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Peer-reviewedCC BY-NCBriefings in bioinformatics

IsoPrism predicts isoform usage from short-read transcriptomes through structure-guided competitive allocation.

  • Peiyi Yan1
  • Jingchen Liu2
  • Xuejian Cui1
  • Xiaoqi Liu2
  • Lei Shi3
  • Liang Cheng4,5
5 affiliations
  1. 1Faculty of Computing, Harbin Institute of Technology, 92 West Dazhi Street, Nangang District, Harbin 150001, China.
  2. 2Department of Orthopedic Surgery, The Second Affiliated Hospital, Harbin Medical University, 246 Xuefu Road, Nangang District, Harbin 150086, China.
  3. 3Department of Orthopedics, Changzheng Hospital, 415 Fengyang Road, Huangpu District, Shanghai 200003, China.
  4. 4College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin 150081, China.
  5. 5NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin 150081, China.
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Abstract

Short-read (SR) single-cell and spatial transcriptomics characterize gene expression but provide limited resolution of within-gene isoform usage. Here we present IsoPrism, a structure-guided competitive allocation framework that predicts isoform proportions from gene-level expression. IsoPrism combines individual expression and local expression context with transcript structural annotations, using shared iterative updates to preserve non-negative, gene-normalized proportions. Joint supervision from individual long-read (LR) counts and group-level isoform usage enables prediction in samples without matched LR measurements. Across five transfer scenarios derived from three paired transcriptomic resources, IsoPrism achieved the highest mean dominant isoform accuracy, compositional similarity, and cluster-level allocation correlation among eight evaluated methods. Ablation analyses supported complementary contributions from structural queries, expression context, iterative allocation, and dual-resolution supervision. Applied to a developing mouse cortex atlas, IsoPrism identified stage- and cell-type-associated isoform-usage patterns, including variation in the predicted relative usage of two Pkm transcripts. IsoPrism enables isoform-usage analysis of SR transcriptomes by transferring information from paired LR references, extending the analytical value of existing single-cell and spatial gene-expression atlases.

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Cite

BibTeXRIS
BibTeX
@article{yan2026isoprism,
  title = {{IsoPrism predicts isoform usage from short-read transcriptomes through structure-guided competitive allocation.}},
  author = {Peiyi Yan and Jingchen Liu and Xuejian Cui and Xiaoqi Liu and Lei Shi and Liang Cheng},
  journal = {Briefings in bioinformatics},
  year = {2026},
  month = nov,
  doi = {10.1093/bib/bbag621},
  url = {https://europepmc.org/article/MED/42969855},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - IsoPrism predicts isoform usage from short-read transcriptomes through structure-guided competitive allocation.
AU  - Peiyi Yan
AU  - Jingchen Liu
AU  - Xuejian Cui
AU  - Xiaoqi Liu
AU  - Lei Shi
AU  - Liang Cheng
PY  - 2026
DA  - 2026/11/01
JO  - Briefings in bioinformatics
DO  - 10.1093/bib/bbag621
UR  - https://europepmc.org/article/MED/42969855
AB  - Short-read (SR) single-cell and spatial transcriptomics characterize gene expression but provide limited resolution of within-gene isoform usage. Here we present IsoPrism, a structure-guided competitive allocation framework that predicts isoform proportions from gene-level expression. IsoPrism combines individual expression and local expression context with transcript structural annotations, using shared iterative updates to preserve non-negative, gene-normalized proportions. Joint supervision from individual long-read (LR) counts and group-level isoform usage enables prediction in samples without matched LR measurements. Across five transfer scenarios derived from three paired transcriptomic resources, IsoPrism achieved the highest mean dominant isoform accuracy, compositional similarity, and cluster-level allocation correlation among eight evaluated methods. Ablation analyses supported complementary contributions from structural queries, expression context, iterative allocation, and dual-resolution supervision. Applied to a developing mouse cortex atlas, IsoPrism identified stage- and cell-type-associated isoform-usage patterns, including variation in the predicted relative usage of two Pkm transcripts. IsoPrism enables isoform-usage analysis of SR transcriptomes by transferring information from paired LR references, extending the analytical value of existing single-cell and spatial gene-expression atlases.
SN  - 1467-5463
SN  - 1477-4054
ER  -