Personal Cancer Genome Reporter: variant interpretation report for precision oncology
| Autor: | Lars Birger Aasheim, Eivind Hovig, Sigve Nakken, Daniel Vodak, Ghislain Fournous, Ola Myklebost |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Statistics and Probability Somatic cell Computer science MEDLINE Context (language use) Computational biology Biochemistry Genome 03 medical and health sciences 0302 clinical medicine Cancer genome Neoplasms medicine Humans Precision Medicine Molecular Biology Gene computer.programming_language Genome Human Interpretation (philosophy) Genetic Variation Cancer Python (programming language) Genome Analysis Precision medicine medicine.disease Data science Applications Notes Computer Science Applications Computational Mathematics 030104 developmental biology Computational Theory and Mathematics Biomarker (medicine) computer Software 030217 neurology & neurosurgery |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/btx817 |
| Popis: | Summary Individual 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. Availability and implementation The 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. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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