MicroRNA Modules Prefer to Bind Weak and Unconventional Target Sites

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2014 Dec 21

PMID 25527098

Citations 12

Authors

Jun Ding

Xiaoman Li

Haiyan Hu

Affiliations

Soon will be listed here.

Abstract

Motivation: MicroRNAs (miRNAs) play critical roles in gene regulation. Although it is well known that multiple miRNAs may work as miRNA modules to synergistically regulate common target mRNAs, the understanding of miRNA modules is still in its infancy.

Results: We employed the recently generated high throughput experimental data to study miRNA modules. We predicted 181 miRNA modules and 306 potential miRNA modules. We observed that the target sites of these predicted modules were in general weaker compared with those not bound by miRNA modules. We also discovered that miRNAs in predicted modules preferred to bind unconventional target sites rather than canonical sites. Surprisingly, contrary to a previous study, we found that most adjacent miRNA target sites from the same miRNA modules were not within the range of 10-130 nucleotides. Interestingly, the distance of target sites bound by miRNAs in the same modules was shorter when miRNA modules bound unconventional instead of canonical sites. Our study shed new light on miRNA binding and miRNA target sites, which will likely advance our understanding of miRNA regulation.

Availability And Implementation: The software miRModule can be freely downloaded at http://hulab.ucf.edu/research/projects/miRNA/miRModule.

Supplementary Information: Supplementary data are available at Bioinformatics online.

Contact: haihu@cs.ucf.edu or xiaoman@mail.ucf.edu.

Citing Articles

A computational modeling of pri-miRNA expression.

Zheng H, Wang S, Li X, Hu H PLoS One. 2024; 19(1):e0290768.

PMID: 38165860 PMC: 10760784. DOI: 10.1371/journal.pone.0290768.

MicroRNAs: Small but Key Players in Viral Infections and Immune Responses to Viral Pathogens.

Bauer A, Majumdar N, Williams F, Rajput S, Pokhrel L, Cook P Biology (Basel). 2023; 12(10).

PMID: 37887044 PMC: 10604607. DOI: 10.3390/biology12101334.

A deep learning method for miRNA/isomiR target detection.

Talukder A, Zhang W, Li X, Hu H Sci Rep. 2022; 12(1):10618.

PMID: 35739186 PMC: 9226005. DOI: 10.1038/s41598-022-14890-8.

A two-stream convolutional neural network for microRNA transcription start site feature integration and identification.

Cha M, Zheng H, Talukder A, Barham C, Li X, Hu H Sci Rep. 2021; 11(1):5625.

PMID: 33707582 PMC: 7952457. DOI: 10.1038/s41598-021-85173-x.

Expression of activating transcription factor 5 (ATF5) is mediated by microRNA-520b-3p under diverse cellular stress in cancer cells.

Gaither K, Watson C, Madarampalli B, Lazarus P PLoS One. 2020; 15(6):e0225044.

PMID: 32603335 PMC: 7326155. DOI: 10.1371/journal.pone.0225044.

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