National Institute for Basic Biology

Theoretical Biology

LABORATORY OF GENOME INFORMATICS

Associate Professor

UCHIYAMA, Ikuo

The accumulation of biological data has recently been
accelerated by various high-throughput “omics” technologies including genomics, transcriptomics, and proteomics.
The field of genome informatics is aimed at utilizing thisdata, or finding the principles behind it, in order to understand complex living systems by integrating the data withcurrent biological knowledge via the use of various computational techniques. In this laboratory we focus on developing
computational methods and tools for comparative genome
analysis, which is a useful approach for finding functionalor evolutionary clues to interpret the genomic information ofvarious species.
The current focus of our research is the comparative
analysis of microbial genomes, the number of which has
been increasing rapidly. Since different types of information
about biological functions and evolutionary processes canbe extracted by comparing genomes at different evolutionary distances, we are developing methods to conduct comparative analyses not only of distantly related but also closelyrelated genomes.

I. Microbial genome database for comparativeanalysis

The microbial genome database for comparative analysis(MBGD, https://mbgd.genome.ad.jp/) is a comprehensive
platform for microbial comparative genomics. The central
function of MBGD is to create orthologous groups among
multiple genomes using the DomClust program combinedwith the DomRefine program. Through the application ofthese programs, MBGD not only provides comprehensive
orthologous groups among the latest genomic data, but also
allows users to create their own ortholog tables on the fly
by using a specified set of organisms. MBGD also has precalculated ortholog tables for each major taxonomic group,and provides several viewing modes to display the entiretyof each ortholog table. For closely related taxa, MBGD
provides conserved synteny information calculated using
the CoreAligner program. MBGD additionally provides a‘MyMBGD’ mode, which allows users to add their own
genomes to MBGD.
We continue to update the database and MBGD now
contains 15397 genomes, including 14786 bacteria, 336
archaea, and 275 eukaryota. These data sets are classified
based on the hierarchical ortholog classification strategy
described in the section below. In addition, in the latest
version of MBGD, we improved several functionalities
including a new display of the core genome alignment(Figure 1) and a novel interface for the MyMBGD mode.
As an application software for analyzing novel genome
sequences based on the database, we have developed atool to predict the functional potential of novel microbial genomes through orthology assignment based on theMBGD ortholog table. To evaluate the metabolic potentialof the query genome from this assignment, we utilized theGenomaple software to calculate the module completion
ratio for each KEGG Module entry (in collaboration with Dr.
Takami, Univ Tokyo). The result is displayed on a module
completion table where a user can compare the presence orabsence of functional modules among specified organisms.

II. Hierarchical strategy for creating ortholog
tables

MBGD previously calculated all-against-all similarities
among the stored genomes and independently created twotypes of ortholog tables: the standard ortholog table containing one representative genome from each genus covering
the entire taxonomic range, and the taxon specific ortholog
tables containing the genomes belonging to each taxonomic
group (species, genus, family and so on).
To create more comprehensive ortholog classification,
we developed a stepwise protocol to construct orthologous relationships. First, for each species with at least two
genomes, all-against-all similarities among the genomes
belonging to that species are calculated and a within-species
ortholog table is created. The species-level pan-genome isthen created by picking one representative gene from eachorthologous group. Next, for each genus with at least twospecies, all-against-all similarities among the species-level
pan-genomes are calculated and a within-genus ortholog
table is created. The genus-level pan-genome is then createdby picking one representative gene from each orthologous
group. Finally, all-against-all similarities among the genuslevel pan-genomes are calculated and the standard ortholog
table covering the entire taxonomic range is created.

III. O rthologous gene classification among
multiple genomes at the domain level

Figure 1. Comparative genome map among the family Enterobacteriaceae
on the basis of the core genome alignment constructed using a novel implementation of the CoreAligner program. The color is assigned according tothe gene order in Escherichia coli.

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As a core technology of our comparative genomics tools,
we have developed a rapid automated method of ortholog
grouping, named DomClust, which allows us to simultaneously compare numerous genomes. This method classifies genes based on a hierarchical clustering algorithm
using all-against-all similarity data as an input, but it also

detects domain fusion or fission events and splits clusters
into domains when required. As a result, the procedure cansplit genes into the domains minimally required for orthologgrouping.
We have also developed a procedure to refine the DomClustclassification based on multiple sequence alignments insteadof pairwise sequence alignments. The method is based ona scoring scheme, the domain-specific sum-of-pairs (DSP)
score, which evaluates domain-level classification using thesum total of domain-level alignment scores. On the basisof this idea, we have developed a refinement pipeline toimprove domain-level clustering, DomRefine, by optimizing DSP scores. DomRefine is used to construct the standard
ortholog table covering all the representative genomes storedin MBGD.
Domain-level classification is a unique feature within ourortholog classification system. In particular, this data is
considered suitable for analyzing domain fusion events thathave occurred during evolution. By using the domain-level
ortholog grouping data combined with taxonomic and functional information, we are trying to elucidate when and inwhat kind of genes domain fusion events frequently occur
during evolution, and how the complexity of the “domainfusion network” can be associated with the phenotypic traits
in each organism.

IV. D evelopment and application of aworkbench for comparative genomics andtranscriptomics

We have developed a comparative genomics workbench
named RECOG (Research Environment for COmparativeGenomics), which aims to extend the current MBGD system
by incorporating more advanced analysis functionalities.
The central function of RECOG is to display and manipulate large-scale ortholog tables. The ortholog table viewer
is a spreadsheet like viewer that can display an entire
ortholog table containing more than a thousand genomes. InRECOG, several basic operations on the ortholog table aredefined, which are classified into categories such as filtering,sorting, and coloring. By combining these basic operations,various comparative analyses can be performed. In addition,
RECOG allows the user to input arbitrary gene properties
and compare these properties among orthologs in variousgenomes.
We are continuing to develop the system and apply it
to various genome comparison studies as part of various
collaborative research projects. Among these, we applied
RECOG to the comparative analyses of transcriptomic dataof Chattonella antiqua and other harmful algae causing red
tide in collaboration with Dr. Shikata (FRA). In this analysis,
we compared five RNA-seq datasets of harmful algae and
10 existing genome sequences of various algae and a plantmodel organism, Arabidopsis thaliana using RECOG, andidentified common sequence features among orthologous
genes belonging to harmful algae that may be related to redtide outbreaks and their toxicity to fish. The resulting data isavailable through the database DB-HABs (https://hab.nibb.
ac.jp).

V. O rtholog data representation using theSemantic Web technology to integratevarious microbial databases

Orthology is a key to integrating knowledge of variousorganisms through comparative analysis. In order to integrate
genomic data and various types of biological information
with this idea, we have constructed an ortholog databaseusing Semantic Web technology. To formalize the structureof the ortholog information in the Semantic Web, we developed the Orthology Ontology (ORTH) and described theortholog information in MBGD in the form of the Resource
Description Framework (RDF).
On the basis of this framework, we have integrated various
kinds of microbial data using the ortholog information as ahub, as part of the MicrobeDB.jp project (http://microbedb.
jp/) under the auspices of the National Bioscience Database
Center.

VI. A novel approach for identification ofgenomic islands
Genomes of bacterial species can show great variation in
their gene content, and thus systematic analysis of the entire
gene repertoire, termed the “pan-genome”, is important forunderstanding bacterial intra-species diversity. As we havealready developed a procedure (CoreAligner) to define thecore genome as the genes conserved among the genomesof the given species, we are now focusing on the remaining part of the genomes (non-core genomes) that is more
directly linked to the within-species genome diversity. We
are developing a method (FindIsland) to identify a set of
non-core genes that have conserved gene order by using amodified version of the CoreAligner algorithm. We applied
the method to the sets of genomes of prokaryotic species
stored in MBGD and developed a database for analyzing
their non-core genomes. Based on the database, we foundthat the resulting conserved clusters frequently correspondto known mobile genetic elements and/or have sequence
features common to known genomic islands.

Publication List:〔Original Papers〕

M., Nishide, H., Nagao, R., Inoue-Kashino, N., Shen, J.-R.,• Kumazawa,Nakano, T., Uchiyama, I., Kashino, Y., and Ifuku, K. (2022). Molecular•phylogeny of fucoxanthin-chlorophyll a/c proteins from Chaetoceros
gracilis and Lhcq/Lhcf diversity. Physiol. Plant. 174, e13598. DOI:
10.1111/ppl.13598
Okubo, T., Toyoda, A., Fukuhara, K., Uchiyama, I., Harigaya, Y.,
Kuroiwa, M., Suzuki, T., Murakami, Y., Suwa, Y., and Takami, H.
(2021). The physiological potential of anammox bacteria as revealedby their core genome structure. DNA Res. 28, dsaa028. DOI: 10.1093/dnares/dsaa028〔Review Article〕

B., Ebersberger, I., McGlynn, S.E., Glover, N., Mochizuki, T.,• Linard,
Patricio, M., Lecompte, O., Nevers, Y., QFO Consortium, Thomas,P.D., Gabaldon, T., Sonnhammer, E., Dessimoz, C., and Uchiyama, I.
(2021). Ten Years of Collaborative Progress in the Quest for Orthologs.
Mol. Biol. Evol. 38, 3033–3045. DOI: 10.1093/molbev/msab098

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