Jones Daniel C, Ruzzo Walter L
Paul G. Allen School of Computer Science & Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350, USA.
NAR Genom Bioinform. 2021 May 25;3(2):lqab046. doi: 10.1093/nargab/lqab046. eCollection 2021 Jun.
The analysis of mRNA transcript abundance with RNA-Seq is a central tool in molecular biology research, but often analyses fail to account for the uncertainty in these estimates, which can be significant, especially when trying to disentangle isoforms or duplicated genes. Preserving uncertainty necessitates a full probabilistic model of the all the sequencing reads, which quickly becomes intractable, as experiments can consist of billions of reads. To overcome these limitations, we propose a new method of approximating the likelihood function of a sparse mixture model, using a technique we call the Pólya tree transformation. We demonstrate that substituting this approximation for the real thing achieves most of the benefits with a fraction of the computational costs, leading to more accurate detection of differential transcript expression and transcript coexpression.
利用RNA测序分析mRNA转录本丰度是分子生物学研究的核心工具,但这类分析往往未能考虑到这些估计值中的不确定性,而这种不确定性可能很大,尤其是在试图区分异构体或重复基因时。要保留不确定性就需要对所有测序读数建立完整的概率模型,但由于实验可能包含数十亿条读数,这很快就会变得难以处理。为克服这些限制,我们提出了一种新方法,即使用一种我们称为波利亚树变换的技术来近似稀疏混合模型的似然函数。我们证明,用这种近似方法替代实际模型,能以一小部分计算成本实现大部分益处,从而更准确地检测差异转录本表达和转录本共表达。