Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, 97 Buxin Rd, Shenzhen, 518000, Guangdong, China.
School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Blvd, Shenzhen 518055, Guangdong, China.
Brief Bioinform. 2023 Sep 20;24(5). doi: 10.1093/bib/bbad298.
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 ATAx = ATb. 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 https://github.com/yhg926/TQSLE.
基于比对的 RNA-seq 定量方法通常需要在估计转录本丰度之前进行耗时的比对过程。相比之下，无比对的 RNA-seq 定量方法绕过了这一步骤，从而显著提高了速度。现有的无比对方法依赖于期望最大化（EM）算法来估计转录本丰度。然而，EM 算法仅保证局部最优解，通过寻找全局最优解，可以进一步提高准确性。在这项研究中，我们提出了 TQSLE，这是第一个提供转录本丰度估计全局最优解的无比对 RNA-seq 定量方法。TQSLE 采用两步法：首先，它为参考转录组构建 k-mer 频率矩阵 A 和为 RNA-seq 读取构建 k-mer 频率向量 b；然后，它通过求解线性方程 ATAx = ATb 直接估计转录本丰度。我们使用模拟和真实的 RNA-seq 数据集评估了 TQSLE 的性能，结果表明，尽管与其他无比对方法的速度相当，但 TQSLE 在准确性方面表现更优。TQSLE 可在 https://github.com/yhg926/TQSLE 上免费获取。
