Quantifying circular RNA expression from RNA-seq data using model-based framework
Quantifying circular RNA expression from RNA-seq data using model-based framework
复制标题
使用基于模型的框架量化 RNA-seq 数据中的环状 RNA 表达
DOI：
10.1093/bioinformatics/btx129
复制
发表时间：
2017-07-15
期刊：
影响因子：
5.8
通讯作者：
Gu, Wanjun
中科院分区：
文献类型：
--
作者：
Li, Musheng;Xie, Xueying;Gu, Wanjun
Circular RNA（circRNA）是一类非编码RNA，广泛表达于多种生物的细胞系和组织中。虽然许多circRNA的确切功能在很大程度上是未知的，但细胞类型和组织特异性circRNA表达暗示了它们在许多生物过程中的关键功能。因此，从高通量RNA-seq数据中定量circRNA表达变得非常重要。虽然已经开发了许多基于模型的方法来量化线性RNA表达的RNA-seq数据，这些方法不适用于circRNA quantitation.Results：在这里，我们提出了一种新的策略，将环状转录本伪线性转录本和估计的表达值的环状和线性转录本使用现有的基于模型的算法，旗鱼。新策略可以从RNA-seq数据准确地估计线性和环状转录物的转录物表达。一些因素，如基因长度，表达量和环状与线性转录本的比例，对环状转录本的定量性能有影响。与基于计数的工具相比，新的计算框架在估计来自模拟和真实的核糖体RNA耗尽（rRNA耗尽）RNA-seq数据集的circRNA表达量方面具有上级性能。另一方面，在来自rRNA耗尽的RNA-seq数据的表达定量中考虑环状转录物显示线性转录物表达的准确性显著增加。我们提出的策略在一个名为Sailfish-cir的程序中实现。
Motivation: Circular RNAs (circRNAs) are a class of non-coding RNAs that are widely expressed in various cell lines and tissues of many organisms. Although the exact function of many circRNAs is largely unknown, the cell type-and tissue-specific circRNA expression has implicated their crucial functions in many biological processes. Hence, the quantification of circRNA expression from high-throughput RNA-seq data is becoming important to ascertain. Although many model-based methods have been developed to quantify linear RNA expression from RNA-seq data, these methods are not applicable to circRNA quantification.Results: Here, we proposed a novel strategy that transforms circular transcripts to pseudo-linear transcripts and estimates the expression values of both circular and linear transcripts using an existing model-based algorithm, Sailfish. The new strategy can accurately estimate transcript expression of both linear and circular transcripts from RNA-seq data. Several factors, such as gene length, amount of expression and the ratio of circular to linear transcripts, had impacts on quantification performance of circular transcripts. In comparison to count-based tools, the new computational framework had superior performance in estimating the amount of circRNA expression from both simulated and real ribosomal RNA-depleted (rRNA-depleted) RNA-seq datasets. On the other hand, the consideration of circular transcripts in expression quantification from rRNA-depleted RNA-seq data showed substantial increased accuracy of linear transcript expression. Our proposed strategy was implemented in a program named Sailfish-cir.