# AI/ML Ready appraoches for integrative RNA processing, splicing and spatial genomics

> **NIH NIH R35** · STANFORD UNIVERSITY · 2021 · $157,450

## Abstract

Project Summary/Abstract From parent grant: Cells and organisms, from simple to complex, carry the same
genetic DNA sequence organized into genes. Multicellular eukaryotes transcribe and process genes into RNA
isoforms through a process called alternative splicing. Alternative splicing is developmentally, and cell-type
specifically regulated. It is foundational to how higher organisms’ genomes are decoded. Yet, critical, and
fundamental questions regarding its regulation and the function of its output remain unanswered. For example,
circRNA being a ubiquitous product of alternative splicing was only discovered in 2012, and its regulation and
function remains enigmatic. circRNAs’ discovery revealed a larger critical knowledge gap in the field for “what,
how and why” genes are alternatively spliced. What RNA splice variants are expressed, how splicing is
regulated, and which spliced RNAs have essential functions? Answering these questions is critical for
predicting which of myriad genetic variants cause disease and why they do so. Answers will also enable a new
generation of digital nucleic acid biomarkers and diagnostics for disease, drug targets for correcting
dysregulated splicing and identification of pathogenic protein- or non-coding products (respectively) as well as
fundamental basic scientific insight into evolution and function of eukaryotic genomes.. Despite the great
promise for discovering how splicing is regulated in massive single cell RNAseq experiments, the field is still
lacking unbiased precise methods to address statistical and computational challenges of splicing analysis in
scRNA-Seq. State-of-the-art, reproducible, statistical algorithms to achieve precise splice variant calls,
detecting how they are regulated in cell types and subcellularly lag far behind the rate at which single cell RNA-
seq (scRNA-seq) data is generated, limiting ML/AI readiness. Here, we will open the possibility of analyzing
novel RNA regulatory biology through ML/AI-ready software and processed data to a huge community of
biomedical researchers enabling new basic and translational discoveries.

## Key facts

- **NIH application ID:** 10407768
- **Project number:** 3R35GM139517-01S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Julia Salzman
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $157,450
- **Award type:** 3
- **Project period:** 2021-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10407768

## Citation

> US National Institutes of Health, RePORTER application 10407768, AI/ML Ready appraoches for integrative RNA processing, splicing and spatial genomics (3R35GM139517-01S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10407768. Licensed CC0.

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