Mutations in the voltage-gated potassium channel gene KCNH1 cause Temple-Baraitser syndrome and epilepsy.

Temple-Baraitser syndrome (TBS) is a multisystem developmental disorder characterized by intellectual disability, epilepsy, and hypoplasia or aplasia of the nails of the thumb and great toe. Here we report damaging de novo mutations in KCNH1 (encoding a protein called ether à go-go, EAG1 or KV10.1), a voltage-gated potassium channel that is predominantly expressed in the central nervous system (CNS), in six individuals with TBS.

Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data.

The analysis of whole-genome or exome sequencing data from trios and pedigrees has been successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyzes data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detect de novo mutations in offspring, and is scalable to large pedigrees.

AZuRE, a scalable system for automated term disambiguation of gene and protein names.

Researchers, hindered by a lack of standard gene and protein-naming conventions, endure long, sometimes fruitless, literature searches. A system is described which is able to automatically assign gene names to their LocusLink ID (LLID) in previously unseen MEDLINE abstracts. The system is based on supervised learning and builds a model for each LLID.

Suregene, a scalable system for automated term disambiguation of gene and protein names.

Researchers, hindered by a lack of standard gene and protein-naming conventions, endure long, sometimes fruitless, literature searches. A system that is able to automatically assign gene names to their LocusLink ID (LLID) in previously unseen MEDLINE abstracts is described. The system is based on supervised learning and builds a model for each LLID.