The PRC2-binding long non-coding RNAs in human and mouse genomes are associated with predictive sequence features

Shiqi Tu,Guo-Cheng Yuan,Zhen Shao

doi:10.1038/srep41669

Abstract

Recently, long non-coding RNAs (lncRNAs) have emerged as an important class of molecules involved in many cellular processes. One of their primary functions is to shape epigenetic landscape through interactions with chromatin modifying proteins. However, mechanisms contributing to the specificity of such interactions remain poorly understood. Here we took the human and mouse lncRNAs that were experimentally determined to have physical interactions with Polycomb repressive complex 2 (PRC2), and systematically investigated the sequence features of these lncRNAs by developing a new computational pipeline for sequences composition analysis, in which each sequence is considered as a series of transitions between adjacent nucleotides. Through that, PRC2-binding lncRNAs were found to be associated with a set of distinctive and evolutionarily conserved sequence features, which can be utilized to distinguish them from the others with considerable accuracy. We further identified fragments of PRC2-binding lncRNAs that are enriched with these sequence features, and found they show strong PRC2-binding signals and are more highly conserved across species than the other parts, implying their functional importance.

Highlights

IntroductionWe have developed a new computational pipeline for analyzing the composition of long DNA and RNA sequences of variable length using a Markov-chain based approach[18]
In order to address these important questions, we carry out a systematic analysis of the DNA sequence patterns associated with PRC2-binding lncRNAs in both human and mouse genomes
A classification model is constructed by applying Bayesian additive regression trees (BART)[21] analysis to test whether these sequence features can be used to predict the group label of each sequence

Summary

Introduction

We have developed a new computational pipeline for analyzing the composition of long DNA and RNA sequences of variable length using a Markov-chain based approach[18]. It considers each sequence as a series of transitions between adjacent nucleotides and uses the frequency of observing each possible transition to characterize the composition of this sequence. Through application of this pipeline to the PRC2-binding and non-binding lncRNAs identified from publicly available RIP data in human and mouse, we discovered a number of transitions that are differentially favored by these two classes of lncRNAs as the sequence features associated with PRC2-lncRNA interactions. The fragments of PRC2-binding lncRNAs that are highly enriched with these sequence features show significant conservation across species, indicating the importance of these fragments

Methods

Results

Conclusion