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Introducing TransCistor for Efficient cis-Regulatory lncRNA Identification



Long noncoding RNAs (lncRNAs) have emerged as crucial regulators in the intricate landscape of gene expression. Some lncRNAs exert their influence in a cis-regulatory manner by modulating neighbouring genes on the same chromosome. Understanding the functions of these cis-acting lncRNAs is pivotal for unravelling the complex regulatory networks governing cellular processes. 


Recent research has illuminated numerous cis-acting lncRNAs involved in a myriad of biological processes. For example, XIST, a notable lncRNA, orchestrates X chromosome inactivation by silencing genes on one of the X chromosomes in female cells. Similarly, HOTAIR regulates gene expression associated with chromatin remodelling and has been implicated in cancer progression. These examples underscore the significance of cis-acting lncRNAs in modulating gene expression, emphasizing their relevance across diverse physiological and pathological contexts.


Despite their importance, identifying cis-acting lncRNAs presents challenges, including high false-positive predictions and a lack of standardized criteria for their definition. Traditional methods often yield numerous false-positive predictions, complicating the discrimination between genuine cis-regulatory relationships and random associations. Furthermore, the absence of standardized criteria impedes the comprehensive cataloguing of cis-acting lncRNAs, thus limiting insights into their regulatory roles.


In response to these challenges, the TransCistor framework offers an approach to classify cis-acting lncRNAs while mitigating false discovery rates. Leveraging transcriptomic data from perturbation experiments, TransCistor directly identifies gene target enrichment using two distinct strategies: functional enrichment of nearby targets and consideration of the linear genomic distance relationship of the target genes. As a result, TransCistor provides a quantitative and statistically testable means of discerning authentic cis-regulatory relationships from background noise.


This framework not only addresses existing challenges but also establishes a standardized method within the scientific community, further advancing our capacity to unravel the intricate landscape of regulation and the pivotal role of cis-acting lncRNAs in cellular processes.


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