© 2014. Published by The Company of Biologists Ltd | Development (2014) 141, 2516-2523 doi:10.1242/dev.105320
RESEARCH ARTICLE
TECHNIQUES AND RESOURCES
MetaImprint: an information repository of mammalian imprinted
genes
ABSTRACT
Genomic imprinting is a complex genetic and epigenetic phenomenon
that plays important roles in mammalian development and diseases.
Mammalian imprinted genes have been identified widely by
experimental strategies or predicted using computational methods.
Systematic information for these genes would be necessary for the
identification of novel imprinted genes and the analysis of their
regulatory mechanisms and functions. Here, a well-designed
information repository, MetaImprint (http://bioinfo.hrbmu.edu.cn/
MetaImprint), is presented, which focuses on the collection of
information concerning mammalian imprinted genes. The current
version of MetaImprint incorporates 539 imprinted genes, including
255 experimentally confirmed genes, and their detailed research
courses from eight mammalian species. MetaImprint also hosts
genome-wide genetic and epigenetic information of imprinted
genes, including imprinting control regions, single nucleotide
polymorphisms, non-coding RNAs, DNA methylation and histone
modifications. Information related to human diseases and functional
annotation was also integrated into MetaImprint. To facilitate data
extraction, MetaImprint supports multiple search options, such as by
gene ID and disease name. Moreover, a configurable Imprinted
Gene Browser was developed to visualize the information on
imprinted genes in a genomic context. In addition, an Epigenetic
Changes Analysis Tool is provided for online analysis of DNA
methylation and histone modification differences of imprinted genes
among multiple tissues and cell types. MetaImprint provides a
comprehensive information repository of imprinted genes, allowing
researchers to investigate systematically the genetic and epigenetic
regulatory mechanisms of imprinted genes and their functions in
development and diseases.
KEY WORDS: Imprinted genes, Database, Genetic and epigenetic
information, Imprinting disorder
INTRODUCTION
Genomic imprinting, a complex epigenetic phenomenon whereby
gene expression occurs in a parent-of-origin specific manner in
various cell or tissue types, has an essential role in normal growth and
development (Reik and Walter, 2001). Fetal development is promoted
generally by paternally expressed genes (Wu et al., 2006), whereas
maternally expressed genes have a tendency to restrict fetal growth
(Abu-Amero et al., 2008). Imprinting is found predominantly in
1
College of Bioinformatics Science and Technology, Harbin Medical University,
2
Harbin 150081, China. School of Life Science and Technology, State Key
Laboratory of Urban Water Resource and Environment, Harbin Institute of
Technology, Harbin 150001, China.
*Authors for correspondence ([email protected]; [email protected])
Received 27 October 2013; Accepted 12 April 2014
2516
placental mammals, and may participate in the balance of parental
resource allocation to the offspring (Ishida and Moore, 2013). It has
been reported that imprinting is related to the evolution of the placenta
in mammals (Fang et al., 2012). Defects in imprinting have been
associated with congenital diseases, infertility, molar pregnancy and
defective assisted reproductive cells (Tomizawa and Sasaki, 2012).
Loss of imprinting (LOI) induced by methylation disruption at the
imprinted loci may lead to serious imprinting disorders (e.g.
Beckwith–Wiedemann syndrome) (Bliek et al., 2001) and cancers
(e.g. Wilms’ tumor) (Rancourt et al., 2013; Wu et al., 2008).
Extensive studies have attempted to identify the imprinted genes
in various cell and tissue types of mammals, and several
experimental strategies have been used to identify imprinted
genes, based on DNA methylation status analysis between two
alleles, gene-targeting experiments and whole-transcriptome RNAsequencing analysis (Babak et al., 2008; Barlow et al., 1991; Luedi
et al., 2007; Wang et al., 2008). In addition, computational methods
have been used to predict new imprinted genes, based on DNA
sequence features, CpG islands, repetitive elements, miRNA
clusters and epigenetic modifications in the human and mouse
genomes (Brideau et al., 2010; Luedi et al., 2005). The mechanisms
that establish and maintain imprinted genes have been found using
different perspectives, such as DNA sequence features (Khatib
et al., 2007), non-coding RNAs (ncRNAs) (Royo and Cavaille,
2008) and epigenetic modifications (Barlow, 2011; Koerner and
Barlow, 2010).
Over the past few years, several databases have been established to
store information related to imprinted genes, including MouseBook
(Blake et al., 2010), WAMIDEX (Schulz et al., 2008), the Otago
Catalogue of Imprinted Genes (Morison and Reeve, 1998) and
Geneimprint (Bischoff et al., 2009). Both MouseBook and
WAMIDEX mainly focus on mouse imprinted genes. Although the
Otago Catalogue of Imprinted Genes and Geneimprint cover diverse
species, the records for the imprinted gene are limited, such that even
the genomic loci information is limited or even lacking. In addition, as
reference genes in public nucleotide databases are updated, a portion
of the imprinted gene records in these databases have become outdated
and cannot be positioned in the recent versions of the genomes. In
addition, the emergence of high-throughput sequencing technologies
in recent years has made it possible to perform genome-wide
annotations of imprinted genes, such as cell/tissue specificity, genetic
and epigenetic information, as well as related diseases. Therefore, a
specifically designed database, MetaImprint, was constructed by
manually mining the literature related to imprinted genes and
incorporating meta-information from the high-throughput genetic
and epigenetic data. The current version of MetaImprint covers
imprinted genes from eight species (human, mouse, sheep, rat, dog,
rabbit, cow and pig).
Compared with the pre-existing databases, MetaImprint has four
advantages. (1) The allele or cell/tissue specificity and detailed
DEVELOPMENT
Yanjun Wei1, Jianzhong Su1, Hongbo Liu2, Jie Lv2, Fang Wang1, Haidan Yan1, Yanhua Wen1, Hui Liu2,
Qiong Wu2, * and Yan Zhang1,*
RESEARCH ARTICLE
Development (2014) 141, 2516-2523 doi:10.1242/dev.105320
research processes of imprinted genes are shown in the detailed
pages as a result of literature mining. (2) MetaImprint provides the
integrative annotation of genetic and epigenetic information for
each imprinted gene at any given chromosome location, such as
imprint control regions (ICRs), single nucleotide polymorphisms
(SNPs), repetitive elements, ncRNAs, DNA methylation and
histone modifications. In particular, a visual engine, Imprinted
Gene Browser, was developed to show a landscape view of the
imprinted genes in the context of existing genetic and epigenetic
annotations. (3) The detailed relationships between imprinted genes
and human diseases are also incorporated. (4) MetaImprint provides
two analysis tools to identify epigenetic differences and for
functional enrichment analysis of imprinted genes among cell/
tissue types. In brief, MetaImprint is a comprehensive and crossspecies database for the systematic study of imprinted genes in terms
of genetics and epigenetics, as well as diseases and evolution.
RESULTS
Database content and construction
The current release of MetaImprint was designed to provide
imprinted gene-related information regarding the imprinting states,
epigenetic modifications, and related diseases. By mining the
literature manually, we collected 845 imprinted gene-related studies
from 1991 to date. From these studies, 539 mammalian imprinted
genes from eight mammalian species were obtained, including 317
human genes, 146 mouse genes, three sheep genes, seven rat genes,
one dog gene, one rabbit gene, 35 cow genes and 29 pig genes
(Table 1). Among these imprinted genes, 255 imprinted genes have
been validated experimentally and the remainder were predicated by
computational methods from the human and mouse genomes.
Based on the collected information, we investigated the research
trends of imprinted genes over time. This revealed that the number
of studies related to imprinting increased from 1900, especially in
the years since 2007 (Fig. 1A). In most of the studies, allele-specific
expression of genes was used to discover imprinted genes by
experimental and computational methods. For example, Igf2 was
identified as the first imprinted gene in mouse in 1991, and H19 in
human in 1992. The time spectrum for the discovery of imprinted
genes revealed that there was a spurt in the identification of novel
imprinted genes around 2008 (Fig. 1B). However, the number of
newly discovered imprinted genes decreased year by year from 2010
whereas the number of studies related to imprinting genes increased.
Although the identification of novel imprinted genes is useful,
researchers have been paying more attention to the regulatory
mechanisms and functions of imprinted genes. Thus, the regulatory
and functional information of imprinted genes would be necessary
for current analysis.
DNA methylation and histone modification information may
be useful to uncover epigenetic regulatory effects on genome
imprinting. Recently, increasing numbers of studies related to
epigenetic modifications (e.g. DNA methylation) have revealed that
epigenetic modifications are regulators of allele-specific expression
of imprinted genes (Court et al., 2014; Fang et al., 2012; Shoemaker
et al., 2010). In addition, some studies focused on the imprinting
mechanisms in the development and related functions of imprinted
genes (Chang et al., 2013). Studies on SNPs, which are ideal
markers for identifying imprinted genes, revealed the roles of
abnormal imprinting in complex traits, including complex diseases
(Ishida and Moore, 2013; Lawson et al., 2013). It has been reported
that imprinted genes contain tandem repeat arrays in their CpG
islands more frequently (Hutter et al., 2006). Thus, we obtained and
normalized the genomic and epigenomic data from public data
resources including the University of California Santa Cruz (UCSC)
Genome Bioinformatics Site (Karolchik et al., 2014), the National
Center for Biotechnology Information (NCBI) Gene Expression
Omnibus (GEO) database (Barrett et al., 2013) and the NCBI
Sequence Read Archive (SRA) (Wheeler et al., 2007).
By integrating genetic and epigenetic information with imprinted
genes and other information, we constructed the MetaImprint
database based on three major software components: a MySQL
relational database, an Apache Tomcat web server and Java-based
backstage processing programs. To guarantee the stability and high
performance of the database, a flexible query engine was developed
using a Java web application framework (Apache Struts2) and a
persistence framework (iBATIS), which automates the mapping
between MySQL databases and objects in Java. We also developed
two pragmatic online tools to analyze imprinted genes, one for
functional enrichment of imprinted genes and the other to analyze
the differences in epigenetic modification among multiple tissues or
cells. Asynchronous JavaScript and XML (AJAX) and JavaServer
Pages (JSP) were used to build the ImprintedGeneBrowser for
visualization of information related to imprinted genes, such as
imprinting states, imprinting control region, allelic-specific
methylation, and imprinting-related diseases.
Table 1. MetaImprint data content and statistics (1 January 2014)
Imprinted gene
Total
Experimentally validated (I)
Predicted (P)
Loss of imprint (LOI)
Not imprinted (NI)
Genome information
RefGenome
RefGene
DNA methylome
Histone modification
Allele-specific methylation
Disease
Other integrated data
Repeat
SNP
Human
Mouse
Dog
Cow
Sheep
Pig
Rabbit
Rat
317
65
200
21
31
146
132
1
4
7
1
1
0
0
0
35
27
0
0
8
3
3
0
0
0
29
20
0
0
9
1
1
0
0
0
7
6
1
0
0
hg19
23,850
19
21
5
344
mm9
23,427
10
18
0
83
canFam3
1327
0
0
0
0
bosTau7
13,584
0
0
0
18
oviAri1
710
0
0
0
0
susScr3
3950
0
0
0
0
oryCun2
1301
0
0
0
0
rn4
16,849
0
0
0
0
10
11
10
1
10
0
10
0
10
0
10
0
10
0
10
1
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DEVELOPMENT
Data statistics
Data content
RESEARCH ARTICLE
Development (2014) 141, 2516-2523 doi:10.1242/dev.105320
Database usage and access
Overview of the usage of the MetaImprint database
MetaImprint is available at http://bioinfo.hrbmu.edu.cn/MetaImprint.
The database supports four basic operations: search, browser, analysis
and download. As shown in Fig. 1C, users can obtain the imprinted
genes and related annotations by exercising user-specified options.
The query results are listed as a table with links to the Imprinted Gene
Browser for visualization, and links to download for further analysis.
Two analysis tools were developed to identify epigenetic differences
of imprinted genes among different tissue/cell types and for
functional enrichment analysis of imprinted gene sets. All of the
datasets hosted in the MetaImprint database can be downloaded to
local servers for further analysis. Novel imprinted genes and their
related information can be directly uploaded into the database using
the ‘submit’ option to keep MetaImprint up to date.
Querying imprinted gene records
MetaImprint provides a flexible search engine based on a
MySQL backend. The starting point of MetaImprint is the search
page, which provides three entries including the Imprinted gene
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search, Disease search and Imprinted Gene Browser. Each search
entry provides several query parameters, such as species, gene
name/ID, genomic location, chromosome band, expressed
parental allele and disease type. The more query parameters
used to query imprinted genes, the more precise the query results
are. Quick search results are shown in Fig. 2A. The links on these
genes would bring users to the detailed information for the
corresponding imprinted genes.
As shown in Fig. 2B, the imprinting states of a gene are divided
into four states (see Materials and Methods). Moreover, the related
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway,
gene ontology (GO) terms and disease information for the imprinted
genes are also provided in MetaImprint. The research courses of
imprinted genes include gene names, genome locations,
EnsembleID, imprinting states, allele-specific expression, tissuespecific types, measurement methods and relevant publications
shown in chronological order. As shown in Fig. 2B, the summary
information for the human imprinted gene IGF2 consists of six
kinds of information. (1) Its genome location is chr11: 21503462160204 (hg19). (2) Its Ensembl (space) ID is ENSG00000167244.
DEVELOPMENT
Fig. 1. Database content and
construction. (A) The number of
published studies of imprinted genes
since 1991. (B) The number of
identified imprinted genes since
1991. (C) Architectural overview of
the MetaImprint database.
RESEARCH ARTICLE
Development (2014) 141, 2516-2523 doi:10.1242/dev.105320
Fig. 2. A comprehensive view of
MetaImprint search results. (A) The
results table after searching five
imprinted genes. (B) Details of a
selected gene from the results table
showing imprinting states, gene IDs,
research records and related
diseases. (C) Visualization of genetic
and epigenetic information in the
Imprinted Gene Browser. (D) Two
analysis tools in MetaImprint. Left:
the Epigenetic Changes Analysis tool
for identification of the imprinted
genes modified by different
epigenetic modifications. Right: the
Functional Enrichment Analysis tool
for functional analysis of a set of
imprinted genes.
Imprinted gene browser
A user-friendly and configurable genome browser, Imprinted Gene
Browser, was developed to view the query results for imprinted
genes intuitively. Imprinted Gene Browser is linked to the MySQL
backend to display the genetic and epigenetic features of imprinted
genes, including the upstream and downstream genetic information,
repetitive elements, SNPs, ncRNAs, ICRs, CpG islands, DNA
methylation and histone modifications. As shown in Fig. 2C, the
Imprinted Gene Browser provides options to zoom through the
predefined genomic regions or gene ID/symbols. Users can view a
comprehensive landscape of each imprinted gene along with the
epigenetic and genetic features in the Imprinted Gene Browser.
Users can also review the imprinted genes that are located in specific
chromosome regions, such as a given chromosome band.
Analysis tools
MetaImprint also provides two analysis tools: the Epigenetic Changes
Analysis tool and the Functional Enrichment Analysis tool (Fig. 2D).
As is well known, DNA methylation mediates imprinted gene
expression by passing an epigenetic state across generations and
differentially marking specific regulatory regions on maternal and
paternal alleles. The dynamics of epigenetic modification may be
indispensable for genomic imprinting, which is a feature of
mammalian development (Liu et al., 2013). Therefore, DNA
methylation and histone modification information may be useful to
uncover the mechanisms by which epigenetic modification mediate
the allele-specific expression of imprinted genes. The Epigenetic
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DEVELOPMENT
(3) Its imprinting states are imprinted (I) according to 46 related
publications, including 39 records supporting maternal-originspecific expression, only seven supporting paternal-origin-specific
expression and 22 supporting biallelic expression. (4) The KEGG
and GO terms related to IGF2. (5) The studies of IGF2 as an
imprinted gene are from 1994 in human. (6) IGF2 is related to some
diseases, including medulloblastomas, hepatocarcinomas, neural
tube defects and Silver Russell syndrome (SRS). All the query
results can be viewed by the Imprinted Gene Browser and
downloaded to a local computer for further analysis.
RESEARCH ARTICLE
Changes Analysis tool is based on analysis of variance (ANOVA) to
identify the epigenetic differences of the average histone modification
signals or the average DNA methylation levels of imprinted genes
among different cells or tissues. The results list the information of
RefSeq genes, repetitive elements, SNPs and ncRNAs. The epigenetic
features and P-values of ANOVA can be adjusted by the users when
necessary. Moreover, the Functional Enrichment Analysis tool
supported by the Database for Annotation, Visualization and
Integrated Discovery (DAVID) application programming interface
(API) (Dennis et al., 2003) is provided for users to identify the
enriched biological functions for any given lists of imprinted genes.
And users can also submit a list of gene (NCBI gene ID/gene symbol)
to identify enriched biological function based on KEGG and GO.
The applications of MetaImprint in the systematic analysis of
imprinted genes
Based on the integrated information of imprinted genes in
MetaImprint, we studied the functions of imprinted genes. As an
example, 317 human imprinted genes in MetaImprint were analyzed
for functional enrichment using the Functional Enrichment Analysis
tool. This revealed that these imprinted genes are significantly enriched
in biological processes, including embryonic morphogenesis,
embryonic organ development, pattern specification processes, etc.
(Fig. 3A), indicating the important roles of these imprinted genes in
development. In addition, numerous genetic diseases are associated
Development (2014) 141, 2516-2523 doi:10.1242/dev.105320
with imprinting defects, including Beckwith–Wiedemann syndrome
(BWS) and multiple cancers (Ishida and Moore, 2013; Surani et al.,
1990). It has been reported that imprinting is related to the evolution of
the placenta in mammals and defects in genome imprinting have been
associated with several diseases. From the disease information of
imprinted genes in MetaImprint, we found that 99 of the 317 human
imprinted genes analyzed are associated with disorders (Fig. 3B). For
example, imprinted genes IGF2 and H19 are related to 38 diseases. We
also found that 84 diseases are induced by at least one imprinted gene
(Fig. 3C). It should be noted that 24 imprinted genes are associated
with Wilms’ tumor. Furthermore, we built an imprinting disorder
network and disorder-related gene network to investigate the potential
relationships between imprinted genes and various diseases (Fig. 3D).
This analysis showed that imprinted genes participate in many kinds of
cancer, including Wilms’ tumor and bladder cancer, which is
consistent with previous findings of loss of imprinting in a large
variety of human tumors (Jelinic and Shaw, 2007). Many imprinted
genes tend to participate jointly in one disease, indicating their coregulation in human disorders.
Furthermore, we used the Epigenetic Changes Analysis tool to
analyze the methylation difference of human imprinted genes
between liver and hepatic carcinoma. Among 317 human imprinted
genes, 253 genes with available data were analyzed, and 97 were
identified as differentially methylated genes (DMGenes) (Fig. 4A).
Based on the disease information in MetaImprint, 11 of these
DEVELOPMENT
Fig. 3. Systematic analysis of imprinted
genes based on the comprehensive
information of imprinted genes in
MetaImprint. (A) Functional analysis of 317
human imprinted genes. (B) Genes involved in
human disorders. (C) Diseases induced by
human imprinted genes. (D) Examples of the
imprinting disorder network and the disorder
gene network.
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RESEARCH ARTICLE
Development (2014) 141, 2516-2523 doi:10.1242/dev.105320
DMGenes are related to hepatic carcinoma (Fig. 4B). For example, a
previous study reported the loss of imprinting in IGF2, which
encodes a member of the insulin family of polypeptide growth
factors involved in development and growth, influencing liver
tumorigenesis (Haddad and Held, 1997). It has been reported that
CDKN1C is abnormally expressed in human hepatocarcinomas, but
without mutations (Bonilla et al., 1998; Schwienbacher et al., 2000),
indicating the potential roles of methylation difference in regulating
the differential expression of this gene between the liver and hepatic
carcinoma (Fig. 4D). The other DMGenes may also be associated
with the development of human hepatic carcinoma and further
analysis of these genes is necessary.
DISCUSSION
Genomic imprinting is a common epigenetic phenomenon in
mammals. Allele-specific expression and DNA methylation are two
well-known characteristics of genomic imprinting involved in
development and complex diseases. For promoting the application
and understanding of imprinted genes, we developed a comprehensive
cross-species database, MetaImprint, which is focused on the
systematic study of imprinted genes in the meta-view, including
genetics, epigenetics and functional genomes. The latest release of
MetaImprint incorporates 539 imprinted genes from eight species,
and related genome-wide genetic and epigenetic information,
including ICRs, SNPs, repetitive elements, non-coding RNAs,
DNA methylation and histone modifications, as well as human
imprinting disorders.
The erasing, building and maintaining of gene imprinting are
important in the normal development of embryos. Tissue and cell
specificity of imprinted genes have been investigated extensively
(Yamasaki-Ishizaki et al., 2007). Appropriate expression of imprinted
genes is important for normal development. The state transformation
of imprinted genes is associated with development, tumors, metabolic
diseases and congenital diseases. For example, Xin et al. found that
dynamic expression of imprinted genes is associated with maternally
controlled nutrient allocation during maize endosperm development
(Xin et al., 2013). However, abnormal imprinting may cause various
human disorders including Wilms’ tumor and other cancers (Chao and
D’Amore, 2008). The disease information also confirmed the
potential roles of imprinted genes in human diseases. Based on
the information for imprinted genes in this database, we provided an
example of its application for studying regulatory mechanisms and
potential roles in development and diseases. MetaImprint may be a
useful analytical tool to facilitate the systemic mining of related
features of imprinted genes. The in-depth study of genomic imprinting
facilitated by MetaImprint will be valuable for understanding disease
mechanisms, such as carcinogenesis.
Recently, high-throughput sequencing data, including single-cell
transcriptomes and MeDIP-based methylomes, have been generated
from a genome-wide perspective to study gene imprinting of different
tissue/cell/disease types. The re-evaluation of imprinted genes in
mammals using the cumulative research over the last few decades
should reveal hidden clues to the genomic homology of chromosome
imprinting regions. Going forward, many novel imprinted genes from
a variety of species and the information related to the imprinted genes
should accumulate much faster, which highlights the importance of a
comprehensive database such as MetaImprint. The allele-specific
expression of noncoding RNA, for instance AK003491 (Zeng et al.,
2013), has also been included in MetaImprint. The database is a useful
information repository for hosting further imprinted genes. As new
studies of imprinting are reported, MetaImprint will be updated
regularly. In future, more information on imprinted genes in different
mammals will be integrated into the database, which should help the
systematic analysis of the dynamics of gene imprinting during
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DEVELOPMENT
Fig. 4. Analysis of DNA methylation
difference of imprinted genes between
liver and hepatic carcinoma.
(A) Differentially methylated imprinted
genes (DMGene) in human liver and hepatic
carcinoma. (B) Imprinted genes related to
hepatic carcinoma in MetaImprint.
(C) Visualization of the DNA methylation and
genetic information in the differentially
methylated regions of imprinted gene
CDKN1C.
RESEARCH ARTICLE
MATERIALS AND METHODS
Funding
This work was supported in part by the National Natural Science Foundation of
China [61203262, 31371334, 31171383 and 31371478]; and the Science
Foundation of Heilongjiang Province [QC2011C061].
The collection of imprinted genes
Supplementary material
To mine the imprinted genes from the literature, we searched the PubMed
database using the keywords ‘imprint OR imprinted OR imprinting’ and
obtained 18,696 publications. We then scanned each publication and
removed 11,163 that were not gene-related studies. From the remaining
7533 publications, we manually excavated imprinted genes according to the
description of the imprinting state in each study. The imprinting state of each
imprinted gene was divided into four classes: (1) imprinted states (I): the
description of the gene’s imprinting states was mentioned as imprinted; (2)
not imprinted states (NI): the gene was not detected as imprinted by the
authors; (3) loss of imprint (LOI): the imprinting states were lost or
abnormal in multiple disorders, such as Prader–Willi syndrome (PWS) and
Angelman syndrome (AS); and (4) imprinted states by prediction (P): the
imprinting states of the genes were predicted by computational methods.
Supplementary material available online at
http://dev.biologists.org/lookup/suppl/doi:10.1242/dev.105320/-/DC1
The genetic and epigenetic annotation information
MetaImprint incorporated the genomic and epigenomic information related
to the imprinted genes from reference literatures and public databases. The
genetic annotation features (imprinted gene names, genomic location,
repetitive elements and chromosome bands data) were downloaded from the
UCSC Table Browser (Karolchik et al., 2004) and NCBI’s Entrez gene
(Maglott et al., 2011). The CpG islands of mammalian genomes were
identified by CpG_MI (Su et al., 2010) and downloaded from the UCSC
Table Browser. The SNP data were downloaded from The International
HapMap Consortium (2003) and the UCSC Table Browser. The ncRNA
data were obtained from NONCODE (Liu et al., 2005). More than 30 ICRs
for human and mouse were collected manually from the literature. KEGG
IDs (Kanehisa, 2002) and GO terms (Ashburner et al., 2000) were obtained
from their web servers or the UCSC Table Browser and preprocessed using a
custom Perl program. The high-throughput epigenetic data for DNA
methylation and histone modifications in multiple cell types of human and
mouse genomes were primarily collected from NCBI Epigenomics
(Fingerman et al., 2011), DiseaseMeth (Lv et al., 2012), Human Histone
Modification Database (HHMD) (Zhang et al., 2010), DevMouse (Liu et al.,
2014) and GEO (Barrett and Edgar, 2006). All these genetic and epigenetic
data are available to download from the ftp server of MetaImprint.
Information related to imprinting disorders
Literature mining was performed to meticulously collect the associations
between imprinted genes and approximately 70 human diseases
(supplementary material Table S1). These records could be valuable
references to analyze the potential function of disorganized imprinting in
diverse diseases.
Construction of imprinted genes and human disease network
Using the information regarding the relationships between human imprinted
genes and diseases in MetaImprint, we used Cytoscape (Shannon et al., 2003)
to build an imprinted genes and human disease network. In the human disease
network, the nodes are diseases and two diseases are linked if they share at least
one gene. In the imprinted genes network, the nodes are imprinted genes, and
two genes are linked if they participate in the same disease.
Acknowledgements
We acknowledge Shanshan Jia and Chunlong Zhang from our laboratory, and Jiang
Zhu from Harbin Medical University Daqing Campus for their comments.
Competing interests
The authors declare no competing financial interests.
Author contributions
Y.Z. and Q.W. developed the concepts. Y. Wei built the database. J.S. and Hongbo
Liu performed the data analysis. J.L., F.W., H.Y., Y. Wen and Hui Liu collected the
information on imprinted genes. Y. Wei, J.S. and Hongbo Liu prepared and edited
the manuscript.
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