Common Features in lncRNA Annotation and Classification: A Survey.
classification problems
coding sequence
feature extraction
lncRNA
machine learning
Journal
Non-coding RNA
ISSN: 2311-553X
Titre abrégé: Noncoding RNA
Pays: Switzerland
ID NLM: 101652294
Informations de publication
Date de publication:
13 Dec 2021
13 Dec 2021
Historique:
received:
12
11
2021
revised:
03
12
2021
accepted:
06
12
2021
entrez:
23
12
2021
pubmed:
24
12
2021
medline:
24
12
2021
Statut:
epublish
Résumé
Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene expression. Their molecular functions range from miRNA sponging to chromatin-associated mechanisms, leading to effects in disease progression and establishing them as diagnostic and therapeutic targets. Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts. In this review we survey common in silico approaches for lncRNA annotation. We focus on the well-established sets of features used for classification and discuss their specific advantages and weaknesses. While the available tools perform very well for the task of distinguishing coding sequence from other RNAs, we find that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences. We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.
Identifiants
pubmed: 34940758
pii: ncrna7040077
doi: 10.3390/ncrna7040077
pmc: PMC8708962
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : Federal Ministry of Education and Research
ID : BMBF 031A538B
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