Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs

Fayaz Seifuddin; Mehdi Pirooznia

doi:10.1007/978-1-0716-1158-6_1

Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs

Methods Mol Biol. 2021:2254:1-13. doi: 10.1007/978-1-0716-1158-6_1.

Authors

Fayaz Seifuddin¹, Mehdi Pirooznia²

Affiliations

¹ Bioinformatics and Computational Biology, National Heart, Lung, and Blood Institute National Institutes of Health, Bethesda, MD, USA.
² Bioinformatics and Computational Biology, National Heart, Lung, and Blood Institute National Institutes of Health, Bethesda, MD, USA. mehdi.pirooznia@nih.gov.

PMID: 33326066
DOI: 10.1007/978-1-0716-1158-6_1

Abstract

There is accumulating evidence that long noncoding RNAs (lncRNAs) play crucial roles in biological processes and diseases. In recent years, computational models have been widely used to predict potential lncRNA-disease relations. In this chapter, we systematically describe various computational algorithms and prediction tools that have been developed to elucidate the roles of lncRNAs in diseases, coding potential/functional characterization, or ascertaining their involvement in critical biological processes as well as provide a comprehensive summary of these applications.

Keywords: LncRNA Bioinformatics; LncRNA coding potential; LncRNA functional prediction; LncRNA–disease association; LncRNA–protein correlation.

MeSH terms

Algorithms
Computational Biology / methods*
Disease / genetics
Humans
Proteins / genetics
RNA, Long Noncoding / genetics*
RNA, Long Noncoding / metabolism

Substances

Proteins
RNA, Long Noncoding