Nucleic Acids Research, 2007, Vol. 35, Web Server issue
KAAS: an automatic genome annotation and pathway
Yuki Moriya, Masumi Itoh, Shujiro Okuda, Akiyasu C. Yoshizawa and Minoru Kanehisa*
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
Received January 30, 2007; Revised March 31, 2007; Accepted April 17, 2007
The number of complete and draft genomes is
rapidly growing in recent years, and it has become
increasingly important to automate the identifica-
tion of functional properties and biological roles of
genes in these genomes. In the KEGG database,
genes in complete genomes are annotated with the
KEGG orthology (KO) identifiers, or the K numbers,
based on the best hit information using Smith–
Waterman scores as well as by the manual curation.
Each K number represents an ortholog group of
genes, and it is directly linked to an object in the
KEGG pathway map or the BRITE functional hier-
archy. Here, we have developed a web-based server
called KAAS (KEGG Automatic Annotation Server:
http://www.genome.jp/kegg/kaas/) i.e. an imple-
mentation of a rapid method to automatically
assign K numbers to genes in the genome, enabling
reconstruction of KEGG pathways and BRITE hier-
archies. The method is based on sequence simila-
rities, bi-directional best hit information and some
heuristics, and has achieved a high degree of
accuracy when compared with the manually curated
KEGG GENES database.
In order to keep up with the rapid growth of sequence
data for complete and draft genomes, more efficient and
accurate computational methods are required for func-
tional annotation of these genomes. The basis for
functional annotation is application of sequence similarity
with well-annotated sequences. This is accomplished
by the sequence comparison methods such as the
Smith–Waterman algorithm (1), FASTA (2) and BLAST
(3,4). However, it is not always true that all similar
sequences have a conserved function. In previous works,
the relationship between the functional conservation and
the sequence similarity score was studied, and it was
suggested for enzymes that from 40 to 70% sequence
identity is necessary for functional prediction with 90%
accuracy (5,6). The availability of many sequenced
genomes has made the utilization of best hit information
possible, in addition to individual sequence similarity
scores. Orthologous genes are functionally conserved
genes in different species, branched from a common
ancestor by speciation. In practice, they are computation-
ally deduced from the bi-directional best hit (BBH)
Therefore, the identification of orthologous genes among
many species is the shortest way to predict functions of
newly sequenced genomes.
The accuracy of annotation largely depends on the
quality of the database to be searched. The Gene
Ontology (GO) has been developed for consistent
descriptions of gene products in different species (9). The
GO terms in the three ontologies, biological process,
cellular component and molecular function, are now used
in many genome databases. However, the GO annotations
for different species may not be easy to integrate because
they exist in different databases.
In contrast, the KEGG GENES database provides
a single resource for cross-species annotation of all
available genomes by a standardized mechanism, called
the KEGG Orthology (KO) system. The essence of the
KO system is that it is a pathway based definition of
orthologous genes. The KO entry represents an ortholog
group that is linked to a box (gene product) in the
KEGG pathway diagram. Thus, once the KO identifiers,
or the K numbers, are assigned to genes in the genome,
which is manually verified in KEGG, organism-specific
KO system has since been expanded to include the
BRITE functional hierarchies, such as hierarchical classi-
fications of protein families. A set of K numbers in the
genome can be mapped, i.e. to specific classes of receptors,
which may then be linked to specific classes of ligands in
the chemical category of the BRITE database (see Figure
1). In essence, the KEGG database provides a reference
*To whom Correspondence should be addressed. Tel: þ81 774 38 3270; Fax: þ81 774 38 3269; Email: email@example.com
? 2007 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/
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knowledge base for linking genomes to the biological
systems, and now to the environments as well (10).
Here, we report a web-based server called KAAS
(KEGG Automatic Annotation Server) to automate
the processes of the K number assignment and the
subsequent pathway mapping and BRITE mapping.
Figure 2 shows a flow chart of KAAS. First, the
BLAST scores between a query sequence and the
reference sequence set (taken from the KEGG GENES
database) are computed, and homologs are found in
the reference set. Next, homologs ranked above the
threshold are selected as ortholog candidates based
on the BLAST score and the bi-directional hit rate
(BHR) defined below. Ortholog candidates are divided
into KO groups according to the annotation of the
KEGG GENES database.
score is calculated based on the likelihood and heuristics
Figure 1. An example of the genome annotation with the KO identifiers or the K numbers by the KAAS service, which is integrated into the KEGG
resource. Once the KAAS assigns K numbers to query genes, the mapping to KEGG pathways and BRITE hierchies is generated using the existing
framework of the KEGG system.
Figure 2. The overall procedure of KAAS.
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for each KO group. Then, the K number of the KO group
with the highest score is assigned to the query sequence.
Bi-directional hit rate
Given a genome to be annotated, it is compared against
each genome in the reference set of the KEGG GENES
database by BLAST searches in both forward and reverse
directions, taking each gene in genome A as a query
compared against all genes in genome B, and vice versa.
Those BLAST hits with bit scores less than 60 are
removed. Because the bit scores of a gene pair a and b
from two genomes A and B, respectively, can be different
in forward and reverse directions, and because the top
scores do not necessarily reflect the order of the rigorous
Smith–Waterman scores, we define the BHR as:
BHR ¼ Rf? Rr:
Here, R¼S0/Sbwhere S0is the bit score of a against b,
and Sbis the score of a against the best-hit gene in genome
B (which may not necessarily be b). Rfrefers to the score
from the forward BLAST (A against B), and Rrrefers to
the score from the reverse BLAST (B against A). We select
those genes whose BHR is greater than 0.95.
We define a score for each ortholog group in order to
assign the best fitting K numbers to the query gene:
SKO¼ Sh? log2ðmnÞ ? log2
xCkpkð1 ? pÞx?k
where Sh is the highest score among all ortholog
candidates in the ortholog group, m and n are the
sequence lengths of the query and the target of BLAST,
respectively, N is the number of ortholog group members,
x is the number of organisms in the original ortholog
group from which this group is derived, and p is the
ratio of the size of the original ortholog group versus the
size of the entire GENES database. The second term is
for the normalization of the first term by sequence lengths,
and the third term is a weighting factor to consider
the number of ortholog candidates that are found in the
On this server, the user can input the FASTA formatted
ORFs or ESTs. The expected query data is amino acid
sequences representing a set of protein-coding genes in a
complete genome to annotate with high accuracy. In that
case, KO assignments are based on the results of
BLASTP. Check the ‘Nucleotide’ checkbox if queries are
nucleotide sequences representing a set of EST contigs or
ESTs. In this case, KO assignments are based on the
results of BLASTX and TBLASTN.
Reference data selection
The user can choose the reference data set from the latest
KEGG GENES entries. As of the end of December 2006,
it contains 469 organisms (36 eukaryotes, 402 bacteria and
31 archaea) with KO annotation. The computation time is
proportional to the size of the data set. The accuracy will
be improved if closely related species of the query are
contained in the data set. A representative set is set out on
the server as the default. It is a pre-selected data set of
species from each taxonomic group in KEGG GENES to
reduce the computation time without drastic lowering of
accuracy. The representative set for eukaryotes includes
15 eukaryotes and 11 prokaryotes, which is roughly one-
seventh of the whole set, and the representative set for
prokaryotes includes 5 eukaryotes and 23 prokaryotes,
approximately one-tenth of whole set.
The KAAS is implemented using two methods: the
bi-directional best hit (BBH) information method, and
the single-directional best hit information method (SBH
method). A complete set of genes in a genome is preferable
as the query because the KAAS works best with BBH
method. On the other hand, the SBH method can also be
used for a limited number of ORFs or ESTs. The
computation time of the BBH method is about twice
that of SBH.
The KAAS provides three views of the analyzed data.
‘KO list’ is the flat list of query genes with the K numbers
given by the KAAS. ‘KO hierarchy’ is the hierarchical list
of annotated genes, which is categorized according to the
BRITE database. ‘Pathway map’ is the list of pathways
with links to graphical pathway maps. The annotated
query genes are highlighted in the maps. Each box in the
map is linked to functional information in the KO
database. The user can re-compute the KO annotation
with a different BLAST threshold from ‘threshold change’
option. ‘Download’ is an option to download the text file
of KO annotation and reconstructed graphical pathway
In the case of a prokaryotic genome that contains about
4000 amino acid sequences, the computation of KAAS
with the reference data set for prokaryotes takes ?1h.
RESULT AND DISCUSSION
To test the accuracy, we reassigned K numbers to selected
organisms in the manually curated KEGG GENES
Escherichia coli, where 25.2, 11.6, 32.1 and 63.3% of the
K numbers. Tables 1 and 2 list the sensitivity, specificity,
positive predictive value (PPV), and precision of selected
organisms with BBH method. The whole set of KEGG
Nucleic Acids Research, 2007, Vol. 35, WebServer issue
GENES and representative set excluding the query Download full-text
genome itself were respectively referred to for Tables 1
As a result of annotation with the whole set of GENES,
the PPV of human gene reassignment was more than 90%.
When the test set was limited to the genes with KO
annotations, 98% of genes in human were correctly
annotated. For E. coli, the accuracy of the reassignment
is higher than that of human, because the KEGG GENES
database contains many closely related organisms of
E. coli. The PPV of Arabidopsis is ?50%, because there
are no plants in the KEGG GENES database and many
genes of Arabidopsis are left unannotated. Because the
KO is not developed based on only the sequence
similarity, there is the case that some KOs contain similar
members. In that situation, the KAAS may not assign
appropriate KOs to genes.
In the case of using the representative set, the genes
were annotated without a drastic lowering of accuracy
compared with the whole set. The computation time for
E. coli takes about one-tenth of the whole set and selected
eukaryotes take about one-seventh. For human and yeast,
the accuracy of annotation was equal to or slightly better
than that with the whole set of KEGG GENES. For
Arabidopsis, the accuracy of annotation went down
because the number of related organisms contained in
the reference data was reduced. The sensitivity for E. coli
went down because the representative set for prokaryotes
excludes closely related organisms. The KAAS is useful as
a rapid and high performance tool for forthcoming
genome annotation because many taxa referred to as
closely related organisms are now contained in the KEGG
GENES database. For plants the accuracy of assignment
will improve, as more plant genome projects are being
This work was supported by grants from the Ministry of
Education, Culture, Sports, Science and Technology,
and the JapanScience
Bioinformatics Center, Institute for Chemical Research,
Kyoto University. Funding to pay the Open Access
publication charges for the article was provided by the
grant-in-aid for scientific research from the Ministry of
Conflict of interest statment. None declared.
1. Smith,T. and Waterman,M. (1981) Identification of common
molecular subsequences. J. Mol. Biol., 147, 195–197.
2. Lipman,D. and Pearson,W. (1985) Rapid and sensitive protein
similarity searches. Science, 227, 1435–1441.
3. Altschul,S., Gish,W., Miller,W., Myers,E. and Lipman,D. (1990)
Basic local alignment search tool. J. Mol. Biol., 215, 403–410.
4. Altschul,S.F., Madden,T.L., Schaffer,A.A., Zhang,J., Zhang,Z.,
Miller,W. and Lipman,D.J. (1997) Gapped BLAST and PSI-
BLAST: a new generation of protein database search programs.
Nucleic Acids Res., 25, 3389–3402.
5. Rost,B. (2002) Enzyme Function Less Conserved than Anticipated.
J. Mol. Biol., 318, 595–608.
6. Tian,W. and Skolnick,J. (2003) How well is enzyme function and
conserved as a function of pairwise sequence identity? J. Mol. Biol.,
7. Tatusov,R., Koonin,E. and Limpan,D. (1997) A genomic perspec-
tive on protein families. Science, 278, 631–637.
8. Tatusov,R., Natale,D., Garkavtsev,I., Tatusova,T.,
Shankavaram,U., Rao,B., Kiryutin,B., Galperin,M., Fedorova,N
et al. (2001) The COG database: new developments in phylogenetic
classification of proteins from complete genomes.
Nucleic Acids Res., 29, 22–28.
9. Ashburner,M., Ball,C.A., Blake,J.A., Botstein,D., Butler,H.,
Cherry,J.M., Davis,A.P., Dolinski,K., Dwight,S.S. et al. (2000)
Gene ontology: tool for the unification of biology. The Gene
Ontology Consortium. Nat. Genet., 25, 25–29.
10. Kanehisa,M., Goto,S., Hattori,M., Aoki-Kinoshita,K.F., Itoh,M.,
Kawashima,S., Katayama,T., Araki,M. and Hirakawa,M. (2006)
From genomics to chemical genomics: new developments in KEGG.
Nucleic Acids Res., 34, D354–D357.
Table 1. Accuracy of K number assignment by KAAS with the BBH
method and the whole set of KEGG GENES
Species H. sapiens A. thaliana S. cerevisiaeE. coli
Sensitivity is the rate of the true positives to all genes with KO
annotations. Specificity is the rate of the true negatives to all genes
without KO annotations. PPV is the rate of true positives to all
positives for all genes in each organism. Precision means the rate of
correctly annotated genes if the test set is limited to the genes with KO
Table 2. Accuracy of K number assignment KAAS with the BBH
method and the representative set
SpeciesH. sapiensA. thalianaS. cerevisiaeE. coli
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