4.5 Article

MicroExonator enables systematic discovery and quantification of microexons across mouse embryonic development

期刊

GENOME BIOLOGY
卷 22, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s13059-020-02246-2

关键词

Microexons; Splicing; Alternative splicing; Neuronal development; Single-cell; Reproducible software

资金

  1. Wellcome Trust
  2. Cancer Research UK [C13474/A18583, C6946/A14492]
  3. Wellcome [104640/Z/14/Z, 092096/Z/10/Z]
  4. MRC [UKDRI-2005] Funding Source: UKRI
  5. Wellcome Trust [092096/Z/10/Z, 104640/Z/14/Z] Funding Source: Wellcome Trust

向作者/读者索取更多资源

MicroExonator is introduced as a new pipeline for the discovery and quantification of microexons in mouse transcriptomes. It identified a large number of microexons, some of which undergo differential splicing during mouse embryonic brain development, and suggests potential cell type-specific roles for certain microexons. This tool enables a comprehensive investigation of microexons in large-scale transcriptome studies.
Background Microexons, exons that are <= 30 nucleotides, are a highly conserved and dynamically regulated set of cassette exons. They have key roles in nervous system development and function, as evidenced by recent results demonstrating the impact of microexons on behaviour and cognition. However, microexons are often overlooked due to the difficulty of detecting them using standard RNA-seq aligners. Results Here, we present MicroExonator, a novel pipeline for reproducible de novo discovery and quantification of microexons. We process 289 RNA-seq datasets from eighteen mouse tissues corresponding to nine embryonic and postnatal stages, providing the most comprehensive survey of microexons available for mice. We detect 2984 microexons, 332 of which are differentially spliced throughout mouse embryonic brain development, including 29 that are not present in mouse transcript annotation databases. Unsupervised clustering of microexons based on their inclusion patterns segregates brain tissues by developmental time, and further analysis suggests a key function for microexons in axon growth and synapse formation. Finally, we analyse single-cell RNA-seq data from the mouse visual cortex, and for the first time, we report differential inclusion between neuronal subpopulations, suggesting that some microexons could be cell type-specific. Conclusions MicroExonator facilitates the investigation of microexons in transcriptome studies, particularly when analysing large volumes of data. As a proof of principle, we use MicroExonator to analyse a large collection of both mouse bulk and single-cell RNA-seq datasets. The analyses enabled the discovery of previously uncharacterized microexons, and our study provides a comprehensive microexon inclusion catalogue during mouse development.

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