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FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data

Andrea Sboner12, Lukas Habegger1, Dorothee Pflueger3, Stephane Terry3, David Z Chen1, Joel S Rozowsky2, Ashutosh K Tewari4, Naoki Kitabayashi3, Benjamin J Moss3, Mark S Chee5, Francesca Demichelis36, Mark A Rubin3* and Mark B Gerstein127*

Author Affiliations

1 Program in Computational Biology and Bioinformatics, Yale University, 300 George Street, New Haven, CT 06511, USA

2 Molecular Biophysics and Biochemistry Department, Yale University, 260 Whitney Avenue, New Haven, CT 06520, USA

3 Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065, USA

4 Department of Urology, Weill Cornell Medical College, 525 East 68th Street, New York, NY 10065, USA

5 Prognosys Biosciences, Inc., 505 Coast Blvd South, La Jolla, CA 92037, USA

6 Institute for Computational Biomedicine, Weill Cornell Medical College, 1305 York Avenue, New York, NY 10065, USA

7 Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA

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Genome Biology 2010, 11:R104  doi:10.1186/gb-2010-11-10-r104

Published: 21 October 2010

Additional files

Additional file 1:

Supplementary material, tables and figures. The results of different mapping tools and approaches, the description of additional filters that are annotation specific, more details about data formats, and the visualization tools.

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Open Data