A fast and globally optimal solution for RNA-seq quantification

文献类型： 外文期刊

第一作者： Yi, Huiguang

作者： Yi, Huiguang;Chang, Qing;Yi, Huiguang;Jin, Wenfei

作者机构：

关键词： alignment-free; RNA-seq quantification; globally optimal

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：9.5； 五年影响因子：10.6 ）

ISSN： 1467-5463

年卷期： 2023 年

页码：

收录情况： SCI

摘要： Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation-Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation A(T)Ax = A(T)b. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at .

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