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3plex enables deep computational investigation of triplex forming lncRNAs

Authors :
Chiara Cicconetti
Andrea Lauria
Valentina Proserpio
Marco Masera
Annalaura Tamburrini
Mara Maldotti
Salvatore Oliviero
Ivan Molineris
Source :
Computational and Structural Biotechnology Journal, Vol 21, Iss , Pp 3091-3102 (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Long non-coding RNAs (lncRNAs) regulate gene expression through different molecular mechanisms, including DNA binding via the formation of RNA:DNA:DNA triple helices (TPXs). Despite the increasing amount of experimental evidence, TPXs investigation remains challenging. Here we present 3plex, a software able to predict TPX interactions in silico. Given an RNA sequence and a set of DNA sequences, 3plex integrates 1) Hoogsteen pairing rules that describe the biochemical interactions between RNA and DNA nucleotides, 2) RNA secondary structure prediction and 3) determination of the TPX thermal stability derived from a collection of TPX experimental evidences. We systematically collected and uniformly re-analysed published experimental lncRNA binding sites on human and mouse genomes. We used these data to evaluate 3plex performance and showed that its specific features allow a reliable identification of TPX interactions. We compared 3plex with the other available software and obtained comparable or even better accuracy at a fraction of the computation time. Interestingly, by inspecting collected data with 3plex we found that TPXs tend to be shorter and more degenerated than previously expected and that the majority of analysed lncRNAs can directly bind to the genome by TPX formation. Those results suggest that an important fraction of lncRNAs can exert its biological function through this mechanism. The software is available at https://github.com/molinerisLab/3plex.

Details

Language :
English
ISSN :
20010370 and 50845896
Volume :
21
Issue :
3091-3102
Database :
Directory of Open Access Journals
Journal :
Computational and Structural Biotechnology Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.20eb3ec0aafc4ca3a5541f50845896ee
Document Type :
article
Full Text :
https://doi.org/10.1016/j.csbj.2023.05.016