MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate messenger RNAs (mRNAs) post-transcriptionally. miRNAs induce target messenger RNA degradation or translational inhibition, predominantly through cognitive binding sites in the 3 untranslated regions (3UTRs). Accurate identification of miRNA targets is essential for the understanding of their functions. Target identification has been a major challenge for understanding this recently recognized exciting dimension of gene regulation. To this end, computational target prediction methods have proven to be valuable. However, current prediction methods suffer from a number of limitations that hinder progress. Recently, biochemical purification and high throughput deep-sequencing have become feasible. Furthermore, we have developed a high-throughput methodology to experimentally test thousands of miRNA-3UTR interactions, and have acquired mRNA/miRNA expression data on hundreds of cancer cell lines. These experimental advances have presented us the opportunity to develop state-of-the-art miRNA target prediction algorithms. In this application, we propose to adapt an interdisciplinary approach to develop novel miRNA-target prediction algorithms to overcome the limitations of current prediction algorithms. Our complementary expertise in RNA Bioinformatics and Statistics and large scale experimental testing have placed us in a unique position to pursue the following specific aims: 1) Develop a target prediction algorithm based on deep sequencing data from biochemically purified miRNA-target complexes;2) Perform large-scale miRNA-3UTR reporter assays to derive functional miRNA-target interactions;3) Develop algorithms and perform analyses and validation for more informative predictions on the level of miRNA-mediated regulation and miRNA-regulated pathways and networks;4) Develop user-friendly software tools and database for distribution to the scientific community. This project will advance our understanding of miRNA-mediated gene regulation in molecular biology and systems biology, facilitate development of miRNA-based therapeutics, and in turn benefit human health.

Public Health Relevance

Novel Approaches to Mammalian MicroRNA Target Prediction Project Relevance Accurate identification of microRNA targets is essential for understanding the diverse functions of microRNAs in gene regulation. The computational tools developed from this project will be broadly applicable to mammalian species beyond human and mouse, as well as stem cells and viruses. Thus, this project will contribute to the advancement of microRNA biology, the understanding of microRNA functions in cancer and other human diseases, and the development of microRNA-based therapeutics.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Wadsworth Center
United States
Zip Code
Cheng, Jijun; Pan, Wen; Lu, Jun (2017) Dense sgRNA Library Construction Using a Molecular Chipper Approach. Bio Protoc 7:
Roden, Christine; Gaillard, Jonathan; Kanoria, Shaveta et al. (2017) Novel determinants of mammalian primary microRNA processing revealed by systematic evaluation of hairpin-containing transcripts and human genetic variation. Genome Res 27:374-384
Liu, Jun; Guo, Bo; Chen, Zhuo et al. (2017) miR-125b promotes MLL-AF9-driven murine acute myeloid leukemia involving a VEGFA-mediated non-cell-intrinsic mechanism. Blood 129:1491-1502
Roden, Christine; Lu, Jun (2016) MicroRNAs in Control of Stem Cells in Normal and Malignant Hematopoiesis. Curr Stem Cell Rep 2:183-196
Cheng, Jijun; Roden, Christine A; Pan, Wen et al. (2016) A Molecular Chipper technology for CRISPR sgRNA library generation and functional mapping of noncoding regions. Nat Commun 7:11178
Kanoria, Shaveta; Rennie, William; Liu, Chaochun et al. (2016) STarMir Tools for Prediction of microRNA Binding Sites. Methods Mol Biol 1490:73-82
Rennie, William; Kanoria, Shaveta; Liu, Chaochun et al. (2016) STarMirDB: A database of microRNA binding sites. RNA Biol 13:554-60
Guo, Yanwen; Liu, Jun; Elfenbein, Sarah J et al. (2015) Characterization of the mammalian miRNA turnover landscape. Nucleic Acids Res 43:2326-41
Wang, Jing; Rennie, William; Liu, Chaochun et al. (2015) Identification of bacterial sRNA regulatory targets using ribosome profiling. Nucleic Acids Res 43:10308-20
Guo, Yanwen; Mastriano, Stephen; Lu, Jun (2014) A high-throughput microRNA expression profiling system. Methods Mol Biol 1176:33-44

Showing the most recent 10 out of 17 publications