Journal
GENOME BIOLOGY
Volume 14, Issue 3, Pages -Publisher
BMC
DOI: 10.1186/gb-2013-14-3-r30
Keywords
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Funding
- Fondation ARC pour la Recherche sur le Cancer [PDF20101202345]
- Ligue Contre le Cancer [JG/VP 8102]
- CNRS INS2I [PEPS BFC: 66293]
- Institute of Computational Biology, Investissement d'Avenir
- Region Languedoc Roussillon
- French MAPPI Project [ANR-2010-COSI-004]
- Ligue regionale contre le cancer
- University of Montpellier 2
- Agence Nationale de la Recherche [Colib'read ANR-12-BS02-008]
- NUMEV Labex
- CNRS Mastodons Program
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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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