Ji Hyun Joo, Pertea Mihaela
Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Genome Biol. 2025 Aug 28;26(1):257. doi: 10.1186/s13059-025-03723-2.
Long-read RNA sequencing captures transcripts at full lengths, but existing methods for transcriptome profiling using long-read data often produce inconsistent transcript identification and quantification results. Here, we introduce TranSigner, a tool designed to provide read-level support for transcripts in a given transcriptome. TranSigner consists of three modules: read alignment to transcripts, computation of read-to-transcript compatibility scores, and a guided expectation-maximization algorithm to assign reads to transcripts and estimate their abundances. Using simulated and experimental data from three well-studied organisms-Homo sapiens, Arabidopsis thaliana, and Mus musculus-we show that TranSigner achieves accurate read assignments and abundance estimates.
长读长RNA测序能够全长捕获转录本，但现有的使用长读长数据进行转录组分析的方法往往会产生不一致的转录本鉴定和定量结果。在此，我们介绍TranSigner，这是一种旨在为给定转录组中的转录本提供读段水平支持的工具。TranSigner由三个模块组成：读段与转录本的比对、读段与转录本兼容性得分的计算，以及一种引导期望最大化算法，用于将读段分配给转录本并估计其丰度。利用来自三种深入研究的生物——智人、拟南芥和小家鼠的模拟数据和实验数据，我们表明TranSigner实现了准确的读段分配和丰度估计。
