# Computational approaches to delineate non-canonical splicing events

> **NIH NIH R35** · UNIVERSITY OF MINNESOTA · 2021 · $397,500

## Abstract

Abstract:
RNA splicing generates enormous variations at the RNA and protein levels to regulate cell-type-
specific functions as well as being the cause for numerous diseases. Several classes of splicing
patterns have been widely studied, such as exon skipping, intron retention and alternative
splicing sites. Advances in sequencing technologies enable the discovery of previously
unknown non-canonical splicing events. However, due to the lack of dedicated computational
approaches, the prevalence and functional consequences of these non-canonical splicing
events remain unexplored. The goal of our research program in the next five years is to develop
novel and specialized computational algorithms for discovery and characterization of emerging
splicing patterns that are currently understudied. We will focus on exitrons and non-linear-
spliced transcripts in our proposed study, as these two non-canonical splicing models have
been implicated in complex human diseases reported by recent studies. We will develop a
series of algorithms to (1) comprehensively catalog these novel splicing patterns using short
and long read sequencing platforms, (2) dissect the genetic basis of non-canonical splicing
events with integrative analysis of deep transcriptome and whole-genome sequencing data, and
(3) elucidate the functional impacts of novel forms of RNA splicing alternations using a
proteogenomic strategy. Our proposed work is innovative in that we will build unique
computational frameworks to detect and characterize novel non-canonical splicing events by
integrating large multi-omics datasets (e.g. TCGA, GTEx and ENCODE). It is significant
because it can be applied both in basic research to improve transcriptome annotation and
potentially in genomic medicine to guide the development of novel therapeutic strategies for
complex diseases.

## Key facts

- **NIH application ID:** 10270575
- **Project number:** 1R35GM142441-01
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Rendong Yang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $397,500
- **Award type:** 1
- **Project period:** 2021-08-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10270575

## Citation

> US National Institutes of Health, RePORTER application 10270575, Computational approaches to delineate non-canonical splicing events (1R35GM142441-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10270575. Licensed CC0.

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