AutDB: a gene reference resource for autism research

MindSpec Inc., 9656 Blake Lane, Fairfax, VA 22031, USA.
Nucleic Acids Research (Impact Factor: 8.81). 12/2008; 37(Database issue):D832-6. DOI: 10.1093/nar/gkn835
Source: PubMed

ABSTRACT Recent advances in studies of Autism Spectrum Disorders (ASD) has uncovered many new candidate genes and continues to do so at an accelerated pace. To address the genetic complexity of ASD, we have developed AutDB (, a publicly available web-portal for on-going collection, manual annotation and visualization of genes linked to the disorder. We present a disease-driven database model in AutDB where all genes connected to ASD are collected and classified according to their genetic variation: candidates identified from genetic association studies, rare single gene mutations and genes linked to syndromic autism. Gene entries are richly annotated for their relevance to autism, along with an in-depth view of their molecular functions. The content of AutDB originates entirely from the published scientific literature and is organized to optimize its use by the research community. The main focus of this resource is to provide an up-to-date, annotated list of ASD candidate genes in the form of reference dataset for interrogating molecular mechanisms underlying the disorder. Our model for consolidated knowledge representation in genetically complex disorders could be replicated to study other such disorders.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Clinical genomics promise to be especially suitable for the study of etiologically heterogeneous conditions such as Autism Spectrum Disorder (ASD). Here we present three siblings with ASD where we evaluated the usefulness of Whole Genome Sequencing (WGS) for the diagnostic approach to ASD. We identified a family segregating ASD in three siblings with an unidentified cause. We performed WGS in the three probands and used a state-of-the-art comprehensive bioinformatic analysis pipeline and prioritized the identified variants located in genes likely to be related to ASD. We validated the finding by Sanger sequencing in the probands and their parents. Three male siblings presented a syndrome characterized by severe intellectual disability, absence of language, autism spectrum symptoms and epilepsy with negative family history for mental retardation, language disorders, ASD or other psychiatric disorders. We found germline mosaicism for a heterozygous deletion of a cytosine in the exon 21 of the SHANK3 gene, resulting in a missense sequence of 5 codons followed by a premature stop codon (NM_033517:c.3259_3259delC, p.Ser1088Profs*6). We reported an infrequent form of familial ASD where WGS proved useful in the clinic. We identified a mutation in SHANK3 that underscores its relevance in Autism Spectrum Disorder.
    PLoS ONE 02/2015; 10(2):e0116358. DOI:10.1371/journal.pone.0116358 · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Circadian rhythms control a variety of physiological processes, but whether they may also time brain development remains largely unknown. Here, we show that circadian clock genes control the onset of critical period plasticity in the neocortex. Within visual cortex of Clock-deficient mice, the emergence of circadian gene expression was dampened, and the maturation of inhibitory parvalbumin (PV) cell networks slowed. Loss of visual acuity in response to brief monocular deprivation was concomitantly delayed and rescued by direct enhancement of GABAergic transmission. Conditional deletion of Clock or Bmal1 only within PV cells recapitulated the results of total Clock-deficient mice. Unique downstream gene sets controlling synaptic events and cellular homeostasis for proper maturation and maintenance were found to be mis-regulated by Clock deletion specifically within PV cells. These data demonstrate a developmental role for circadian clock genes outside the suprachiasmatic nucleus, which may contribute mis-timed brain plasticity in associated mental disorders. Copyright © 2015 Elsevier Inc. All rights reserved.
    Neuron 03/2015; DOI:10.1016/j.neuron.2015.02.036 · 15.98 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Identifying high-confidence candidate genes that are causative for disease phenotypes, from the large lists of variations produced by high-throughput genomics, can be both time-consuming and costly. The development of novel computational approaches, utilizing existing biological knowledge for the prioritization of such candidate genes, can improve the efficiency and accuracy of the biomedical data analysis. It can also reduce the cost of such studies by avoiding experimental validations of irrelevant candidates. In this study, we address this challenge by proposing a novel gene prioritization approach that ranks promising candidate genes that are likely to be involved in a disease or phenotype under study. This algorithm is based on the modified conditional random field (CRF) model that simultaneously makes use of both gene annotations and gene interactions, while preserving their original representation. We validated our approach on two independent disease benchmark studies by ranking candidate genes using network and feature information. Our results showed both high area under the curve (AUC) value (0.86), and more importantly high partial AUC (pAUC) value (0.1296), and revealed higher accuracy and precision at the top predictions as compared with other well-performed gene prioritization tools, such as Endeavour (AUC-0.82, pAUC-0.083) and PINTA (AUC-0.76, pAUC-0.066). We were able to detect more target genes (9/18/19/27) on top positions (1/5/10/20) compared to Endeavour (3/11/14/23) and PINTA (6/10/13/18). To demonstrate its usability, we applied our method to a case study for the prediction of molecular mechanisms contributing to intellectual disability and autism. Our approach was able to correctly recover genes related to both disorders and provide suggestions for possible additional candidates based on their rankings and functional annotations.
    Journal of computational biology: a journal of computational molecular cell biology 04/2015; 22(4):313-323. DOI:10.1089/cmb.2015.0001 · 1.67 Impact Factor