# Integrative approach to studying LncRNA functions

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2020 · $302,225

## Abstract

ABSTRACT
 Long non-coding RNAs (lncRNAs) play regulatory roles in biological cell process and disease development.
It has been emerging as a key regulator of diverse cellular processes. Great efforts have been made towards
investigation of lncRNA functions with both experimental determination and theoretical modeling, leading to a
rudimentary understanding of this class of RNAs. However, all of these cannot keep pace with the fast growth
of diverse genetic data and urgent request of individual lncRNA function annotation, which is inhibited by the
tremendous amount of lncRNAs and expensive experimental cost. This propose aim to address this issue by
providing efficient and user-friendly tools for key lncRNA discovery and lncRNA function annotation. To do so,
we will develop a unique bioinformatics and Systems Biology integrated approach, ISSNLncFA system, which
enables the integration of all sorts of omics data and a comprehensive understanding of lncRNA functions.
 We propose three specific aims for the ultimate lncRNA function annotation: (1) To develop a novel Co-
Modules-based LncRNA Function Annotation (CoMoLncFA) model to detect key lncRNAs and to annotate
lncRNA functions at post transcription level as lncRNA-PCG co-modules, lncRNA-pathways association
network and lncRNA’s triplets (lncRNA-miRNA/TF-PCG) by considering the expression profiles of lncRNA,
protein coding genes and miRNAs and transcript factors, and integrating the curated protein-protein
interactions and biological pathways. (2) To develop a novel Structure-based LncRNA-protein Function
Annotation (STRULncFA) model to characterize lncRNAs identified from Aim 1 by using their primary sequences
and secondary structures for detecting lncRNA-protein functional relations; and to further reveal the regulatory
roles and mechanism of these lncRNAs by determining the binding sites in both lncRNA and protein. (3) To
experimentally validate the identified abnormal lncRNAs and their cellular products, to validate the identified
lncRNA-protein interacting pairs and the predicted binding sites, and to develop software tools and an
environment for functional annotation of lncRNAs, use these tools to evaluate the overall proposed approach,
and apply them to identify lncRNA functions that may be involved in cell states, species, diseases and cancers
and build lncRNA function databases.
 We believe that we will build the models, tools and databases, and make them available to the public in a
timely fashion. Our achievements will lead to a complete understanding of lncRNA functions and regulatory
roles in cell and disease states. Moreover, our models and tools will be feasibly transformed to other function
annotation tasks and disease studies with appropriate changes, and thus will move forward the general
function annotation community and disease-related drug or therapy development.

## Key facts

- **NIH application ID:** 9983714
- **Project number:** 5R01GM123037-04
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Xiaobo Zhou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $302,225
- **Award type:** 5
- **Project period:** 2017-09-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9983714

## Citation

> US National Institutes of Health, RePORTER application 9983714, Integrative approach to studying LncRNA functions (5R01GM123037-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9983714. Licensed CC0.

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