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CRAC: an integrated approach to the analysis of RNA-seq reads

Nicolas Philippe125, Mikaël Salson34, Thérèse Commes25 and Eric Rivals15*

  • * Corresponding author: Eric Rivals

  • † Equal contributors

Author affiliations

1 Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), UMR 5506, CNRS and Université de Montpellier 2, 161 rue Ada, 34095 Montpellier Cedex 5, France

2 Institut de Recherche en Biothérapie (IRB), U1040 INSERM, CHRU Montpellier Hôpital Saint-Eloi 80, av. Augustin Fliche, 34295 Montpellier Cedex 5, France

3 Laboratoire d'Informatique Fondamentale de Lille (LIFL), (UMR CNRS 8022, Université Lille 1) and Inria Lille-Nord Europe, Cité scientifique-Bâtiment M3, 59655 Villeneuve d'Ascq Cedex, France

4 LITIS EA 4108, Université de Rouen, 1 rue Thomas Becket, 76821 Mont-Saint-Aignan Cedex, France

5 Institut de Biologie Computationnelle, 95 Rue de la Galéra, 34095 Montpellier Cedex 5, France

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Citation and License

Genome Biology 2013, 14:R30  doi:10.1186/gb-2013-14-3-r30

Published: 28 March 2013


A large number of RNA-sequencing studies set out to predict mutations, splice junctions or fusion RNAs. We propose a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-seq read analysis. A k-mer profiling approach detects candidate mutations, indels and splice or chimeric junctions in each single read. CRAC increases precision compared with existing tools, reaching 99:5% for splice junctions, without losing sensitivity. Importantly, CRAC predictions improve with read length. In cancer libraries, CRAC recovered 74% of validated fusion RNAs and predicted novel recurrent chimeric junctions. CRAC is available at webcite.