1 - 14 of 14 results

CADD / Combined Annotation Dependent Depletion

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Scores the deleteriousness of single nucleotide variants as well as insertion/deletions variants in the human genome. CADD integrates many diverse annotations into a quantitative score. The basis of CADD is to contrast the annotations of fixed or nearly fixed derived alleles in humans relative to simulated variants. CADD can quantitatively prioritize functional, deleterious, and disease causal variants across a wide range of functional categories, effect sizes and genetic architectures and can be used prioritize causal variation in both research and clinical settings.


Provides a generalized linear model for functional genomic data and genome annotations. LINSIGHT is a computational method that outperforms state-of-the-art prediction methods in the task of prioritizing noncoding disease variants from the Human Gene Mutation database (HGMD) and the National Center for Biotechnology Information (NCBI) ClinVar database. By integrating a large number of genomic features, LINSIGHT provides a precise, high-resolution description of the fitness consequences of noncoding mutations in human genome.


A sequence-based computational method to predict the effect of regulatory variation, using a classifier (gkm-SVM) that encodes cell type-specific regulatory sequence vocabularies. The induced change in the gkm-SVM score, deltaSVM, quantifies the effect of variants. We show that deltaSVM accurately predicts the impact of SNPs on DNase I sensitivity in their native genomic contexts and accurately predicts the results of dense mutagenesis of several enhancers in reporter assays. deltaSVM provides a powerful computational approach to systematically identify functional regulatory variants.


Allows prediction of the functional consequences of non-coding and coding single nucleotide variants (SNVs). FATHMM-XF is a method consisting in an improvement over the predictor FATHMM-MKL. The software was built using supervised machine learning with labeled examples ascribed to pathogenic (positive) or benign (neutral) mutations. It assigns a confidence score (a p-score) for every prediction to simplify interpretation and focus analysis on a subset of high-confidence predictions (cautious classification).

INFERNO / INFERring the molecular mechanisms of NOncoding genetic variants

Provides a method to determine functional genetic variants underlying genetic association signals and to characterize their tissue-specific effects on regulatory elements, target genes, and downstream biological processes. INFERNO is a pipeline available as both a standalone software to perform the full process, and a web application which provides two default genome wide association studies (GWASs) datasets and can compute a lighter analysis.