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. It can contrast the annotations of fixed or nearly fixed derived alleles in humans relative to simulated variants. This tool prioritizes functional, deleterious, and disease causal variants across a wide range of functional categories, effect sizes and genetic architectures.
Supports prioritization of noncoding variants by integrating various genomic and epigenomic annotations. The GWAVA web server allows users to retrieve precomputed scores from each of the three classifiers for all known germ-line and somatic SNVs found in Ensembl release 70.
A deep learning-based algorithmic framework for predicting the chromatin effects of sequence alterations with single nucleotide sensitivity. DeepSEA can accurately predict the epigenetic state of a sequence, including transcription factors binding, DNase I sensitivities and histone marks in multiple cell types. It can further utilize this capability to predict the chromatin effects of sequence variants and prioritize regulatory variants.
Identifies potential noncoding drivers. FunSeq is a framework that annotate and prioritize somatic alterations integrating various resources from genomic and cancer studies. The framework consists of two components: (1) data context from uniformly processing large-scale datasets and (2) a high-throughput variant prioritization pipeline. This method permits to prioritize the variant and provides a hypothesis for its potential functional impact.
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).
Aims to recognize pathogenic variants by annotating genetic variants, and especially noncoding variants. DANN is based on a deep neural network (DNN) algorithm consisting of an input layer, a sigmoid function output layer, and three 1000-node hidden layers with hyperbolic tangent activation function. This tool prioritizes putative causal variants, such as those derived from genome wide association studies (GWAS).
A whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. GenoCanyon allows to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation.