Youri Hoogstrate1, René Böttcher2, Saskia Hiltemann1, Peter J van der Spek3, Guido Jenster2, Andrew P Stubbs3. 1. Department of Urology and Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands. 2. Department of Urology and. 3. Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands.
Abstract
UNLABELLED: A new generation of tools that identify fusion genes in RNA-seq data is limited in either sensitivity and or specificity. To allow further downstream analysis and to estimate performance, predicted fusion genes from different tools have to be compared. However, the transcriptomic context complicates genomic location-based matching. FusionMatcher (FuMa) is a program that reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/ErasmusMC-Bioinformatics/fuma and available for Galaxy in the tool sheds and directly accessible at https://bioinf-galaxian.erasmusmc.nl/galaxy/ CONTACT: y.hoogstrate@erasmusmc.nl or a.stubbs@erasmusmc.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: A new generation of tools that identify fusion genes in RNA-seq data is limited in either sensitivity and or specificity. To allow further downstream analysis and to estimate performance, predicted fusion genes from different tools have to be compared. However, the transcriptomic context complicates genomic location-based matching. FusionMatcher (FuMa) is a program that reports identical fusion genes based on gene-name annotations. FuMa automatically compares and summarizes all combinations of two or more datasets in a single run, without additional programming necessary. FuMa uses one gene annotation, avoiding mismatches caused by tool-specific gene annotations. FuMa matches 10% more fusion genes compared with exact gene matching due to overlapping genes and accepts intermediate output files that allow a stepwise analysis of corresponding tools. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/ErasmusMC-Bioinformatics/fuma and available for Galaxy in the tool sheds and directly accessible at https://bioinf-galaxian.erasmusmc.nl/galaxy/ CONTACT: y.hoogstrate@erasmusmc.nl or a.stubbs@erasmusmc.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Björn A Grüning; Jörg Fallmann; Dilmurat Yusuf; Sebastian Will; Anika Erxleben; Florian Eggenhofer; Torsten Houwaart; Bérénice Batut; Pavankumar Videm; Andrea Bagnacani; Markus Wolfien; Steffen C Lott; Youri Hoogstrate; Wolfgang R Hess; Olaf Wolkenhauer; Steve Hoffmann; Altuna Akalin; Uwe Ohler; Peter F Stadler; Rolf Backofen Journal: Nucleic Acids Res Date: 2017-07-03 Impact factor: 16.971