Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China.
School of Mathematics, Shandong University, Jinan, Shandong 250100, China.
Genome Res. 2022 Jul 27;32(7):1398-1407. doi: 10.1101/gr.276434.121.
Assembling RNA-seq reads into full-length transcripts is crucial in transcriptomic studies and poses computational challenges. Here we present TransMeta, a simple and robust algorithm that simultaneously assembles RNA-seq reads from multiple samples. TransMeta is designed based on the newly introduced vector-weighted splicing graph model, which enables accurate reconstruction of the consensus transcriptome via incorporating a cosine similarity-based combing strategy and a newly designed label-setting path-searching strategy. Tests on both simulated and real data sets show that TransMeta consistently outperforms PsiCLASS, StringTie2 plus its merge mode, and Scallop plus TACO, the most popular tools, in terms of precision and recall under a wide range of coverage thresholds at the meta-assembly level. Additionally, TransMeta consistently shows superior performance at the individual sample level.
将 RNA-seq reads 组装成全长转录本在转录组学研究中至关重要，但这也带来了计算上的挑战。在这里，我们介绍了 TransMeta，这是一种简单而强大的算法，可同时组装来自多个样本的 RNA-seq reads。TransMeta 是基于新引入的向量加权剪接图模型设计的，该模型通过采用基于余弦相似度的梳状策略和新设计的标签设置路径搜索策略，能够准确重建共识转录组。在模拟和真实数据集上的测试表明，在元组装水平上，在广泛的覆盖阈值范围内，TransMeta 在精度和召回率方面始终优于最流行的工具 PsiCLASS、StringTie2 及其合并模式以及 Scallop 和 TACO。此外，TransMeta 在单个样本水平上也始终表现出卓越的性能。
