the sample-specific effect on the quantification result, respectively. These two metrics can be used to identify potential genes for future gene model improvement and to understand the reliability of differentially expressed genes identified by RNA-Seq data analysis. Before a more comprehensive genome reference for CHO cells emerges, the strategy proposed in this study can enable more robust transcriptome analysis from CHO cell RNA-Seq data. Biotechnol. Bioeng. 2017;114: 1603-1613. © 2017 Wiley Periodicals, Inc.
RNA测序是用于哺乳动物细胞培养工艺开发的一种强大的转录组学工具。成功的RNA测序数据分析需要高质量的参考序列用于读段比对和基因表达定量。目前,有两个公开的中国仓鼠卵巢(CHO)细胞基因组参考序列,CHO细胞是生物制药行业中主要的哺乳动物细胞系。在本研究中,我们通过分析来自多种CHO细胞培养条件的60个RNA测序样本,对这两个参考序列进行了比较。在两个参考序列共有的20,891个基因中,我们观察到31.5%的基因定量差异超过7.1%,这意味着两个参考序列在基因定义上存在差异。我们提出了一个框架,使用两个指标来量化这种差异,即一致性和严格性,它们分别表示所有样本中两个参考序列之间的平均定量差异以及样本对定量结果的特定影响。这两个指标可用于识别未来基因模型改进的潜在基因,并了解RNA测序数据分析所鉴定的差异表达基因的可靠性。在出现更全面的CHO细胞基因组参考序列之前,本研究中提出的策略能够实现基于CHO细胞RNA测序数据的更稳健的转录组分析。《生物技术与生物工程》2017年;114卷:1603 - 1613页。© 2017威利期刊公司