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A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection

Steve Hoffmann123, Christian Otto123, Gero Doose123, Andrea Tanzer4, David Langenberger123, Sabina Christ5, Manfred Kunz6, Lesca M Holdt37, Daniel Teupser37, Jörg Hackermüller258 and Peter F Stadler110112349*

Author Affiliations

1 Junior Research Group Transcriptome Bioinformatics, Leipzig University, Haertelstrasse 16-18, Leipzig, Germany

2 Interdisciplinary Center for Bioinformatics and Bioinformatics Group, University Leipzig, Haertelstrasse 16-18, Leipzig, Germany

3 LIFE Research Center for Civilization Diseases, Leipzig University

4 Department of Theoretical Chemistry, University of Vienna, Währinger Strasse 17, Vienna, Austria

5 RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology – IZI, Perlickstrasse 1, Leipzig, Germany

6 Department of Dermatology, Venerology and Allergology, Leipzig University, Philipp-Rosenthal-Strasse 23, Leipzig, Germany

7 Institute of Laboratory Medicine, Ludwig Maximilian University, Marchioninistrasse 15, Munich, Germany

8 Young Investigators Group Bioinformatics and Transcriptomics, Department of Proteomics, Helmholtz Centre for Environmental Research – UFZ, Permoserstrasse 15, Leipzig, Germany

9 Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, Leipzig, Germany

10 Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg, Denmark

11 Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, USA

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Genome Biology 2014, 15:R34  doi:10.1186/gb-2014-15-2-r34

Published: 10 February 2014


Numerous high-throughput sequencing studies have focused on detecting conventionally spliced mRNAs in RNA-seq data. However, non-standard RNAs arising through gene fusion, circularization or trans-splicing are often neglected. We introduce a novel, unbiased algorithm to detect splice junctions from single-end cDNA sequences. In contrast to other methods, our approach accommodates multi-junction structures. Our method compares favorably with competing tools for conventionally spliced mRNAs and, with a gain of up to 40% of recall, systematically outperforms them on reads with multiple splits, trans-splicing and circular products. The algorithm is integrated into our mapping tool segemehl ( webcite).