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Improved circRNA Identification by Combining Prediction Algorithms

Non-coding RNA is an interesting class of gene regulators with diverse functionalities. One large subgroup of non-coding RNAs is the recently discovered class of circular RNAs (circRNAs). CircRNAs are conserved and expressed in a tissue and developmental specific manner, although for the vast majori...

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Autor principal: Hansen, Thomas B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5844931/
https://www.ncbi.nlm.nih.gov/pubmed/29556495
http://dx.doi.org/10.3389/fcell.2018.00020
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author Hansen, Thomas B.
author_facet Hansen, Thomas B.
author_sort Hansen, Thomas B.
collection PubMed
description Non-coding RNA is an interesting class of gene regulators with diverse functionalities. One large subgroup of non-coding RNAs is the recently discovered class of circular RNAs (circRNAs). CircRNAs are conserved and expressed in a tissue and developmental specific manner, although for the vast majority, the functional relevance remains unclear. To identify and quantify circRNAs expression, several bioinformatic pipelines have been developed to assess the catalog of circRNAs in any given total RNA sequencing dataset. We recently compared five different algorithms for circRNA detection, but here this analysis is extended to 11 algorithms. By comparing the number of circRNAs discovered and their respective sensitivity to RNaseR digestion, the sensitivity and specificity of each algorithm are evaluated. Moreover, the ability to predict de novo circRNA, i.e., circRNAs not derived from annotated splice sites, is also determined as well as the effect of eliminating low quality and adaptor-containing reads prior to circRNA prediction. Finally, and most importantly, all possible pair-wise combinations of algorithms are tested and guidelines for algorithm complementarity are provided. Conclusively, the algorithms mostly agree on highly expressed circRNAs, however, in many cases, algorithm-specific false positives with high read counts are predicted, which is resolved by using the shared output from two (or more) algorithms.
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spelling pubmed-58449312018-03-19 Improved circRNA Identification by Combining Prediction Algorithms Hansen, Thomas B. Front Cell Dev Biol Cell and Developmental Biology Non-coding RNA is an interesting class of gene regulators with diverse functionalities. One large subgroup of non-coding RNAs is the recently discovered class of circular RNAs (circRNAs). CircRNAs are conserved and expressed in a tissue and developmental specific manner, although for the vast majority, the functional relevance remains unclear. To identify and quantify circRNAs expression, several bioinformatic pipelines have been developed to assess the catalog of circRNAs in any given total RNA sequencing dataset. We recently compared five different algorithms for circRNA detection, but here this analysis is extended to 11 algorithms. By comparing the number of circRNAs discovered and their respective sensitivity to RNaseR digestion, the sensitivity and specificity of each algorithm are evaluated. Moreover, the ability to predict de novo circRNA, i.e., circRNAs not derived from annotated splice sites, is also determined as well as the effect of eliminating low quality and adaptor-containing reads prior to circRNA prediction. Finally, and most importantly, all possible pair-wise combinations of algorithms are tested and guidelines for algorithm complementarity are provided. Conclusively, the algorithms mostly agree on highly expressed circRNAs, however, in many cases, algorithm-specific false positives with high read counts are predicted, which is resolved by using the shared output from two (or more) algorithms. Frontiers Media S.A. 2018-03-05 /pmc/articles/PMC5844931/ /pubmed/29556495 http://dx.doi.org/10.3389/fcell.2018.00020 Text en Copyright © 2018 Hansen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Hansen, Thomas B.
Improved circRNA Identification by Combining Prediction Algorithms
title Improved circRNA Identification by Combining Prediction Algorithms
title_full Improved circRNA Identification by Combining Prediction Algorithms
title_fullStr Improved circRNA Identification by Combining Prediction Algorithms
title_full_unstemmed Improved circRNA Identification by Combining Prediction Algorithms
title_short Improved circRNA Identification by Combining Prediction Algorithms
title_sort improved circrna identification by combining prediction algorithms
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5844931/
https://www.ncbi.nlm.nih.gov/pubmed/29556495
http://dx.doi.org/10.3389/fcell.2018.00020
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