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

Nicolas Philippe 1, 2, 3 Mikaël Salson 4, 5 Thérèse Commes 6, 1 Eric Rivals 1, 2, * 
* Corresponding author
2 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 BONSAI - Bioinformatics and Sequence Analysis
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : 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
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Submitted on : Saturday, August 10, 2013 - 7:40:28 AM
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Nicolas Philippe, Mikaël Salson, Thérèse Commes, Eric Rivals. CRAC: an integrated approach to the analysis of RNA-seq reads. Genome Biology, BioMed Central, 2013, 14 (3), pp.R30. ⟨10.1186/gb-2013-14-3-r30⟩. ⟨inserm-00850972⟩



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