1 - 14 of 14 results


star_border star_border star_border star_border star_border
star star star star star
Simplifies the annotation of genetic variants in VCF format. Vcfanno can extract and summarize multiple attributes from one or more annotation files and append the resulting annotations to the INFO field of the original VCF file. Vcfanno also integrates the lua scripting language so that users can easily develop custom annotations and metrics. It represents a substantial improvement over existing methods, enabling rapid annotation of whole-genome and whole-exome datasets and provides substantial analytical power to studies of disease, population genetics, and evolution.


A tool to improve the clinical utility of computational CNV prediction in exome data. cnvScan can accept input from any CNV prediction program. cnvScan consists of two steps: CNV screening and CNV annotation. CNV screening evaluates CNV prediction using quality scores and refines this using an in-house CNV database, which greatly reduces the false positive rate. The annotation step provides functionally and clinically relevant information using multiple source datasets. cnvScan enables researchers to use different programs to predict CNVs and apply suitable filtration thresholds to remove false positives and non-disease- casing variants. This reduces the time and effort required to detect disease-causing CNVs and improves the clinical utility of exonic CNV prediction.


Allows detection of somatic mutations with low allele frequencies from exome sequence data. OVarCall is a Bayesian hierarchical method which uses the information of overlapping paired-end reads for detecting low allele frequency somatic single nucleotide variants (SNVs). The method was evaluated using two types of tumor allele frequency (10% and 1%), four pairs of average and variance of DNA fragment size, and three pairs of average and variance of depth around the true SNVs or error prone sites.


A simple and powerful tool designed for variant ranking from next generation sequencing data. VaRank provides a comprehensive workflow for annotating and ranking SNVs and indels. Four modules create the strength of this workflow: (i) Variant call quality summary (total and variant depth of coverage, phred like information), to filter out false positive calls, (ii) Alamut Batch or SnpEff variant annotations, to integrate genetic and predictive information (functional impact, putative effects in the protein coding regions, population frequency...) from different sources, using HGVS nomenclature, (iii) Barcode representing the presence/absence of variants (with homozygote/heterozygote status), to search for recurrence between families or group of individuals, and (iv) Prioritization score, to rank variants according to their predicted pathogenic status. VaRank results aims at reducing the daily work of clinical geneticists and molecular biologists and will help to accelerate the progress in identifying disease causing variants.


A Web-based application for in-depth analysis and rapid evaluation of disease-causative genome sequence alterations. Vanno integrates information from biomedical databases, functional predictions from available evaluation models, and mutation landscapes from TCGA cancer types. A highly integrated framework that incorporates filtering, sorting, clustering, and visual analytic modules is provided to facilitate exploration of oncogenomics datasets at different levels, such as gene, variant, protein domain, or three-dimensional structure. Such design is crucial for the extraction of knowledge from sequence alterations and translating biological insights into clinical applications. Taken together, Vanno supports almost all disease-associated gene tests and exome sequencing panels designed for NGS, providing a complete solution for targeted and exome sequencing analysis.