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, *
* Auteur correspondant
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 http://crac.gforge.inria.fr.
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Soumis le : samedi 10 août 2013 - 07:40:28
Dernière modification le : jeudi 24 mai 2018 - 15:59:22

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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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