BackgroundIntellectual disability (ID) is characterised by an extreme genetic heterogeneity. Several hundred genes have been associated to monogenic forms of ID, considerably complicating molecular diagnostics. Trio-exome sequencing was recently proposed as a diagnostic approach, yet remains costly for a general implementation.MethodsWe report the alternative strategy of targeted high-throughput sequencing of 217 genes in which mutations had been reported in patients with ID or autism as the major clinical concern. We analysed 106 patients with ID of unknown aetiology following array-CGH analysis and other genetic investigations. Ninety per cent of these patients were males, and 75% sporadic cases.ResultsWe identified 26 causative mutations: 16 in X-linked genes (ATRX, CUL4B, DMD, FMR1, HCFC1, IL1RAPL1, IQSEC2, KDM5C, MAOA, MECP2, SLC9A6, SLC16A2, PHF8) and 10 de novo in autosomal-dominant genes (DYRK1A, GRIN1, MED13L, TCF4, RAI1, SHANK3, SLC2A1, SYNGAP1). We also detected four possibly causative mutations (eg, in NLGN3) requiring further investigations. We present detailed reasoning for assigning causality for each mutation, and associated patients’ clinical information. Some genes were hit more than once in our cohort, suggesting they correspond to more frequent ID-associated conditions (KDM5C, MECP2, DYRK1A, TCF4). We highlight some unexpected genotype to phenotype correlations, with causative mutations being identified in genes associated to defined syndromes in patients deviating from the classic phenotype (DMD, TCF4, MECP2). We also bring additional supportive (HCFC1, MED13L) or unsupportive (SHROOM4, SRPX2) evidences for the implication of previous candidate genes or mutations in cognitive disorders.ConclusionsWith a diagnostic yield of 25% targeted sequencing appears relevant as a first intention test for the diagnosis of ID, but importantly will also contribute to a better understanding regarding the specific contribution of the many genes implicated in ID and autism.

One consistent finding among studies using shotgun metagenomics to analyze whole viral communities is that most viral sequences show no significant homology to known sequences. Thus, bioinformatic analyses based on sequence collections such as GenBank nr, which are largely comprised of sequences from known organisms, tend to ignore a majority of sequences within most shotgun viral metagenome libraries. Here we describe a bioinformatic pipeline, the Viral Informatics Resource for Metagenome Exploration (VIROME), that emphasizes the classification of viral metagenome sequences (predicted open-reading frames) based on homology search results against both known and environmental sequences. Functional and taxonomic information is derived from five annotated sequence databases which are linked to the UniRef 100 database. Environmental classifications are obtained from hits against a custom database, MetaGenomes On-Line, which contains 49 million predicted environmental peptides. Each predicted viral metagenomic ORF run through the VIROME pipeline is placed into one of seven ORF classes, thus, every sequence receives a meaningful annotation. Additionally, the pipeline includes quality control measures to remove contaminating and poor quality sequence and assesses the potential amount of cellular DNA contamination in a viral metagenome library by screening for rRNA genes. Access to the VIROME pipeline and analysis results are provided through a web-application interface that is dynamically linked to a relational back-end database. The VIROME web-application interface is designed to allow users flexibility in retrieving sequences (reads, ORFs, predicted peptides) and search results for focused secondary analyses.