Long non-coding RNAs (lncRNAs) have recently acquired a boost of interest for their implication in several biological conditions. However, many of these elements are not yet characterized. LErNet is a method to in silico define and predict the roles of IncRNAs. The core of the approach is a network expansion algorithm which enriches the genomic context of IncRNAs. The context is built by integrating the genes encoding proteins that are found next to the non-coding elements both at genomic and system level. The pipeline is particularly useful in situations where the functions of discovered IncRNAs are not yet known. The results show both the outperformance of LErNet compared to enrichment approaches in literature and its robustness in case of partially missing context information. LErNet is provided as an R package. It is available at https://github.com/InfOmics/LErNet.

LErNet: characterization of lncRNAs via context-aware network expansion and enrichment analysis / Bonnici, Vincenzo; Caligola, Simone; Fiorini, Giulia; Giudice, Luca; Giugno, Rosalba. - ELETTRONICO. - (2019), pp. 1-8. ((Intervento presentato al convegno 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) tenutosi a Siena nel 9-11 luglio 2019 [10.1109/CIBCB.2019.8791487].

LErNet: characterization of lncRNAs via context-aware network expansion and enrichment analysis

Bonnici, Vincenzo;
2019

Abstract

Long non-coding RNAs (lncRNAs) have recently acquired a boost of interest for their implication in several biological conditions. However, many of these elements are not yet characterized. LErNet is a method to in silico define and predict the roles of IncRNAs. The core of the approach is a network expansion algorithm which enriches the genomic context of IncRNAs. The context is built by integrating the genes encoding proteins that are found next to the non-coding elements both at genomic and system level. The pipeline is particularly useful in situations where the functions of discovered IncRNAs are not yet known. The results show both the outperformance of LErNet compared to enrichment approaches in literature and its robustness in case of partially missing context information. LErNet is provided as an R package. It is available at https://github.com/InfOmics/LErNet.
978-1-7281-1462-0
LErNet: characterization of lncRNAs via context-aware network expansion and enrichment analysis / Bonnici, Vincenzo; Caligola, Simone; Fiorini, Giulia; Giudice, Luca; Giugno, Rosalba. - ELETTRONICO. - (2019), pp. 1-8. ((Intervento presentato al convegno 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) tenutosi a Siena nel 9-11 luglio 2019 [10.1109/CIBCB.2019.8791487].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2901542
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact