[14 June 2021]: notice amended to state that the funding information has been corrected to remove a duplicate reference to two funding organizations introduced in error during the initial correction. In the originally published version of this manuscript, requested author amendments to the Funding section were inadvertently omitted prior to publishing. The section should read: ``German Federal Ministry of Research and Education (01ZZ1802); Physician-Scientist Program of the University of Heidelberg, Faculty of Medicine, DKTK (German Cancer Consortium) School of Oncology and the Cancer Core Europe TRYTRAC program (to A.M.); MTB-Report project (VolkswagenStiftung ZN3424) (to J.H.).'' instead of ``German Federal Ministry of Research and Education (01ZZ1802); University of Heidelberg, Faculty of Medicine (to A.M.); Volkswagen Foundation (to J.H.).'' This has now been corrected.

Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.

MOTIVATION: Detailed copy number (CN) variation data can be obtained from 450k or EPIC Illumina methylation assays. However, the effects of different preprocessing strategies (normalization, transformation and selection of gain/loss cutoff values) on variant calling have not been evaluated systematically. RESULTS: We provide an R package which allows to directly compare any preprocessed CN data. It provides its own CN alteration detection methodology: segments are identified through detection of changes in variance of CN data and are subsequently filtered for significance. Meaningful cutoffs for gain/loss definition can be identified automatically through analysis of the resulting ΔCN distributions of all analyzed samples. Three exemplary datasets (2x450k, 1xEPIC) were selected for comparative analyses of Raw, Illumina, SWAN, Quantile, Noob, Funnorm and Dasen normalizations. Importantly, all CN data distributions were skewed (-0.66 to -1.2) therefore requiring different gain/loss cutoffs. Depending on the normalization method, prominent baseline differences between samples could be observed. We present a workflow, which alleviates both issues: Z-transformation removes baseline differences between samples, and automatic cutoff selection circumvents the problems accompanying the skewed distributions. Additional filtering of candidates by significance yields comparable results for most enumerated normalization methods except for SWAN. In contrast, manual cutoff determination results in highly variable numbers of variant calls, highly dependent on the selected normalization method. Taken together, we present a workflow which allows to robustly identify copy number alterations in methylation array data fairly independent of the applied normalization. AVAILABILITY AND IMPLEMENTATION: The cnAnalysis450k package is available on github ( https://github.com/mknoll/cnAnalysis450k ). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.