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<p>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—<i>Homo sapiens</i>, <i>Arabidopsis thaliana</i>, and <i>Mus musculus</i>—we show that TranSigner achieves accurate read assignments and abundance estimates.</p>

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Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月28日

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  • Hyun Joo Ji,
  • Mihaela Pertea

摘要

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.