Detection of Co-regulated Genes by Comparative
Analysis of Microbial Genomes

Hiroyuki Ogata

Wataru Fujibuchi

ogata@kuicr.kyoto-u.ac.jp

Minoru Kanehisa

wataru@kuicr.kyoto-u.ac.jp

kanehisa@kuicr.kyoto-u.ac.jp

Institute for Chemical Research, Kyoto University
Gokasho, Uji, Kyoto 611-0011, Japan

1 Introduction
Rapid accumulation of genomic sequences is giving us a new opportunity to investigate and understandintricate biological systems, which may be represented as networks of genes and gene products. Several
new experimental methods such as DNA microarray technology would be utilized to produce a hugeamount of expression pro les and to determine correlated expression of genes in a wide variety ofcells. The informatics based approaches should take large parts in this research area of functional
genomics. Especially computational techniques to predict co-regulated genes must be developed,
since they provide useful information for designing the experiments and for interpreting the correlated
expression of genes. It is well known that, in some bacteria and also in archaea, several genes withfunctional links are often clustered on the genomes. Such a clustering of genes implies commonregulation of genes, for example, by the mechanism of polycistronic transcription. In some cases,
multiple transcripts carrying related functions are co-regulated by common factors. In this study, we
propose a new technique to detect possible functional links between genes that are not necessarily
clustered in the genome.

2 Materials and Methods
Thus far we have constructed the gene cluster database (GCDB). To construct the GCDB, we took
two kinds of strategies. Firstly, conserved gene clusters were extracted by interspecies comparisons oflocalization of orthologous genes. Secondly, clusters of genes that are functionally related in metabolic
pathways were extracted by the network comparison technique [1].
Here we introduce an index to measure the degree of cluster formation (DC F ) for arbitrary pairsof genes in the genome. The DC F is based on the information of gene pairs that appear in the samegene clusters in the GCDB. Consider a pair of genes, a0 and b0 in a genome G0 , which is denoted by
P0 , and their orthologs a and b in another genome G (i = 1:::n 0 1; n is the number of organismsconsidered). If a and b are in the same cluster according to the GCDB, they are de ned as a clusteredpair C . If an appropriate measure is given to estimate the distances between P0 and C , andbetween C and C , DC F is de ned as the following equation:a;biiiiia;ba;bia;bia;ba;bij

DC F=

X (a;bDis P0;Ca;bi)+i

X (Dis Ca;bi;Ca;bj):i;jIn this study, we employed the distance between small subunit rRNA sequences as the distance measurein the equation.

304

(a)
54
3
2
10
1

5

9

adjacent gene pairs within operons

2125

29

adjacent gene pairs between operons

17

DCF

13305

(b)
b4006
b4005
b2938

b0336
b0337

b0945

b0522
b0523

b1062

b2557

b1131

b2312

b2311

b1658

b2553

b2313

b2551

b2314

b1207

b2476

b2508
b2507
b2500
b2499

[1] Ogata, H., Goto, S., Fujibuchi, W. and Kanehisa, M., Computation with the KEGG pathway
database, BioSystems, 47:119{128, 1998.

References

This work was supported in part by the Grant-in-Aid for Scienti c Research on the Priority Areas`Genome Science' from the Ministry of Education, Science, Sports and Culture in Japan. The computation time was provided by the Supercomputer Laboratory, Institute for Chemical Research, Kyoto
University.

Acknowledgements

We used the GCDB constructed from the genomic data of fteen organisms including ten bacteria
and ve archaea. In order to see the correlation between DC F and operon organization, we plotted
the distribution of DC F values for adjacent pairs of genes both for those within operons and for thoseacross operons by using the E. coli operon data (Fig. 1 (a)). It is clearly shown that the adjacentgene pairs with non-zero DC F values are more frequent within operons than across operons (Pairswith DC F = 0 are not shown). However, at the moment, the operon prediction utilizing DC F did
not improve the prediction based only on the intergenic distances (the performance was about 74%).
We expect that the performance could be improved with the increasing number of organisms used forconstruction of the GCDB and/or with the optimization of parameters.
The relation of DC F values with regulon organization must be another important issue. In Fig. 1(b), relatedness by DC F values among 24 genes that are separate in the genome are represented.
These genes are relevant to purine nucleotide synthesis and known to be co-regulated as a regulon.
Interestingly most genes are tightly connected by lines representing DC F values larger than 0. We
will further study the ecient use of DC F for detecting co-regulated genes, especially regulons, bycombining with additional information of gene expression pro les.

3 Results and DiscussionFigure 1: (a) Relative frequency of gene pairs that are adjacent along the E. coli genome is plottedagainst their DC F values. Filled bars for the adjacent gene pairs within operons, and open bars forthe pairs across operons. (b) Pairs of genes with DC F > 0 among 24 co-regulated genes (black boxes)
are connected by thin lines. Thick lines indicate operon organizations.

Frequence of Pairs (%)