Systematic Classi cation of B: subtilis Genes andConstruction of a Knowledge Base to Represent
Functional Information
Atsushi Ogiwara
ogi@nibb.ac.jp

National Institute for Basic Biology
38 Nishigounaka, Myoudaiji, Okazaki 444-8585, Japan

1 Introduction
is a typical Gram-positive bacterium whose whole genomic sequence was determined.
Before the genome sequencing was completed, we had constructed a B. subtilis genome databasecalled BSORF [2]. This was not only a database of sequences, but also to store as many functionalannotations as possible. Since the complete genome sequence of Bacillus subtilis had been determined[1], we also tried to classify whole ORF products in the viewpoint of functional categories. We started
with the clustering of paralogous genes [3], and using the result of paralog clustering, we annotatedtransporter genes using other features like motifs and operon structures. To rearrange the resulting
annotation, I constructed a knowledge base system using Java language. Since the main focus of B.
subtilis and other microbial genome projects was shifted to the functional analysis, genome-informatics
should also treat the functional information. Here I'll present a framework for functional information
representation that is suitable for WWW.

Bacills subtilis

2 Classi cation of transporter genes
As reported previously [1], there seemed to exist 4,100 ORFs in Bacillus subtilis strain 168 genome.
Paralog clustering revealed that about a half of them consisted of paralogues. The largest paralogous
group, which contained 79 ORFs, corresponded to the ATP binding subunit of the ATP-binding
cassette (ABC) transporters. Thus, to categorize the whole ORFs systematically, we started withclassifying the transporter genes.
2.1

ABC transportersThe ABC transporter is known to consist of some components. (1) ATP-binding subunit, (2) membrane spanning subunit, and in some cases, (3) substrate speci c binding subunit. At least, twoATP-binding subunits and two membrane spanning subunits are required. Each component consistsof either homo-dimer or hetero-dimer. In the case of B. subtilis, genes of these components tend
to constitute operons. i.e., these seem to be co-regulated on transcription. There are some otherfeatures: ATP-binding subunit having a speci c ATP binding motif, and membrane spanning subunit having multiple membrane spanning features. Thus, by combining these features, I classi ed
79 candidate ATP-binding subunits of ABC transporters into 5 major groups. These groups seemto correspond fairly well to the substrate speci city, judging from the annotation of orthologues. Inaddition, sub-clustering of these ATP-binding subunits suggested that there seemed to be strong relationship between sequence homology and substrate speci city. It may be interpreted to re ect thehistory of paralogous evolution.
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2.2

Phosphotransferase systems

Phosphotransferase system (PTS) consists of 3 components: a type I enzyme that transfers a phosphategroup from phosphoenol pyruvate to an HPr component, and a type II enzyme that accepts thephosphate group from HPr, imports certain sugar and phosphorylates it. In the case of B. subtilis,
operon structures are diversed especially for type II enzymes. There are 4 component in type IIenzymes, but at least, component B and C seem to be essential. Though some PTS speci c motifs
and transmembrane features are observed, it was dicult to utilize the information of operon structure
to discriminate the PTS system.
2.3

Multidrug resistance families

Many drug resistance family homologues were observed in B. subtilis. Some of them were ABC typetransporters, like eucaryotic mdr. Other kinds of multidrug resistance family, like major facilitator
superfamily (MFS) and small multidrug resistance family (SMR) were found. However, no speci cmotifs nor operon structure were observed in these families. Thus, I used only homology informationand membrane spanning features to discriminate these classes.

3 Knowledge base system by Java language
To rearrange these results, I made a knowledge base system not only to store each fact, but also torepresent the feature of certain function. I adopted Java language for knowledge representation. Since
Java is a kind of object-oriented language, it is reasonable to represent a functional category as a class,and each genes as instances. Since Java was originally designed to construct a Web content, it is quite
easy to construct a system which is suitable for the Web presentation. In the strict sense of the wordof \knowledge base", the system might be inadequate to call the knowledge base because there is nogeneral mechanism to achieve rule-based reasoning. But I implemented some \reasoning" mechanism
in a class-dependent manner. For example, we can discriminate whether an operon belongs to theABC transporter family or not.

Acknowledgements
The author thanks Professor Naotake Ogasawara in Nara Institute of Science and Technology for valuable suggestions and discussions. Sta s of the Human Genome Center, especially Ms Mari Watanabe
assisted in constructing the database. This work was supported by a Grant-in-Aid for Scienti c
Research on Priority Area \Genome Science" from the Ministry of Education, Science, Sports andCulture of Japan. The computational time was provided by the Human Genome Center, Institute ofMedical Science, the University of Tokyo, and by the Supercomputer laboratory, Institute for Chemical
Research, Kyoto University.

References[1] Kunst, F. et al., The complete genome sequence of the Gram-positive bacterium Bacillus subtilis,
Nature, 390:249{256, 1997.
[2] Ogiwara, A., Ogasawara, N., Watanabe, M., and Takagi, T., Construction of the Bacillus subtilis
ORF database (BSORF DB), Genome Informatics 1996, Universal Academy Press, 228{229,1996.
[3] Ogiwara, A., Ogasawara, N., Watanabe, M., and Takagi, T., Comprehensive Sequence Analysis ofB. subtilis Genome Using the BSORF Database, Genome Informatics 1997, Universal AcademyPress, 320{321, 1997.
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