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Biology subjects

Vodak, D.

Publications and source records attributed to Vodak, D..

3 recordsLinked to original sources

CANCAN: high-resolution copy number and mutation heterogeneity analysis of DNA sequence data for clinical applications

High-throughput DNA sequencing is central to precision oncology, yet robust and interpretable methods for integrated analysis of copy number alterations and somatic variants across sequencing platforms remain limited. We present CANCAN (Copy number integrative ANalysis in CANcer), a platform-agnostic computational framework for high-resolution analysis of allele-specific copy number and variant data. CANCAN integrates novel normalization and segmentation strategies and enables inference of tumor purity, ploidy, subclonality and mutation multiplicity, while providing statistical confidence estimates and transparent evaluation of alternative solutions. Benchmarking across whole-genome, whole-exome and targeted sequencing datasets from TCGA and the IMPRESS-Norway study demonstrates high concordance with established methods, with particularly strong performance on targeted sequencing data. CANCAN accurately estimates global genomic features, including purity and ploidy, even at reduced sequencing coverage, and shows comparable or improved agreement relative to existing tools. In addition, it provides detailed visualization of the genomic context of clinically relevant biomarkers, supporting diagnostic interpretation. CANCAN constitutes a reproducible and interpretable approach for integrated genomic analysis, addressing key methodological and practical challenges in clinical cancer genomics.

bioinformatics↗

Sample-Index Misassignment Impacts Tumor Exome Sequencing

Sample pooling enabled by dedicated indexes is a common and cost-effective strategy used in high-throughput DNA sequencing. Index misassignment leading to cross-sample contamination has however been described as a general problem of sequencing instruments which utilize exclusion amplification. Using real-life data from multiple tumor sequencing projects, we demonstrate that co-multiplexed samples can induce artifactual calls closely resembling high-quality somatic variant calls, and argue that dual indexing is the most reliable countermeasure.

genomics↗

Personal Cancer Genome Reporter: Variant Interpretation Report For Precision Oncology

SummaryIndividual tumor genomes pose a major challenge for clinical interpretation due to their unique sets of acquired mutations. There is a general scarcity of tools that can i) systematically interrogate cancer genomes in the context of diagnostic, prognostic, and therapeutic biomarkers, ii) prioritize and highlight the most important findings, and iii) present the results in a format accessible to clinical experts. We have developed a stand-alone, open-source software package for somatic variant annotation that integrates a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene and variant level. Our application generates a tiered report that will aid the interpretation of individual cancer genomes in a clinical setting.\n\nAvailability and ImplementationThe software is implemented in Python/R, and is freely available through Docker technology. Documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/pcgr)\n\nContactsigven@ifi.uio.no

bioinformatics↗