Improving gene isoform quantification with miniQuant

RNA sequencing has been widely applied for gene isoform quantification, but limitations exist in quantifying isoforms of complex genes accurately, especially for short reads. Here we identify genes that are difficult to quantify accurately with short reads and illustrate the information benefit of using long reads to quantify these regions. We present miniQuant, which ranks genes with quantification errors caused by the ambiguity of read alignments and integrates the complementary strengths of long reads and short reads with optimal combination in a gene- and data-specific manner to achieve more accurate quantification. These results are supported by rigorous mathematical proofs, validated with a wide range of simulation data, experimental validations and more than 17,000 public datasets from GTEx, TCGA and ENCODE consortia. We demonstrate miniQuant can uncover isoform switches during the differentiation of human embryonic stem cells to pharyngeal endoderm and primordial germ cell-like cells.

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Publication Details

Journal
Nature Biotechnology
Published
2025-06-03
DOI
https://doi.org/10.1038/s41587-025-02633-9
Citations
7
Primary Topic
Genomics and Chromatin Dynamics
Type
article
Field-Weighted Citation Impact
5.64
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article

Improving gene isoform quantification with miniQuant

Vittorio Sebastiano, Kin Fai Au, Nancy R. Zhang, Chia‐Lin Wei et al.
7 citations
Nature Biotechnology
Genomics and Chromatin Dynamics
5.64
article

Improving gene isoform quantification with miniQuant

Vittorio Sebastiano, Kin Fai Au, Nancy R. Zhang, Chia‐Lin Wei, Aifu Li, Chi Song, Xiaoyu Cai, Puwen Tan, Seyed Amir Malekpour, Andrea Cipriano, Andrew L. Thurman, Haoran Li, Ying Zhang, Yue Zhao, Yunhao Wang, Dingjie Wang, Qi Gao, Roberta Sala
article en
7 citations

Abstract

RNA sequencing has been widely applied for gene isoform quantification, but limitations exist in quantifying isoforms of complex genes accurately, especially for short reads. Here we identify genes that are difficult to quantify accurately with short reads and illustrate the information benefit of using long reads to quantify these regions. We present miniQuant, which ranks genes with quantification errors caused by the ambiguity of read alignments and integrates the complementary strengths of long reads and short reads with optimal combination in a gene- and data-specific manner to achieve more accurate quantification. These results are supported by rigorous mathematical proofs, validated with a wide range of simulation data, experimental validations and more than 17,000 public datasets from GTEx, TCGA and ENCODE consortia. We demonstrate miniQuant can uncover isoform switches during the differentiation of human embryonic stem cells to pharyngeal endoderm and primordial germ cell-like cells.

Nature BiotechnologyVol. 44(3)
University of Iowa (US), California Institute for Regenerative Medicine (US), University of Michigan (US), Jackson Laboratory (US), The Ohio State University (US), University of Pennsylvania (US), Stanford University (US)
Openalex Percentile: Top 4%
Genomics and Chromatin Dynamics
5.64
DOIOpen Access PDFOpenAlex
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