Tag: Variant Annotation


[PMID:28416821] [Nature Genetics]

Pathogenic variants that alter protein code often disrupt splicing

“We discovered that the alleles causing splicing defects cluster in disease-associated genes (for example, haploinsufficient genes). We analyzed 4,964 published disease-causing exonic mutations using a massively parallel splicing assay (MaPSy), which showed an 81% concordance rate with splicing in patient tissue. Approximately 10% of exonic mutations altered splicing, mostly by disrupting multiple stages of spliceosome assembly.”


[PMID:28502612] [American Journal of Human Genetics]

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

“MARRVEL (model organism aggregated resources for rare variant exploration) is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER.”


[] [PLoS Computational Biology]

Semantic prioritization of novel causative genomic variants

“Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets.”

3D Hotspots

[PMID:28115009] [Genome Medicine]

3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets

“Several methods are currently being used to identify driver genes based on the frequency of mutations observed in a gene across a set of tumors, e.g., MutSig and MuSiC. These methods have two limitations: (1) their unit of analysis is a gene and they do not distinguish individual driver mutations from passengers in a given gene, and (2) they are not able to detect functional mutations in infrequently mutated genes, often referred to as the “long tail” of the frequency distribution of somatic mutations in cancer.” “Here, we describe a novel method that identifies mutational 3D clusters, i.e., missense (amino-acid-changing) mutations that cluster together in 3D proximity in protein structures above a random background, with a focus on identifying rare mutations. In this largest 3D cluster analysis of whole exome or genome sequencing data in cancer to date, we analyzed more than one million somatic missense mutations in 11,119 human tumors across 32,445 protein structures from 7390 genes.”