biostudies-literatureUnknown7Rudewicz JTargeted 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 Frontiers in genetics214https://www.ebi.ac.uk/biostudies/studies/S-EPMC5143680biostudies-literatureMICADo - Looking for Mutations in Targeted PacBio Cancer Data: An Alignment-Free Method.PMC5143680Bergh JBonnefoi HNikolski MIggo RUricaru RRudewicz JSoueidan HfalseMICADo - Looking for Mutations in Targeted PacBio Cancer Data: An Alignment-Free Method.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 2016-01-01T00:00:00Z20162026-04-08T00:53:00.202Z2019-03-27T02:31:01ZS-EPMC51436802800833610.3389/fgene.2016.00214