TY - JOUR
T1 - Towards a comprehensive pipeline to identify and functionally annotate long noncoding RNA (lncRNA)
AU - Ramakrishnaiah, Yashpal
AU - Kuhlmann, Levin
AU - Tyagi, Sonika
PY - 2020/12
Y1 - 2020/12
N2 - Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.
AB - Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.
KW - ANN
KW - Bioinformatics
KW - Epigenomics
KW - Gene regulation
KW - lncRNA
KW - Machine learning
KW - Noncoding RNA
UR - http://www.scopus.com/inward/record.url?scp=85094326471&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2020.104028
DO - 10.1016/j.compbiomed.2020.104028
M3 - Review Article
C2 - 33126123
AN - SCOPUS:85094326471
SN - 0010-4825
VL - 127
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 104028
ER -