ASCOT identifies key regulators of neuronal subtype-specific splicing

Jonathan P. Ling, Christopher Wilks, Rone Charles, Patrick J. Leavey, Devlina Ghosh, Lizhi Jiang, Clayton P. Santiago, Bo Pang, Anand Venkataraman, Brian S. Clark, Abhinav Nellore, Ben Langmead () and Seth Blackshaw ()
Additional contact information
Jonathan P. Ling: Johns Hopkins University
Christopher Wilks: Johns Hopkins University
Rone Charles: Johns Hopkins University
Patrick J. Leavey: Johns Hopkins University
Devlina Ghosh: Johns Hopkins University
Lizhi Jiang: Johns Hopkins University
Clayton P. Santiago: Johns Hopkins University
Bo Pang: Johns Hopkins University
Anand Venkataraman: Johns Hopkins University
Brian S. Clark: Washington University
Abhinav Nellore: Oregon Health and Science University
Ben Langmead: Johns Hopkins University
Seth Blackshaw: Johns Hopkins University

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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 uses annotation-free methods to rapidly analyze and visualize splice variants across tens of thousands of bulk and single-cell data sets in the public archive. To demonstrate the utility of ASCOT, we identify novel cell type-specific alternative exons across the nervous system and leverage ENCODE and GTEx data sets to study the unique splicing of photoreceptors. We find that PTBP1 knockdown and MSI1 and PCBP2 overexpression are sufficient to activate many photoreceptor-specific exons in HepG2 liver cancer cells. This work demonstrates how large-scale analysis of public RNA-Seq data sets can yield key insights into cell type-specific control of RNA splicing and underscores the importance of considering both annotated and unannotated splicing events.

Date: 2020
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DOI: 10.1038/s41467-019-14020-5

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