In a study that shows both its promise and limitations in the clinic, researchers have used genomic sequencing to diagnose the genetic cause of disease in individual children — but succeeded in only a small proportion of them.

Summary: The GWAtoolbox is an R package that standardizes and accelerates the handling of data from genome-wide association studies (GWAS), particularly in the context of large-scale GWAS meta-analyses. A key feature of GWAtoolbox is its ability to perform quality control (QC) of any number of files in a matter of minutes. The implemented workflow has been structured to check three particular data quality aspects: (i) data formatting, (ii) quality of the GWAS results and (iii) data consistency across studies.

Motivation: One of the challenges in interpreting high-throughput genomic studies such as a genome-wide associations, microarray or ChIP-seq is their open-ended nature—once a set of experimentally identified regions is identified as statistically significant, at least two questions arise: (i) besides P-value, do any of these significant regions stand out in terms of biological implications? (ii) Does the set of significant regions, as a whole, have anything in common genome wide?

Summary: The article presents an infrastructure for supporting the semantic interoperability of biomedical resources based on the management (storing and inference-based querying) of their ontology-based annotations. This infrastructure consists of: (i) a repository to store and query ontology-based annotations; (ii) a knowledge base server with an inference engine to support the storage of and reasoning over ontologies used in the annotation of resources; (iii) a set of applications and services allowing interaction with the integrated repository and knowledge base.