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Methods Mol Biol. 2018;1724:9-25. doi: 10.1007/978-1-4939-7562-4_2.

Deep Computational Circular RNA Analytics from RNA-seq Data.

Author information

1
Section of Bioinformatics and Systems Cardiology, Department of Internal Medicine III, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg, Germany. tobias.jakobi@med.uni-heidelberg.de.
2
German Center for Cardiovascular Research (DZHK)-Partner site Heidelberg/Mannheim, Heidelberg, Germany. tobias.jakobi@med.uni-heidelberg.de.
3
Section of Bioinformatics and Systems Cardiology, Department of Internal Medicine III, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg, Germany.
4
German Center for Cardiovascular Research (DZHK)-Partner site Heidelberg/Mannheim, Heidelberg, Germany.

Abstract

Circular RNAs (circRNAs) have been first described as "scrambled exons" in the 1990s. CircRNAs originate from back splicing or exon skipping of linear RNA templates and have continuously gained attention in recent years due to the availability of high-throughput whole-transcriptome sequencing methods. Numerous manuscripts describe thousands of circRNAs throughout uni- and multicellular eukaryote species and demonstrated that they are conserved, stable, and abundant in specific tissues or conditions. This manuscript provides a walk-through of our bioinformatics toolbox, which covers all aspects of in silico circRNA analysis, starting from raw sequencing data and back-splicing junction discovery to circRNA quantitation and reconstruction of internal the circRNA structure.

KEYWORDS:

Bioinformatics; Circular; Circular RNA detection; RNA analysis; Whole-transcriptome sequencing

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