Document Type
Article
Publication Date
3-1-2026
Original Citation
Li H,
Wang D,
Gao Q,
Tan P,
Wang Y,
Cai X,
Li A,
Zhao Y,
Thurman A,
Malekpour S,
Zhang Y,
Sala R,
Cipriano A,
Wei C,
Sebastiano V,
Song C,
Zhang N,
Au K.
Improving gene isoform quantification with miniQuant. Nat Biotechnol. 2026;44(3):477-89.
Keywords
JGM, Humans, Protein Isoforms, Sequence Analysis, RNA, Cell Differentiation, Software, Human Embryonic Stem Cells
JAX Source
Nat Biotechnol. 2026;44(3):477-89.
ISSN
1546-1696
PMID
40461779
DOI
https://doi.org/10.1038/s41587-025-02633-9
Grant
R01HG011469 to P.T., Q.G., C.-L.W., V.S., R.S., A.C. and C.S.; R01HG006137 and R56AG081351 to N.R.Z.
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.