TransGram for heterogeneity-aware transcriptome reconstruction from long read RNA-Seq data
Abstract
The advancement of long-read RNA sequencing technologies leads to a bright future for transcriptome analysis. Reconstructing alternative isoforms from long-read RNA data is crucial in transcriptomic studies and poses computational challenges. In this research, we study the heterogeneity of current long reads RNA-seq data, and categorize the sequencing data into two types according to the fraction of the alternative splicing events appeared in the extracted reliably supported sub-path of the splicing graph. Leveraging the heterogeneity, we develop TransGram, an algorithm to reconstruct transcriptome from long RNA-seq reads, which employs the traditional assemble strategy to reconstruct the transcript-representing paths within the splicing graph, and incorporates a machine-learning-based model trained on splice graph-derived features to eliminate false positive transcripts. TransGram supports both annotation-free and annotation-guided modes. Comprehensive evaluations across multiple datasets and conditions demonstrate that TransGram consistently outperforms widely used tools StringTie2, IsoQuant, Bambu, and ESPRESSO, in terms of precision and recall.
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BibTeX
@article{ren2026transgram,
title = {{TransGram for heterogeneity-aware transcriptome reconstruction from long read RNA-Seq data}},
author = {Zitong Ren and Junhai Qi and Renmin Han and Shengkai Guo and Jiahao Li and Mingyu Liu and Guojun Li and Ting Yu},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78013-x},
url = {https://www.nature.com/articles/s41467-026-78013-x},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - TransGram for heterogeneity-aware transcriptome reconstruction from long read RNA-Seq data
AU - Zitong Ren
AU - Junhai Qi
AU - Renmin Han
AU - Shengkai Guo
AU - Jiahao Li
AU - Mingyu Liu
AU - Guojun Li
AU - Ting Yu
PY - 2026
DA - 2026/10/05
JO - Nature Communications
DO - 10.1038/s41467-026-78013-x
UR - https://www.nature.com/articles/s41467-026-78013-x
AB - The advancement of long-read RNA sequencing technologies leads to a bright future for transcriptome analysis. Reconstructing alternative isoforms from long-read RNA data is crucial in transcriptomic studies and poses computational challenges. In this research, we study the heterogeneity of current long reads RNA-seq data, and categorize the sequencing data into two types according to the fraction of the alternative splicing events appeared in the extracted reliably supported sub-path of the splicing graph. Leveraging the heterogeneity, we develop TransGram, an algorithm to reconstruct transcriptome from long RNA-seq reads, which employs the traditional assemble strategy to reconstruct the transcript-representing paths within the splicing graph, and incorporates a machine-learning-based model trained on splice graph-derived features to eliminate false positive transcripts. TransGram supports both annotation-free and annotation-guided modes. Comprehensive evaluations across multiple datasets and conditions demonstrate that TransGram consistently outperforms widely used tools StringTie2, IsoQuant, Bambu, and ESPRESSO, in terms of precision and recall.
SN - 2041-1723
ER -