# Mapping RNA protein interaction networks in the human genome

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2021 · $302,431

## Abstract

Detecting protein-RNA interactions is challenging–both experimentally and computationally–
because RNA transcripts are large in number, diverse in cellular location and function. As a
result, many RNA-binding proteins (RBPs) and their cognate motifs are likely unknown or
uncharacterized in humans as well as other model organisms. With increasing number of RBPs
implicated in human diseases, there is an urgent need for identifying and mapping functional
and phenotypic information for RBPs as well as to complete a map of the protein-RNA
interaction network. The objective here is to establish a robust computational technique that
integrates expression associations with sequence as well as several RBP centric features for
genome-scale prediction of binding motifs for hundreds of human RBPs to facilitate the
elucidation of their tissue-specific post-transcriptional networks. At the completion of this project,
we expect to have developed the most advanced tool for predicting human RBP motifs and
methods as well as resources which can facilitate the construction of tissue-specific RBP-RNA
networks. Our central hypothesis, supported by our initial genome-scale computational study
and assessment by comparative analysis of known RBP binding motifs is that, since many
RBPs are involved in different stages of RNA metabolism, exon expression level associations
with an RBP and other exon related features can be very powerful in identifying the binding
motifs of an RBP in a tissue-specific manner. The proposed integrated approach to
experimentally validate several binding motifs using CLIP-seq and to deconvolute global
posttranscriptional networks in specific cell/tissue types, using genome-wide data from protein
protection assays (POP-seq) will significantly enhance our capability of uncovering network
dynamics of RBPs in cell types and tissues. Such high-quality predictions based on
experimental validations, resulting from all the Aims which will be made public, can become a
venue for future experimental follow up to dissect the role of these important regulatory
molecules in different tissues and disease states. The proposed studies will make an impact in
the field as the first large-scale computational mapping of protein-RNA interaction networks in
the human tissues by taking our ability to predict RBP targets to the next level. The
complementary experience and expertise of investigators will make this project successful.

## Key facts

- **NIH application ID:** 10249195
- **Project number:** 5R01GM123314-05
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Sarath Chandra Janga
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $302,431
- **Award type:** 5
- **Project period:** 2017-09-11 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10249195

## Citation

> US National Institutes of Health, RePORTER application 10249195, Mapping RNA protein interaction networks in the human genome (5R01GM123314-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10249195. Licensed CC0.

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