MOTIVATION: Computational identification of copy number variants (CNVs) in sequencing data is a challenging task. Existing CNV-detection methods account for various sources of variation and perform different normalization strategies. However, their applicability and predictions are restricted to specific enrichment protocols. Here, we introduce a novel tool named varAmpliCNV, specifically designed for CNV-detection in amplicon-based targeted resequencing data (Haloplex™ enrichment protocol) in the absence of matched controls. VarAmpliCNV utilizes principal component analysis (PCA) and/or metric dimensional scaling (MDS) to control variances of amplicon associated read counts enabling effective detection of CNV signals. RESULTS: Performance of VarAmpliCNV was compared against three existing methods (ConVaDING, ONCOCNV and DECoN) on data of 167 samples run with an aortic aneurysm gene panel (n = 30), including 9 positive control samples. Additionally, we validated the performance on a large deafness gene panel (n = 145) run on 138 samples, containing 4 positive controls. VarAmpliCNV achieved higher sensitivity (100%) and specificity (99.78%) in comparison to competing methods. In addition, unsupervised clustering of CNV segments and visualization plots of amplicons spanning these regions are included as a downstream strategy to filter out false positives. AVAILABILITY AND IMPLEMENTATION: The tool is freely available through galaxy toolshed and at: https://hub.docker.com/r/cmgantwerpen/varamplicnv. Supplementary Data File S1: https://tinyurl.com/2yzswyhh; Supplementary Data File S2: https://tinyurl.com/ycyf2fb4. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Computational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrating Pubmed abstracts, Gene Ontology, Sequence similarities, Mammalian and Human Phenotype Ontology, Pathway, Interactions, Disease Ontology, Gene Association database and Human Genome Epidemiology database, into the prediction model. We explore and address effects of sparsity and inter-feature dependencies within annotation sources, and the impact of bias towards specific annotations. Results: pBRIT models feature dependencies and sparsity by an Information-Theoretic (data driven) approach and applies intermediate integration based data fusion. Following the hypothesis that genes underlying similar diseases will share functional and phenotype characteristics, it incorporates Bayesian Ridge regression to learn a linear mapping between functional and phenotype annotations. Genes are prioritized on phenotypic concordance to the training genes. We evaluated pBRIT against nine existing methods, and on over 2000 HPO-gene associations retrieved after construction of pBRIT data sources. We achieve maximum AUC scores ranging from 0.92 to 0.96 against benchmark datasets and of 0.80 against the time-stamped HPO entries, indicating good performance with high sensitivity and specificity. Our model shows stable performance with regard to changes in the underlying annotation data, is fast and scalable for implementation in routine pipelines. Availability and implementation: http://biomina.be/apps/pbrit/; https://bitbucket.org/medgenua/pbrit. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Haloplex targeted resequencing is a popular method to analyze both germline and somatic variants in gene panels. However, involved wet-lab procedures may introduce false positives that need to be considered in subsequent data-analysis. No variant filtering rationale addressing amplicon enrichment related systematic errors, in the form of an all-in-one package, exists to our knowledge. RESULTS: We present pyAmpli, a platform independent parallelized Python package that implements an amplicon-based germline and somatic variant filtering strategy for Haloplex data. pyAmpli can filter variants for systematic errors by user pre-defined criteria. We show that pyAmpli significantly increases specificity, without reducing sensitivity, essential for reporting true positive clinical relevant mutations in gene panel data. CONCLUSIONS: pyAmpli is an easy-to-use software tool which increases the true positive variant call rate in targeted resequencing data. It specifically reduces errors related to PCR-based enrichment of targeted regions.

BACKGROUND: Microarray technology allows the analysis of genomic aberrations at an ever increasing resolution, making functional interpretation of these vast amounts of data the main bottleneck in routine implementation of high resolution array platforms, and emphasising the need for a centralised and easy to use CNV data management and interpretation system. RESULTS: We present CNV-WebStore, an online platform to streamline the processing and downstream interpretation of microarray data in a clinical context, tailored towards but not limited to the Illumina BeadArray platform. Provided analysis tools include CNV analsyis, parent of origin and uniparental disomy detection. Interpretation tools include data visualisation, gene prioritisation, automated PubMed searching, linking data to several genome browsers and annotation of CNVs based on several public databases. Finally a module is provided for uniform reporting of results. CONCLUSION: CNV-WebStore is able to present copy number data in an intuitive way to both lab technicians and clinicians, making it a useful tool in daily clinical practice.