Methods for Annotation and Validation of Circular RNAs from RNAseq Data.


Journal

Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969

Informations de publication

Date de publication:
2019
Historique:
entrez: 13 1 2019
pubmed: 13 1 2019
medline: 14 6 2019
Statut: ppublish

Résumé

Circular RNAs are an emerging class of transcript isoforms created by unique back splicing of exons to form a closed covalent circular structure. While initially considered as product of aberrant splicing, recent evidence suggests unique functions and conservation across evolution. While circular RNAs could be largely attributed to have little or no potential to encode for proteins, recent evidence points to at least a small subset of circular RNAs which encode for peptides. Circular RNAs are also increasingly shown to be biomarkers for a number of diseases including neurological disorders and cancer. The advent of deep sequencing has enabled large-scale identification of circular RNAs in human and other genomes. A number of computational approaches have come up in recent years to query circular RNAs on a genome-wide scale from RNA-seq data. In this chapter, we describe the application and methodology of identifying circular RNAs using three popular computational tools: FindCirc, Segemehl, and CIRI along with approaches for experimental validation of the unique splice junctions.

Identifiants

pubmed: 30635890
doi: 10.1007/978-1-4939-8982-9_3
doi:

Substances chimiques

Biomarkers 0
RNA, Circular 0
RNA 63231-63-0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

55-76

Auteurs

Disha Sharma (D)

G.N. Ramachandran Knowledge Center for Bioinformatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
Academy of Scientific and Innovative Research (AcSIR), CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.

Paras Sehgal (P)

Academy of Scientific and Innovative Research (AcSIR), CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.

Judith Hariprakash (J)

G.N. Ramachandran Knowledge Center for Bioinformatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.

Sridhar Sivasubbu (S)

Academy of Scientific and Innovative Research (AcSIR), CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.
Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.

Vinod Scaria (V)

G.N. Ramachandran Knowledge Center for Bioinformatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India. vinods@igib.res.in.
Academy of Scientific and Innovative Research (AcSIR), CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India. vinods@igib.res.in.

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Classifications MeSH