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AutismKB: an evidence-based knowledgebase of autism genetics.

Nucleic acids research | Jan 23, 2012

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with a prevalence of 0.9-2.6%. Twin studies showed a heritability of 38-90%, indicating strong genetic contributions. Yet it is unclear how many genes have been associated with ASD and how strong the evidence is. A comprehensive review and analysis of literature and data may bring a clearer big picture of autism genetics. We show that as many as 2193 genes, 2806 SNPs/VNTRs, 4544 copy number variations (CNVs) and 158 linkage regions have been associated with ASD by GWAS, genome-wide CNV studies, linkage analyses, low-scale genetic association studies, expression profiling and other low-scale experimental studies. To evaluate the evidence, we collected metadata about each study including clinical and demographic features, experimental design and statistical significance, and used a scoring and ranking approach to select a core data set of 434 high-confidence genes. The genes mapped to pathways including neuroactive ligand-receptor interaction, synapse transmission and axon guidance. To better understand the genes we parsed over 30 databases to retrieve extensive data about expression patterns, protein interactions, animal models and pharmacogenetics. We constructed a MySQL-based online database and share it with the broader autism research community at http://autismkb.cbi.pku.edu.cn, supporting sophisticated browsing and searching functionalities.

Pubmed ID: 22139918 RIS Download

Mesh terms: Autistic Disorder | Databases, Genetic | Genetic Linkage | Genetic Variation | Humans | Knowledge Bases | Molecular Sequence Annotation | User-Computer Interface

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This is a list of tools and resources that we have found mentioned in this publication.


Ensembl

A collection of genome databases for vertebrates and other eukaryotic species with DNA and protein sequence search capabilities. The goal of Ensembl is to automatically annotate the genome, integrate this annotation with other available biological data and make the data publicly available via the web. The range of available data has also expanded to include comparative genomics, variation and regulatory data. Ensembl allows users to: upload and analyze data and save it to an Ensembl account; search for a DNA or protein sequence using BLAST or BLAT; fetch desired data from the public database, using the Perl API; download the databases via FTP in FASTA, MySQL and other formats; and mine Ensembl with BioMart and export sequences or tables in text, HTML, or Excel format. The DNA sequences and assemblies used in the Ensembl genebuild are provided by various projects around the world. Ensembl has entered into an agreement with UCSC and NCBI with regard to sequence identifiers in order to improve consistency between the data provided by different genome browsers. The site also links to the Ensembl blog with updates on new species and sequences as they are added to the database.

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UniProt

Resource for protein sequence and functional information that welcomes updates, corrections, and the submission of new protein sequence data. It is comprised of four components, each optimized for different uses. UniProt Knowledgebase (UniProtKB) is the central access point for curated protein information, including function, classification, and cross-reference, and contains two sections: UniProtKB/Swiss-Prot, which is manually annotated and reviewed; and UniProtKB/TrEMBL, which is automatically annotated and not reviewed. UniProt Reference Clusters (UniRef) are databases providing clustered sets of sequences from the UniProtKB and selected UniProt Archive records to obtain complete coverage of sequence space at several resolutions while hiding redundant sequences. UniProt Archive (UniParc) is a comprehensive repository used to keep track of sequences and their identifiers. UniProt Metagenomic and Environmental Sequences (UniMES) database is a repository specifically developed for metagenomic and environmental data.

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GO

A community-based bioinformatics resource consisting of three structured controlled vocabularies (ontologies) for the annotation of gene products with respect to their molecular function, cellular component, and biological role in a species-independent manner. This initiative to standardize the representation of gene and gene product attributes across species and databases is an effort to address the need for consistent descriptions of gene products in different databases. The Gene Ontology project encourages input from the community into both the content of the GO and annotation using GO. There are three separate aspects to this effort: first, they write and maintain the ontologies themselves; second, they make cross-links between the ontologies and the genes and gene products in the collaborating databases; and third, they develop tools that facilitate the creation, maintenance and use of ontologies. The controlled vocabularies are structured so that users can query them at different levels: for example, uers can use GO to find all the gene products in the mouse genome that are involved in signal transduction, or users can zoom in on all the receptor tyrosine kinases. This structure also allows annotators to assign properties to gene products at different levels, depending on how much is known about a gene product.

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