title: MICADo - Looking for Mutations in Targeted PacBio Cancer Data: An Alignment-Free Method.
abstract: Targeted sequencing is commonly used in clinical application of NGS technology since it enables generation of sufficient sequencing depth in the targeted genes of interest and thus ensures the best possible downstream analysis. This notwithstanding, the accurate discovery and annotation of disease causing mutations remains a challenging problem even in such favorable context. The difficulty is particularly salient in the case of third generation sequencing technology, such as PacBio. We present MICADo, a de Bruijn graph based method, implemented in python, that makes possible to distinguish between patient specific mutations and other alterations for targeted sequencing of a cohort of patients. MICADo analyses NGS reads for each sample within the context of the data of the whole cohort in order to capture the differences between specificities of the sample with respect to the cohort. MICADo is particularly suitable for sequencing data from highly heterogeneous samples, especially when it involves high rates of non-uniform sequencing errors. It was validated on PacBio sequencing datasets from several cohorts of patients. The comparison with two widely used available tools, namely VarScan and GATK, shows that MICADo is more accurate, especially when true mutations have frequencies close to backgound noise. The source code is available at http://github.com/cbib/MICADo.
authors.affiliation: Centre de BioInformatique de Bordeaux, University of BordeauxBordeaux, France; Laboratoire Bordelais de Recherche en Informatique, Centre National de la Recherche Scientifique, University of BordeauxBordeaux, France; Bergonié Cancer Institute, Institut National de la Santé et de la Recherche Médicale U1218, University of BordeauxBordeaux, France.
authors.forename: Justine
authors.initials: J
authors.lastname: Rudewicz
authors.affiliation: Centre de BioInformatique de Bordeaux, University of BordeauxBordeaux, France; Laboratoire Bordelais de Recherche en Informatique, Centre National de la Recherche Scientifique, University of BordeauxBordeaux, France.
authors.forename: Hayssam
authors.initials: H
authors.lastname: Soueidan
authors.affiliation: Centre de BioInformatique de Bordeaux, University of BordeauxBordeaux, France; Laboratoire Bordelais de Recherche en Informatique, Centre National de la Recherche Scientifique, University of BordeauxBordeaux, France.
authors.forename: Raluca
authors.initials: R
authors.lastname: Uricaru
authors.affiliation: Bergonié Cancer Institute, Institut National de la Santé et de la Recherche Médicale U1218, University of Bordeaux Bordeaux, France.
authors.forename: Hervé
authors.initials: H
authors.lastname: Bonnefoi
authors.affiliation: Bergonié Cancer Institute, Institut National de la Santé et de la Recherche Médicale U1218, University of Bordeaux Bordeaux, France.
authors.forename: Richard
authors.initials: R
authors.lastname: Iggo
authors.affiliation: Karolinska Institute and University Hospital Stockholm, Sweden.
authors.forename: Jonas
authors.initials: J
authors.lastname: Bergh
authors.affiliation: Centre de BioInformatique de Bordeaux, University of BordeauxBordeaux, France; Laboratoire Bordelais de Recherche en Informatique, Centre National de la Recherche Scientifique, University of BordeauxBordeaux, France.
authors.forename: Macha
authors.initials: M
authors.lastname: Nikolski
keywords: cancer; code:python; de Bruijn graphs; patients' cohort; targeted sequencing; third generation sequencing