MICADo - Looking for mutations in targeted PacBio cancer data: an alignment-free method

Frontiers in Genetics

Richard Iggo¹  Hervé Bonnefoi¹  Raluca Uricaru²  Justine Rudewicz²  Hayssam Soueidan²  Macha Nikolski²  Jonas Bergh³ 

[1] Bergonié Cancer Institute;CBiB/LaBRI;Karolinska Institute and University Hospital;

关键词: Cancer;    targeted sequencing;    third generation sequencing;    De Bruijn graphs;    Code:Python;    patients’ cohort;   

DOI  :  10.3389/fgene.2016.00214

来源: DOAJ

【 摘 要 】

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 VarScanand GATK, shows that MICADo is more accurate, especially when true mutations have frequencies close to backgound noise.The source code is available at url http://github.com/cbib/MICADo .

【 授权许可】

Unknown