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

Abstract

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.

LAR Receptor Tyrosine Phosphatase Family in Healthy and Diseased Brain

“…In the case of LAR-RPTPs, alternative splicing regulates the inclusion of four mini-exons (short peptide sequences of up to 16 amino acids; meA-D, Figure 1 ). LAR-RPTPs mini-exon peptide sequences are encoded by micro-exons (nucleotide sequences of shorter than 30 nucleotides), which are part of a highly conserved and dynamic network where micro-exons are critical for neuronal alternative splicing events ( Irimia et al, 2014 ; Li et al, 2015 ; Parada et al, 2021 ).…”

Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

“…Every splicing node is associated with different types of AS, alternative transcriptional start sites or alternative polyadenylation events and their inclusion rates are calculated as percent-spliced-in (ψ) values, which is quantified by taking the ratio of reads that support the inclusion of a given splicing node (Figure 6A). To quantify AS patterns, we used Whippet, a computationally light-weight and accurate quantification method, previously integrated in computational workflows dedicated to process scRNA-seq data (Parada et al, 2021;Sterne-Weiler et al, 2018). Since splicing node coverage is limited at the single-cell level (Figure S4A), we implemented a pseudo-bulk pooling approach, developed as part of MicroExonator (Parada et al, 2021), where reads from the same cell type are pooled in silico before splicing node quantification.…”

“…To quantify AS patterns, we used Whippet, a computationally light-weight and accurate quantification method, previously integrated in computational workflows dedicated to process scRNA-seq data (Parada et al, 2021;Sterne-Weiler et al, 2018). Since splicing node coverage is limited at the single-cell level (Figure S4A), we implemented a pseudo-bulk pooling approach, developed as part of MicroExonator (Parada et al, 2021), where reads from the same cell type are pooled in silico before splicing node quantification.…”

“…This filter is particularly important for transcripts assembled from reads that are mapped to repetitive sequences or exons that are ≤30 nt, which can arise from HISAT2 misalignments. To annotate potentially novel microexons, we used MicroExonator, a specialized computational workflow for discovering and quantifying microexons (Parada et al, 2021). After running the MicroExonator's discovery module we obtained a transcriptome annotation, which was later processed with custom scripts to limit the number of alternative transcription start and end sites.…”

Prediction of functional microexons by transfer learning

“…With the development of RNA sequencing and corresponding computational tools, a specific kind of exon called microexon (3–30 nucleotides (nt) in length) was found, which has been attracting increasing interests [ 1 – 3 ]. In 2014, Irimia et al designed VAST-TOOLS to analyze vertebrate alternative splicing (AS) and identified 696 AS microexons (3–27 nt) in 603 genes [ 4 ].…”