# Robust, high-throughput identification of RNA processing regulators and regulatory networks genome-wide

> **NIH NIH R00** · BAYLOR COLLEGE OF MEDICINE · 2022 · $249,000

## Abstract

PROJECT SUMMARY
 RNA binding proteins (RBPs) bind to non-coding, pre-, and mature RNA within the cell to regulate each
step of RNA processing, including pre-mRNA alternative splicing, RNA stability and localization, and control of
translation. It has become clear that altered RNA processing plays critical roles in nearly every studied
biological system, and recent work has suggest that a substantial fraction of disease-causing genetic mutations
affect RNA processing, including mutations that cause familial Spinal Muscular Atrophy, Amyotrophic Lateral
Sclerosis, and multiple cancer types. Mechanistic understanding of the downstream regulatory network of an
RBP is essential to studying and, ultimately, ameliorating these diseases; however, there remains a need for
robust, unbiased genome-wide methods to characterize RBP targets and regulators. Building upon our recent
development of enhanced crosslinking and immunoprecipitation (eCLIP), I propose to extend this work in three
unique directions that each contribute to our ability to gain global, high-quality views of RNA processing
transcriptome-wide:
 1. Develop low-sample and tag-eCLIP methods for highly parallelizable in vivo profiling of RBPs in low
 input samples, and for RBPs which lack high-quality native antibodies for immunoprecipitation.
 2. Show that transcriptome profiling coupled with RBP target identification can identify critical
 regulators of a biological system, using differentiation of human induced pluripotent stem cells as a
 model system
 3. Develop methods for unbiased identification of upstream functional regulators of non-coding RNAs
 and RNA processing in an RNA-centric manner.
 My extensive expertise in genomics, computational biology, and the study of DNA and RNA binding
proteins makes me an ideal candidate to perform the research proposed above. These three aims take
different approaches that will coalesce in a robust ability to begin either with an RBP of interest and identify its
regulated targets, or begin with an RNA of interest and identify regulator RBPs, which will serve as the basis
for my independent research program as an independent faculty candidate. The Yeo lab at UCSD is an ideal
environment to perform this research and complete my training towards pursuit of an independent academic
faculty position, as it has consistently been a leader in developing both experimental and computational
methods to characterize RBP regulation. Additionally, the location of the Yeo lab proximal to outstanding
researchers at UCSD, the Salk Institute, and other research institutes and biotechnology companies in La Jolla
will provide specific hands-on experimental training in stem cell culture and differentiation, as well as ample
opportunities for mentored training in performing research and developing an independent research program.

## Key facts

- **NIH application ID:** 10364689
- **Project number:** 5R00HG009530-05
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Eric Lyman Van Nostrand
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $249,000
- **Award type:** 5
- **Project period:** 2020-05-06 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10364689

## Citation

> US National Institutes of Health, RePORTER application 10364689, Robust, high-throughput identification of RNA processing regulators and regulatory networks genome-wide (5R00HG009530-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10364689. Licensed CC0.

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