用miniQuant改进基因异构体定量，这一成果由密歇根大学Kin Fai Au课题组经过不懈努力而取得。该研究于2025年6月3日发表于国际一流学术期刊《自然—生物技术》杂志上。

在这里，课题组研究人员确定了难以用短读取准确量化的基因，并阐明了对长读取进行主题化以量化这些区域的信息益处。研究人员提出了miniQuant，它对由读取序列歧义引起的定量误差基因进行排序，并以基因和数据特定的方式整合长读取和短读取的互补优势，以最佳组合，以实现更准确的定量。这些结果得到了严格的数学证明的支持，并得到了广泛的模拟数据、实验验证和来自GTEx、TCGA和ENCODE联盟的超过17,000个公共数据集的验证。该研究组证明miniQuant可以揭示人胚胎干细胞向咽部内胚层和原始生殖细胞样细胞分化过程中的异型开关。

研究人员表示，RNA测序已广泛应用于基因异构体的定量，但复杂基因的异构体定量存在一定的局限性，尤其是短读段。

附：英文原文

Title: Improving gene isoform quantification with miniQuant

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

Issue&Volume: 2025-06-03

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.

DOI: 10.1038/s41587-025-02633-9

Source: https://www.nature.com/articles/s41587-025-02633-9