Nucleic Acids Research Advance Access published online on November 11, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp948
© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
MBGD update 2010: toward a comprehensive resource for exploring microbial genome diversity
Ikuo Uchiyama1,*,
Toshio Higuchi2 and
Mikihiko Kawai1
1National Institute for Basic Biology, National Institutes of Natural Sciences, Nishigonaka 38, Myodaiji, Okazaki, Aichi 444-8585, Japan and 2Intec Systems Institute, Inc., 1-3-3 Shinsuna, Koto-ku, Tokyo 136-0075, Japan
*To whom correspondence should be addressed. Tel: +81 564 55 7629; Fax: +81 564 55 7625; Email: uchiyama{at}nibb.ac.jp
Received September 14, 2009. Revised October 9, 2009. Accepted October 12, 2009.
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ABSTRACT
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The microbial genome database (MBGD) for comparative analysis
is a platform for microbial comparative genomics based on automated
ortholog group identification. A prominent feature of MBGD is
that it allows users to create ortholog groups using a specified
subgroup of organisms. The database is constantly updated and
now contains almost 1000 genomes. To utilize the MBGD database
as a comprehensive resource for investigating microbial genome
diversity, we have developed the following advanced functionalities:
(i) enhanced assignment of functional annotation, including
external database links to each orthologous group, (ii) interface
for choosing a set of genomes to compare based on phenotypic
properties, (iii) the addition of more eukaryotic microbial
genomes (fungi and protists) and some higher eukaryotes as references
and (iv) enhancement of the MyMBGD mode, which allows users
to add their own genomes to MBGD and now accepts raw genomic
sequences without any annotation (in such a case, it runs a
gene-finding procedure before identifying the orthologs). Some
analysis functions, such as the function to find orthologs with
similar phylogenetic patterns, have also been improved. MBGD
is accessible at
http://mbgd.genome.ad.jp/.
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INTRODUCTION
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Nearly 1000 microbial genomes have been completely sequenced,
and the number of sequences is still growing exponentially.
The growth of this number will be even further accelerated by
the recent advancement of next-generation sequencing technologies.
Thanks to this vast amount of information, much progress has
been made in genomics studies toward understanding microbial
diversity. One of the promising approaches is the comparison
of dozens of closely related or moderately related genomes,
which is effective for analyzing critical differences among
organisms and understanding the evolutionary process generating
such diversity. Another important new advancement is the metagenomic
approach, by which researchers can investigate the community
structures of microbes and their gene contents in various environmental
samples. To facilitate genomic diversity studies, however, effective
utilization of the existing genomic data in terms of comparative
genomics is crucial, although this becomes more difficult as
the size of the genomic database increases.
MBGD (1,2) is a microbial genome database for large-scale comparative genomics based on comprehensive ortholog classification generated by a hierarchical clustering method, DomClust (3). As compared to other comparative genomics resources covering complete microbial genomes, such as CMR (4), MicrobesOnline (5), IMG (6), eggNOG (7) and OMA (8), a prominent feature of MBGD is that it allows users to create ortholog groups using a specified subgroup of organisms. This feature is useful for various types of comparative analysis, including comparisons among closely related as well as among distantly related organisms (1).
In addition to the flexible ortholog analysis functionality, we have recently enhanced the database content by incorporating various types of information regarding gene function and organism phenotype, adding more eukaryotic genomes and implementing several additional functionalities to facilitate large-scale comparative genome analysis. Here, we describe the recent enhancement of MBGD.
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DEFAULT ORTHOLOG TABLE
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Although one of the significant features of MBGD is that it
allows users to create their own ortholog tables by specifying
any set of organisms, MBGD also holds a precalculated ‘default
ortholog table’, which is now extended to cover all the
organisms stored in the database. Actually, the default ortholog
table is created using the default set of organisms that contains
one representative genome from each genus, but in the ‘extended’
table, genes of unselected genomes are also classified into
an appropriate ortholog group as follows: each gene is classified
into the ortholog group giving the best average similarity score
if (i) that score is better than the smallest within-cluster
score (i.e. the score assigned at the cluster root node) or
(ii) that gene is also the most similar to that ortholog group
in that genome (i.e. they are in a bidirectional best-hit relationship).
Based on this extended default ortholog table, users can now
access the ortholog cluster information from any gene information
page. Users can also download the entire default ortholog table
as a flat text file.
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ANNOTATION ASSIGNMENT TO EACH CLUSTER
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The ortholog cluster information page has been redesigned (
Figure 1). Several types of information are generated from the annotation
of its member genes and are added to this page as cluster annotation.
The following procedure determines the title of each ortholog
cluster: the occurrence of words in the title lines of the member
genes are counted, and the words whose occurrence is above or
equal to 30% of the most frequent words are retained as frequent
words; after scoring each title line based on the frequency
of frequent words, the cluster title is constructed by extracting
the frequent words from the best-scoring title.
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Figure 1. Ortholog cluster entry page displaying the orthologous group of the cobalamin biosynthetic gene, cobI, of the default cluster set. The page contains a cluster annotation table showing the gene name, title and cross-references to other databases, and a table showing the list of member genes.
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Each cluster entry also contains cross-references to the corresponding
entries of COGs, KEGG Orthology, TIGRFAMs and Gene Ontology
terms. A correspondence between an MBGD group and a group of
another database is classified into ‘equivalent’,
‘supergroup’ and ‘subgroup’, which are
defined based on the following set-comparison procedure: let
A and
B be the MBGD group and the other group, respectively,
containing only organisms commonly included in both sets, and
let
= |
A
B|/ |
A|, β = |
AB|/|
B| and
F = 2
β/(
+β);
we defined
B as being equivalent to
A if
F 
0.7 ; otherwise,
B is a supergroup of
A if
0.7 or a subgroup of
A if β
0.7. In these cases,
B is assigned as a cross-reference entry
of
A (
Figure 1)
.
As previously, each cluster entry is assigned functional categories, but the definition of functional category has been extended: in addition to the original definition (1), users can now choose a functional category system from among those defined in other databases (COG, KEGG and TIGR); category assignment to each cluster is based on a majority vote of categories assigned to individual genes referring to the cross-reference data.
In the cluster entry page, several comparison functions are available, such as multiple map comparison and multiple sequence alignment (Figure 1). In addition, a function to search for clusters with similar phylogenetic patterns is now available. Here, the cluster table is ordered according to the dissimilarity in phylogenetic pattern between each cluster and the original cluster (Figure 2), where the dissimilarities are calculated based on a correlation coefficient, the hamming distance or mutual information (9). This function is useful for predicting functional linkages (10) and similar functions are implemented in some more specialized databases (11,12). In MBGD, users can combine this type of analysis with more flexible ortholog analysis.
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Figure 2. Ortholog cluster table containing ortholog clusters with phylogenetic patterns similar to those of the cobI orthologs shown in Figure 1. The table is ordered by correlation coefficient against the phylogenetic pattern of the cobI ortholog group. The phylogenetic patterns are graphically represented by green bars indicating ‘present’. The value shown in the rightmost column is a dissimilarity value, d=(1 – r)/2, calculated from the correlation coefficient, r.
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ORGANISM SELECTION BASED ON PHENOTYPIC PROPERTIES
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The utilization of information on the phenotypic properties
of individual organisms is becoming more important for comparative
genomics studies as the number of genomes increases. The current
version of MBGD provides an interface for specifying a set of
organisms to be analyzed using phenotypic information as well
as taxonomic information as a reference, where the phenotypic
properties are taken from the organism metadata collection in
the GOLD database (
13) that includes cell shape, motility, oxygen
requirements, temperature range and so on (
Figure 3). Users
can also use a similar interface to specify a phylogenetic pattern
in order to search for orthologous groups having similar phylogenetic
patterns or specify species colors to interpret the phylogenetic
patterns displayed in the header of the ortholog table (
Figure 2).
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Figure 3. The interface for organism selection. In the right panel, users can specify the conditions on the organism properties taken from the GOLD database to filter the set of organisms. In this example, the condition on the temperature range is specified as ‘Either hyperthemophile or themophile’, and the organisms satisfying this condition are listed in the middle panel. Upon adding another condition on taxonomy (‘Choose one genome for each species’ at the top of the right panel), a further selection occurs, and the selected organisms are highlighted in the middle panel and added to the box in the left panel.
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ADDING MORE EUKARYOTIC GENOMES
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MBGD is periodically updated using the complete genome data
in the RefSeq database as a data source. Although previous MBGD
versions mainly contained prokaryotic genomes (except
Saccharomyces cerevisiae and
Schizosaccharomyces pombe for reference purposes),
we have now added more complete genome sequences of eukaryotic
microbes belonging to the fungi and protists such as
Plasmodium falciparum,
Dictyostelium discoideum,
Aspergillus nidulans and
Candida glabrata. In addition, the complete genome sequences
of some higher eukaryotes, including
Caenorhabditis elegans,
Drosophila melanogaster,
Arabidopsis thaliana and
Homo sapiens,
have also been added as a reference. To incorporate the eukaryotic
genomic data, we have modified our genome map viewer as well
as the database schema to correctly display eukaryotic genomes
where the genes are interrupted by introns.
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ENHANCEMENT OF THE MyMBGD FUNCTIONALITY
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The MyMBGD functionality, which allows users to add their own
genome sequences to MBGD, has been enhanced: we now provide
the ‘gene prediction mode’, in which users can submit
genomic nucleotide sequences without any annotation and ask
the system to predict the genes within them. The gene-finding
procedure implemented here uses both the GeneMarkS program (
14) and the Glimmer3 program (
15) and merges their outputs by
taking the longer region when two programs predict different
start positions in the same reading frame. The predicted genes
are then subjected to the DomClust procedure (
3) after an all-against-all
similarity search, which is the usual MyMBGD procedure described
previously (
2). MyMBGD also provides the ‘metagenome mode’,
which accepts a set of nucleotide or protein sequences from
a mixture of genomes and applies an ortholog assignment procedure
similar to that for extending the default ortholog table described
above, except for omitting the secondary condition for testing
the bidirectional best hit. With this enhancement, users can
now use the MyMBGD functionality as a tool to annotate a newly
sequenced genome or new metagenome data.
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FUNDING
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Institute for Bioinformatics Research and Development, Japan
Science and Technology Agency; Grant-in-Aid for Publication
of Scientific Research Results from Japan Society for the Promotion
of Science (218061). Funding for open access charge: Institute
for Bioinformatics Research and Development, Japan Science and
Technology Agency.
Conflict of interest statement. None declared.
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REFERENCES
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ABSTRACT
INTRODUCTION