miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs
Abstract 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-s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03549-y |
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| author | Andrea M. Baran Arun H. Patil Ernesto Aparicio-Puerta Seong-Hwan Jun Marc K. Halushka Matthew N. McCall |
| author_facet | Andrea M. Baran Arun H. Patil Ernesto Aparicio-Puerta Seong-Hwan Jun Marc K. Halushka Matthew N. McCall |
| author_sort | Andrea M. Baran |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-b1b8edea71cc472cb9e22dd3306298a5 |
| institution | OA Journals |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-b1b8edea71cc472cb9e22dd3306298a52025-08-20T02:19:58ZengBMCGenome Biology1474-760X2025-04-0126112110.1186/s13059-025-03549-ymiRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRsAndrea M. Baran0Arun H. Patil1Ernesto Aparicio-Puerta2Seong-Hwan Jun3Marc K. Halushka4Matthew N. McCall5Department of Biostatistics and Computational Biology, University of Rochester Medical CenterLieber Institute for Brain Development, Johns Hopkins UniversityDepartment of Biostatistics and Computational Biology, University of Rochester Medical CenterDepartment of Biostatistics and Computational Biology, University of Rochester Medical CenterInstitute of Pathology and Laboratory Medicine, Cleveland ClinicDepartment of Biostatistics and Computational Biology, University of Rochester Medical CenterAbstract 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.https://doi.org/10.1186/s13059-025-03549-ymicroRNAisomiRDifferential expressionMixed modelAggregationmiRNA-seq |
| spellingShingle | Andrea M. Baran Arun H. Patil Ernesto Aparicio-Puerta Seong-Hwan Jun Marc K. Halushka Matthew N. McCall miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs Genome Biology microRNA isomiR Differential expression Mixed model Aggregation miRNA-seq |
| title | miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs |
| title_full | miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs |
| title_fullStr | miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs |
| title_full_unstemmed | miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs |
| title_short | miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs |
| title_sort | mirglmm a generalized linear mixed model of isomir level counts improves estimation of mirna level differential expression and uncovers variable differential expression between isomirs |
| topic | microRNA isomiR Differential expression Mixed model Aggregation miRNA-seq |
| url | https://doi.org/10.1186/s13059-025-03549-y |
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