miRglmm：异构体水平计数的广义线性混合模型改善了对mirna水平差异表达的估计，并揭示了异构体之间可变的差异表达

IF 10.1 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Genome Biology Pub Date : 2025-04-22 DOI:10.1186/s13059-025-03549-y

Andrea M. Baran, Arun H. Patil, Ernesto Aparicio-Puerta, Seong-Hwan Jun, Marc K. Halushka, Matthew N. McCall

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引用次数: 0

摘要

microRNA -seq数据是通过将不同microRNA转录异构体（称为isomir）的小RNA测序读数与已知的microRNA进行比对而产生的。聚合到microrna水平计数丢弃了信息，违反了为mRNA-seq数据开发的差异表达方法的核心假设。我们建立了miRglmm，一种microRNA-seq数据的差异表达方法，它使用异构体水平计数的广义线性混合模型，便于检测具有差异表达或差异异构体使用的miRNA。我们证明了miRglmm在估计miRNA的差异表达方面优于当前的差异表达方法，无论是否存在差异isomiR的使用，同时提供了isomiR水平差异表达的估计。

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miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs

MicroRNA-seq data is produced by aligning small RNA sequencing reads of different microRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression methods developed for mRNA-seq data. We establish miRglmm, a differential expression method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current differential expression methods in estimating differential expression for miRNA, whether or not there is differential isomiR usage, and simultaneously provides estimates of isomiR-level differential expression.

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来源期刊

Genome Biology Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

21.00

自引率

3.30%

发文量

241

审稿时长

2 months

期刊介绍： Genome Biology stands as a premier platform for exceptional research across all domains of biology and biomedicine, explored through a genomic and post-genomic lens. With an impressive impact factor of 12.3 (2022),* the journal secures its position as the 3rd-ranked research journal in the Genetics and Heredity category and the 2nd-ranked research journal in the Biotechnology and Applied Microbiology category by Thomson Reuters. Notably, Genome Biology holds the distinction of being the highest-ranked open-access journal in this category. Our dedicated team of highly trained in-house Editors collaborates closely with our esteemed Editorial Board of international experts, ensuring the journal remains on the forefront of scientific advances and community standards. Regular engagement with researchers at conferences and institute visits underscores our commitment to staying abreast of the latest developments in the field.