Repository: Freie Universit├Ąt Berlin, Math Department

Acfs: accurate circRNA identification and quantification from NGS data

You, Xintian Arthur and Conrad, T. O. F. (2016) Acfs: accurate circRNA identification and quantification from NGS data. Nature Scientific Reports, 6 . ISSN 2045-2322

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Official URL: http://www.nature.com/articles/srep38820

Abstract

Circular RNAs (circRNAs) are a group of single-stranded RNAs in closed circular form. They are splicing-generated, widely expressed in various tissues and have functional implications in development and diseases. To facilitate genome-wide characterization of circRNAs using RNA-Seq data, we present a freely available software package named acfs. Acfs allows de novo, accurate and fast identification and abundance quantification of circRNAs from single- and paired-ended RNA-Seq data. On simulated datasets, acfs achieved the highest F1 accuracy and lowest false discovery rate among current state-of-the-art tools. On real-world datasets, acfs efficiently identified more bona fide circRNAs. Furthermore, we demonstrated the power of circRNA analysis on two leukemia datasets. We identified a set of circRNAs that are differentially expressed between AML and APL samples, which might shed light on the potential molecular classification of complex diseases using circRNA profiles. Moreover, chromosomal translocation, as manifested in numerous diseases, could produce not only fusion transcripts but also fusion circRNAs of clinical relevance. Featured with high accuracy, low FDR and the ability to identify fusion circRNAs, we believe that acfs is well suited for a wide spectrum of applications in characterizing the landscape of circRNAs from non-model organisms to cancer biology.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Biological Sciences > Molecular Biology > Applied Molecular Biology
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics
Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group
Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Proteomics Group
ID Code:1781
Deposited By: Admin Administrator
Deposited On:04 Feb 2016 12:37
Last Modified:07 Mar 2017 12:34

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