Nature CommunicationsNeurosciencePeer-reviewed journal article
ASCOT identifies key regulators of neuronal subtype-specific splicing
Jonathan P. LingiD, Christopher WilksiD, Rone CharlesiD, Patrick J. LeaveyiD, Devlina GhoshiD, Lizhi Jiang, Clayton P. SantiagoiD, Bo Pang, Anand VenkataramaniD, Brian S. ClarkiD, Abhinav NelloreiD, Ben LangmeadiD, Seth BlackshawiD
Abstract
Public archives of next-generation sequencing data are growing exponentially, but the difficulty of marshaling this data has led to its underutilization by scientists. Here we present ASCOT, a resource that allows researchers to summarize, visualize, and query alternative splicing patterns in public RNA-Seq data. ASCOT enables rapid identification of splice-variants across tens of thousands of bulk and single-cell RNA-Seq datasets in human and mouse. To demonstrate the utility of ASCOT, we first focused on the nervous system and identified many alternative exons used only by a single neuronal subtype. We then leveraged datasets from the ENCODE and GTEx consortiums to study the unique splicing patterns of rod photoreceptors and found that PTBP1 knockdown combined with overexpression of MSI1 and PCBP2 activates rod-specific exons in HepG2 liver cancer cells. Furthermore, we observed that MSI1 targets intronic UAG motifs proximal to the 5 splice site and interacts synergistically with PTBP1 downregulation. Finally, we show that knockdown of MSI1 in the retina abolishes rod-specific splicing. This work demonstrates how large-scale analysis of public RNA-Seq datasets can yield key insights into cell type-specific control of RNA splicing and underscores the importance of considering both annotated and unannotated splicing events. ASCOT splicing and gene expression data tables, software, and interactive browser are available at http://ascot.cs.jhu.edu.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- Citations
- 107
- FWCI
- 3.24
- Field percentile
- top 6%
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