Nucleic Acids Research 2004 32(Web Server Issue):W336-W339; doi:10.1093/nar/gkh365
© 2004, the authors
Nucleic Acids Research, Vol. 32, Web Server issue © Oxford University Press 2004; all rights reserved
Phydbac2: improved inference of gene function using interactive phylogenomic profiling and chromosomal location analysis
François Enault*,
Karsten Suhre,
Olivier Poirot,
Chantal Abergel and
Jean-Michel Claverie
Information Génomique & Structurale (UPR CNRS 2589), Institut de Biologie Structurale et Microbiologie, 31, chemin Joseph Aiguier, 13402 Marseille Cedex 20, France
* To whom correspondence should be addressed. Tel: +33491164548; Fax: +33491164549; Email: enault{at}igs.cnrs-mrs.fr
Received February 14, 2004; Accepted March 4, 2004
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ABSTRACT
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Phydbac (phylogenomic display of bacterial genes) implemented
a method of phylogenomic profiling using a distance measure
based on normalized BLAST scores. This method was able to increase
the predictive power of phylogenomic profiling by about 25%
when compared to the classical approach based on Hamming distances.
Here we present a major extension of Phydbac (named here Phydbac2),
that extends both the concept and the functionality of the original
web-service. While phylogenomic profiles remain the central
focus of Phydbac2, it now integrates chromosomal proximity and
gene fusion analyses as two additional non-similarity-based
indicators for inferring pairwise gene functional relationships.
Moreover, all presently available (January 2004) fully sequenced
bacterial genomes and those of three lower eukaryotes are now
included in the profiling process, thus increasing the initial
number of reference genomes (71 in Phydbac) to 150 in Phydbac2.
Using the KEGG metabolic pathway database as a benchmark, we
show that the predictive power of Phydbac2 is improved by 27%
over the previous version. This gain is accounted for on one
hand, by the increased number of reference genomes (11%) and
on the other hand, as a result of including chromosomal proximity
into the distance measure (16%). The expanded functionality
of Phydbac2 now allows the user to query more than 50 different
genomes, including at least one member of each major bacterial
group, most major pathogens and potential bio-terrorism agents.
The search for co-evolving genes based on consensus profiles
from multiple organisms, the display of Phydbac2 profiles side
by side with COG information, the inclusion of KEGG metabolic
pathway maps the production of chromosomal proximity maps, and
the possibility of collecting and processing results from different
Phydbac queries in a common shopping cart are the main new features
of Phydbac2. The Phydbac2 web server is available at
http://igs-server.cnrs-mrs.fr/phydbac/.
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INTRODUCTION
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Three major non-similarity-based methods for the prediction
of bacterial pairwise gene relationships have been proposed
in the past (
1–
6) and are now widely established. These
methods are based on (i) the co-evolution of functionally related
genes (phylogenomic profiles), (ii) the conservation of chromosomal
proximity (operon-like structures) and (iii) the detection of
gene fusion events (Rosetta-stones). A number of web-servers
are presently available that implement these methods at different
levels of detail, such as STRING (
7). The primary data on gene-absence/presence
used by STRING is derived from the well-established ‘Cluster
of Orthologous Groups’ (COG) database (
8). We previously
introduced the web server Phydbac (phylogenomic display of bacterial
genes) (
9) implementing a new method of phylogenomic profiling
based on normalized BLAST (
10) scores rather than on the simple
presence or absence of a given gene in a genome (as for instance
in STRING). This approach was shown to improve the predictive
power of phylogenomic profiling by about 25% with respect to
the classical approach (
11). However, the original Phydbac was
limited to queries for genes from
Escherichia coli and its predictions
solely based on phylogenomic profiling. Here we describe Phydbac2,
which removes these limitations by allowing 50 genomes to be
queried and integrates chromosomal proximity and gene-fusion
analysis to generate improved functional predictions. Numerous
additional new features (described below) were also implemented
to make Phydbac2 an even more useful tool for the inference
of bacterial gene function.
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NEW FEATURES IN PHYDBAC2
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The concept of
Consensus Phylogenomic Profiles (
CPP) is a major
addition to the original Phydbac. The CPP is automatically computed
from the displayed individual gene profiles by simply averaging
the scores in each column where at least half of the selected
genes show a significant similarity (
Figure 1). These profiles
can be generated from a query gene and its co-evolving neighbors,
or from the genes involved in a predefined KEGG pathway (
12).
The CPP can then be used as a query, in which all selected genes
contribute equally. Such a search will identify target genes
exhibiting a co-evolution relationship to the whole pathway,
rather than to a specific gene.
A different type of CPP is also generated by clicking on the
individual phylogenomic profile of any given gene. This will
pop out the profiles (and the CPP) for the corresponding genes
(putative orthologs) in all other bacterial genomes. This CPP
now has the advantage of being independent of the starting genome.
Such an ortholog-average CPP usually amplifies the noisy phylogenomic
signal obtained when the similarity between the query gene and
its best match in other genomes is low. It often leads to the
identification of more functionally coherent neighborhoods than
profiles computed on individual genes.
The analysis of conserved gene to gene chromosomal proximity is now implemented. Co-localized genes were initially detected by distributing the 150 bacterial genomes (including multiple strains of the same bacteria or evolutionary close species) into 87 groups (clusters of very similar organisms) on the basis of the multiple alignments of the 150 homologs of three genes. Two genes were then classified as ‘co-localized’ when their most similar sequences were found at a distance <2000 bp in three (or more) genomes of the 87 groups. For all the genes satisfying this constraint, the chromosome regions of the relevant organisms can be displayed, with each of the regions anchored on the position of the gene for which the map was requested. The positions of the genes found to co-localize with the query are highlighted in different colors (Figure 1).
Gene fusion events are derived from the FusionDB database (13) and are flagged in the Phydbac2 output for all genes in a phylogenomic neighborhood that exhibits Rosetta-stone sequences in one or several genomes. The Phydbac2 output now provides active links to the detailed analyses of these fusion events, including multiple alignments and phylogenetic trees. The ‘reality’ of a putative gene fusion event can thus be checked interactively.
The improved predictive power of Phydbac2 with respect to the original Phydbac is due to two factors: (i) the higher number of profiled genomes (150 instead of 71), and (ii) the integration of the chromosomal proximity in the computation of the pairwise gene distance used for the functional predictions. We quantified this improvement using the KEGG metabolic pathway database (13) as reference. Assuming that genes belonging to the same KEGG pathway should have a higher probability of co-evolution than unrelated ones, we expect improved phylogenomic profiles to identify a higher number of such gene pairs in their respective neighborhoods. For this validation, the KEGG pathways of 11 organisms (chosen to represent all main bacterial clades) were used, including 870 sets of pathways, for a total of 11 643 gene occurrences. For each gene in that data set, and using a fixed number of nearest phylogenomic neighbors, we computed the fraction of genes belonging to the query neighborhood and sharing the query KEGG pathway. The results are shown in Figure 2 as a function of the neighborhood size. When considering the ten nearest neighbors, Phydbac2 outperforms its predecessor by about 11%. Integrating chromosomal proximity to the distance measure yields a supplementary 16% gain in performance. Overall, Phydbac2 exhibits a 27% improvement in predictive power over the original Phydbac.
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Figure 2. Number of predicted matching pairs for a given number of closest neighbors: old Phydbac (blue), new Phydbac2 (green), new Phydbac2 + chromosomal proximity (red).
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Automatic gene annotation using the KEGG database is available
through the ‘Full annotation’ link. As described
in detail in (
11), for a given gene and for different neighborhood
sizes we determined the number of genes sharing the same KEGG
pathway. We then computed the probability of observing as many
or more genes from the same pathways through random samples
of the same size. In cases associated with probability less
than 1/100 000, the given gene is deemed to have a co-evolutionary
(and most likely functional) relationship with the relevant
KEGG pathway and the association is reported together with the
corresponding statistics (probability and neighborhood size).
As a test, we automatically annotated all genes occurring in
the KEGG pathways to see how much of the information would be
recovered. Using the above probability threshold (1E-5) and
considering neighborhoods of up to 500 genes we could retrieve
1093 of the 2149 KEGG annotations of
Escherichia coli (793 correct
ones when limiting the neighborhood to 10 neighbors and 383
when using a threshold of 1E-10).
Finally, Phydbac2 allows the display of COG annotation on phylogenomic profiles. This new function is useful to highlight potential inconsistencies between the COG annotation of a query gene and that of its best match in a target genome. Such a display also provides a visual illustration of the matches taken into account by Phydbac that are not (or are mis-) assigned to a COG (i.e. multiple domain protein, or highly divergent homologs).
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CONCLUDING REMARKS
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Due to size limitation, this article could provide only a brief
overview of the new functions offered by Phydbac2. They are
described in much greater details in an animated guided tour
(best viewed with the Flash media player plugin) available on
the Phydbac2 website. A static HTML version is also provided.
Among the most useful new features there is now the possibility
of collecting genes from different queries in a shopping cart
(e.g. for consensus profile searching and phylogenomic-distance-based
tree drawing), the display of additional information such as
KEGG pathway maps and other detailed annotations (paralogs,
fusion events, co-localization, etc.). The processing of entire
genomes for their inclusion in the web server being now completely
automated, additional bacteria will be added continuously as
their genome sequences become publicly available. Specific strains
or species that are not yet included on the web server may also
be added upon email request.
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Notes
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The online version of this article has been published under
an open access model. Users are entitled to use, reproduce,
disseminate, or display the open access version of this article
provided that: the original authorship is properly and fully
attributed; the Journal and Oxford University Press are attributed
as the original place of publication with the correct citation
details given; if an article is subsequently reproduced or disseminated
not in its entirety but only in part or as a derivative work
this must be clearly indicated.
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