Nucleic Acids Research Advance Access published online on November 10, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn835
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
AutDB: a gene reference resource for autism research
Saumyendra N. Basu,
Ravi Kollu and
Sharmila Banerjee-Basu*
MindSpec Inc., 9656 Blake Lane, Fairfax, VA 22031
* To whom correspondence should be addressed. Tel: +1 703 938 0161; Fax: +1 703 938 5325; Email: sharmila{at}mindspec.org
Received August 15, 2008. Revised October 8, 2008. Accepted October 14, 2008.
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ABSTRACT
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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 (
http://www.mindspec.org/autdb.html),
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.
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INTRODUCTION
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Autism (MIM 209850
[OMIM]
) is a broad-spectrum multifactorial condition
with onset in the first years of life persisting throughout
the lifetime (
1). A triad of deficits in the areas of social
communication, language development, repetitive activities and
restricted range of interests define the core symptoms used
in the diagnosis of autism (DSM IV, 1994). Autism Spectrum Disorders
(ASD) is a commonly used term to cover the wide variations of
autism. The dramatic rise in the prevalence of ASD in recent
years is of major public concern (
2,
3).
A strong genetic component underlying ASD is firmly established from various lines of studies (4–7). The search for ‘causative’ gene(s) has resulted in >10 whole genome scans reporting numerous putative linkage regions for ASD susceptibility (8,9). Genetic association studies have identified many candidate genes for ASD (10–12); however, many candidates fail to replicate between studies and populations. In a minor proportion of cases, chromosomal aberrations have been identified (13). Recently, submicroscopic copy number variations (CNVs) were strongly associated with ASD (8,14,15). Additionally, ASD is consistently associated with a number of specific genetic disorders such as Fragile X syndrome amongst others (16,17). Single-gene mutations are also linked to rare cases of ASD (18,19). The high genetic heterogeneity of ASD poses an enormous challenge for understanding disease etiology.
We have developed an autism gene database, AutDB, for ongoing cataloguing of genes linked to the disorder. Our model for representing genetic knowledge encompasses collecting all types of genes including monogenic and risk-conferring candidates linked to ASD. We have implemented an integrated informatics approach to richly annotate the candidate genes for their relevance to autism, as well as an in-depth view of molecular functions. The focus of this resource is to provide up-to-date, annotated list of ASD candidate genes to serve as reference datasets to understand disease biology. To the best of our knowledge, this is the first example of an integrated gene database for a genetically complex disorder.
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DISEASE-SPECIFIC KNOWLEDGE MODEL OF AutDB
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AutDB was designed and developed as an integrated, disease-driven
database model where both monogenic and small risk-conferring
candidates associated with ASD are collected and annotated with
diverse information (
Figure 1). In this resource, ASD-related
genes are classified into four categories including three genetic
categories based on the type of genetic variation: (i) rAut:
This category includes genes implicated in rare monogenic forms
of ASD. The types of allelic variants within this class include
rare polymorphisms and single-gene disruptions/mutations directly
linked to ASD, together with sub-microscopic deletions/duplications
(CNVs) encompassing single-genes specific for ASD. (ii) sAut:
Genes implicated in syndromic forms of autism where a sub-population
with a specific genetic syndrome develops autistic symptoms
(iii) iAut: Small risk-conferring candidate genes with common
polymorphisms identified from genetic association studies in
idiopathic ASD, and (iv) fAut: Functional candidates relevant
for ASD biology, not covered by any of the other genetic categories.
However, it is possible that a gene can belong to more than
one category depending on the mutation; a common variant conferring
risk for developing idiopathic autism, while an inactivating
mutation in the same gene placing it in higher risk-conferring
categories. In such cases, all categories are used to annotate
the genes. Both rAut and sAut categories represent monogenic
forms of ASD, however, we made a distinct class for each based
on the fact that rAut genes were identified in the course of
screening for genetic variants in individuals diagnosed with
ASD. In contrast, ASD is diagnosed secondary to the main clinical
features of the specific genetic disorder within the syndromic
forms. An additional category of functional candidates (fAut)
extracted from reports linking a gene/protein to the biology
of ASD, are also included in this resource and annotated accordingly.
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Figure 1. (A) An integrated model for collection, annotation, storage and visualization of candidate genes for ASD. (1) Candidate genes for ASD are compiled and maintained from an exhaustive search of scientific literature in the PubMed database maintained at NCBI (2) ASD candidates are classified into four categories. (3) Entries are organized in the database for search and retrieval. (4) User-friendly display of candidate genes at two levels for display and easy access with links to various external databases. (B) Search and display of candidate genes in AutDB. A search box is used to retrieve ASD candidate genes catalogued in AutDB using various gene attributes. The search results are displayed at two levels. At level 1, the gene entry is displayed in the summary row format showing: (A) gene symbol, (B) gene name, (C) chromosomal location, (D) Genetic category, (E) GAD and (F) OMIM links where available, (G) Number of autism-specific studies, (H) primary PubMed reference and (I) detail/edit button. A level 2, the entry is further displayed at a detail level in three-tier format showing: (1) gene summary with links to external databases such as Entrez Gene and UniProt, (2) relevance for autism displaying candidate gene report summary and (3) references arranged under three sub-headings: references for the candidate gene reports, highly referred and recent studies of the candidate genes.
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AutDB: THE AUTISM GENE DATABASE
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The content of AutDB originates entirely from published scientific
literature and is manually annotated by expert biologists. A
comprehensive collection of ASD-related genes was initially
compiled from an exhaustive search of the scientific literature
from PubMed database at NCBI (
http://www.ncbi.nih.gov/pubmed),
followed by timed-searches to maintain an up-to-date resource
(see Methods section for details). An AutDB entry is a candidate
gene linked to ASD with all its attributes. The steps involved
in the process for curation of an AutDB entry are schematically
shown in
Figure 1. First, all reports pertaining to a candidate
gene are extracted, counted for the number of studies and collapsed
under a single header representing the gene entry. Second, a
multi-step annotation strategy is implemented to incorporate
diverse molecular information about a candidate gene for assessment
of its relevance for ASD. To enhance functional knowledge of
a candidate gene, our annotation model also expands entries
by drilling down to highly cited articles on candidate gene/protein,
together with recently published articles to represent current
knowledge of gene functions. This feature of AutDB provides
functional information of an ASD candidate gene beyond the basic
information available in large public databases. Finally, candidate
genes are classified based on the type of genetic category according
to AutDB knowledge model (described in the previous section).
An example of an annotated AutDB gene is shown in
supplementary Figure S1.
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SEARCH AND DISPLAY OF AutDB DATA
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Genetic information in AutDB can be searched and displayed in
several ways including complex Boolean queries. A representative
search result of AutDB is shown in
Figure 2. In this example,
searching for ASD candidate genes on Chromosome 2 will retrieve
a comprehensive list of ASD candidate genes in a tabular format.
This list includes candidates reported from genetic association
studies, together with rare single-gene mutations and functional
candidates. Searching by gene name or gene symbol will retrieve
a gene entry that can be displayed at two levels (
Figure 1B).
At the first level of display in the summary row format, each
entry is annotated with gene symbol, gene name, chromosomal
location, genetic category and GAD and OMIM links where available,
total number of autism-specific studies used to create the entry
in AutDB, together with a primary PubMed reference reporting
the candidate gene. Each entry is further displayed at a detail
level showing (i) gene summary with links to external databases
such as Entrez Gene (
http://www.ncbi.nlm.nih.gov/sites/entrez)
and UniProt (
http://www.uniprot.org/) to provide standardized
generic information on candidate genes, (ii) relevance for autism
showing autism-specific information describing the type of genetic
variants linked to ASD and the studies reporting the gene and
(iii) references arranged under three subheadings. First, references
linking the gene to ASD are displayed. A primary reference in
AutDB is defined as the first positive report linking the candidate
gene to ASD. Next, highly referred studies on the candidate
gene extracted using Google scholar based on number of citations
are shown. Finally, references for recent studies pertaining
to the candidate genes extracted from timed-search of PubMed
are also provided. A flow-chart describing the search and display
of an AutDB entry is shown in
Figure 1B. Annotated list of ASD
candidate genes can also be obtained from AutDB to serve as
primer for building customized reference datasets. These reference
datasets provide global attributes of ASD-linked genes and define
an entry point for further enhancement and optimization based
on specific research requirements.
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DISCUSSION
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AutDB has been developed to provide current knowledge on candidate
genes linked to ASD and their relevance for the disorder. We
have adopted several novel strategies to design the resource
with the focus on gene functions. For example, each candidate
gene is annotated with highly referred citations, together with
recent recommendation to provide a panoramic view of gene function.
In our view, AutDB annotation model can be applied to annotate
protein entries in other databases. Compared to other disease-specific
databases such as AlzGene (
http://www.alzforum.org/res/com/gen/alzgene/),
which collects only candidates identified from genetic association
studies, our integrative model includes all types of genetic
variations implicated in ASD. A recent search of OMIM database
(
http://www.ncbi.nlm.nih.gov/sites/entrez?db=OMIM) for genes
linked to autism retrieved
60 entries (August, 2008), compared
to 133 entries in AutDB as of this writing. A recent review
article lists 26 candidate genes (
12), thereby affirming that
without a systematic and sustained effort it is difficult to
keep up with the rapidly evolving field of autism biology. The
first rare single gene mutation in ASD was reported in 2003
(
18), however, in the last two years with the application of
genomic technologies, a steady stream of single gene mutations
in ASD is reported in the scientific literature (
15,
20). Consequently,
the balance is shifting between reported single-gene mutations
and candidates identified from genetic association studies.
The current trend in autism genetics validates the integrative
model in AutDB.
Our major focus is to provide a filtered, annotated reference set of ASD-linked genes to the research community for immediate application in the areas of (i) building predictive disease models using bioinformatics (manuscript in preparation) and (ii) systems-level analysis of various molecular data to gain insight into the etiology of ASD.
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PERSPECTIVE
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Autism, a rare encounter in mid last century, is becoming increasingly
prevalent in the last two decades. Accumulating evidence strongly
point towards many genetic causes can contribute to the development
of ASD. In practical terms, the number of articles reporting
putative candidate loci, as well as high throughput array-based
studies reporting many loci in a single publication are rapidly
accumulating. We have created AutDB to bridge the gap between
the vast amount of information embedded in the scientific literature
and consolidated knowledge representation of ASD for molecular
analysis. Lastly, the dynamic nature of this database provides
a window into the current state of research to aid in the early
diagnostics and therapeutics for individuals with autism.
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METHOD
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Design of an integrated platform
AutDB is a portal developed in JAVA on the J2EE platform on
Linux with an Relational Database Management System (RDBMS)
backend as its repository. The portal for AutDB is designed
to be extensible where newer modules could be incorporated with
relative ease by configuration. The application is deployed
as a webapps in the Tomcat Application server connecting to
the RDBMS. Connection pooling is provided by the Application
Server, which greatly decreases the load on the system and enhances
the performance. It also connects to the NLM database with the
help of their DTDs and collects relevant information from the
NLM databases for the end user. Apart from batch mass updates
and new additions through database load programs, AutDB also
provides interfaces for online updates restricted by user roles.
Most importantly, AutDB provides interfaces within the Autism
research community for co-operative, moderated annotations and
curations for deeply annotated genes or gene sets. A search
engine is provided on different attribute names on their applicable
data sets to build queries at varying degrees of complexity.
Development of AutDB
Collection, storage and categorization of candidate genes
To collect ASD candidate genes for the first release of AutDB, we performed a comprehensive search of all articles in the PubMed database maintained at NCBI (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=pubmed). The search terms included ‘gene’ AND (‘autism’ OR ‘autistic’) restricted to the titles and abstracts of the publications for retrieval. Furthermore, candidate genes listed in review articles on molecular genetics of ASD, along with cross-references therein, were mapped and added (if new) to our candidate gene list from PubMed searches to compile the most exhaustive gene set. After the first release, starting from June 2006, a daily semi automated search of the PubMed with the same keywords is implemented to maintain an up-to-date resource of all candidate genes linked to ASD. Additionally, relevant journal articles in the fields of genetics, neurobiology, and psychiatry are screened on a regular basis to enrich the resource.
To select the primary reference for a candidate gene, the first positive report linking the gene to (ASD) was searched in PubMed. However, to select the primary reference for a ‘Syndromic autism’ gene where a large number of reports are published connecting the syndrome to autism, we adopted the following steps: First, google scholar is used with the criteria [(autism OR autistic) AND ‘syndrome name’ AND ‘gene name’] to search for the most highly cited reports. Next, a primary reference is selected among these highly cited reports by reading the article (see supplementary Table 1).
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SUPPLEMENTARY DATA
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Supplementary Data are available at NAR Online.
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Funding
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Funding for open access charge: MindSpec Inc., a non-profit
organization dedicated to autism research.
Conflict of interest statement: None declared.
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ABSTRACT
INTRODUCTION