BMC Bioinformatics
BioMed Central
Open Access
Database
FUNYBASE: a FUNgal phYlogenomic dataBASE
Sylvain Marthey1, Gabriela Aguileta1,2, François Rodolphe1,
Annie Gendrault1, Tatiana Giraud*2, Elisabeth Fournier3, Manuela LopezVillavicencio4, Angélique Gautier5, Marc-Henri Lebrun6 and
Hélène Chiapello*1
Address: 1UR MIG, INRA, Bâtiment 233 Domaine de Vilvert 78350, Cedex, France, 2UMR ESE, Université Paris-Sud, CNRS, AgroParisTech,
Bâtiment 360, Université Paris-Sud, 91405 Orsay, Cedex France; CNRS 91405 Orsay, France, 3UMR BGPI, INRA, CIRAD, AgroSup, TA A 54/K,
Campus International de Baillarguet, 34398, Montpellier, cedex 5, France, 4MNHN, Département Systématique et Evolution, 12 rue Buffon CP
39, 75005 Paris, France, 5UMR BIOGER, INRA, AgroParisTech, Centre INRA de Versailles, Route de Saint Cyr 78026 ,Versailles, France and 6UMR
MAP Université Lyon-1, CNRS, INSA, BAYER CS, 14, rue Pierre Baizet 69009 Lyon, France
Email: Sylvain Marthey - sylvain.marthey@jouy.inra.fr; Gabriela Aguileta - gabriela.aguileta@u-psud.fr;
François Rodolphe - francois.rodolphe@jouy.inra.fr; Annie Gendrault - annie.jacquemard@jouy.inra.fr; Tatiana Giraud* - tatiana.giraud@upsud.fr; Elisabeth Fournier - elisabeth.fournier@supagro.inra.fr; Manuela Lopez-Villavicencio - mlopez@mnhn.fr;
Angélique Gautier - agautier@versailles.inra.fr; Marc-Henri Lebrun - marc-henri.lebrun@bayercropscience.com;
Hélène Chiapello* - helene.chiapello@jouy.inra.fr
* Corresponding authors
Published: 27 October 2008
BMC Bioinformatics 2008, 9:456
doi:10.1186/1471-2105-9-456
Received: 18 July 2008
Accepted: 27 October 2008
This article is available from: http://www.biomedcentral.com/1471-2105/9/456
© 2008 Marthey et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: The increasing availability of fungal genome sequences provides large numbers of proteins for evolutionary
and phylogenetic analyses. However the heterogeneity of data, including the quality of genome annotation and the
difficulty of retrieving true orthologs, makes such investigations challenging. The aim of this study was to provide a reliable
and integrated resource of orthologous gene families to perform comparative and phylogenetic analyses in fungi.
Description: FUNYBASE is a database dedicated to the analysis of fungal single-copy genes extracted from available
fungal genomes sequences, their classification into reliable clusters of orthologs, and the assessment of their informative
value for phylogenetic reconstruction based on amino acid sequences. The current release of FUNYBASE contains two
types of protein data: (i) a complete set of protein sequences extracted from 30 public fungal genomes and classified into
clusters of orthologs using a robust automated procedure, and (ii) a subset of 246 reliable ortholog clusters present as
single copy genes in 21 fungal genomes. For each of these 246 ortholog clusters, phylogenetic trees were reconstructed
based on their amino acid sequences. To assess the informative value of each ortholog cluster, each was compared to a
reference species tree constructed using a concatenation of roughly half of the 246 sequences that are best approximated
by the WAG evolutionary model. The orthologs were classified according to a topological score, which measures their
ability to recover the same topology as the reference species tree. The full results of these analyses are available on-line
with a user-friendly interface that allows for searches to be performed by species name, the ortholog cluster, various
keywords, or using the BLAST algorithm. Examples of fruitful utilization of FUNYBASE for investigation of fungal
phylogenetics are also presented.
Conclusion: FUNYBASE constitutes a novel and useful resource for two types of analyses: (i) comparative studies can
be greatly facilitated by reliable clusters of orthologs across sets of user-defined fungal genomes, and (ii) phylogenetic
reconstruction can be improved by identifying genes with the highest informative value at the desired taxonomic level.
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Background
Since the historical genome sequencing of the yeast Saccharomyces cerevisiae in 1996 [1], a large increase in the
number of available fungal genomes has occurred, especially during the last five years. This is partly due to the
small size of fungal genomes and the role of consortia
such as the Fungal Genome Initiative at the Broad Institute, the Eukaryotic Genomics Initiative at the JGI, the
TIGR and Genoscope sequencing projects. Consequently,
more than 60 fungal genomes are now publicly accessible
[2,3]http://fungalgenomes.org/wiki/
Fungal_Genome_Links, making this group one of the
best-represented eukaryotic phyla with regard to available
genomic data.
This rapid increase in fungal genome sequences has identified a very large number of genes useful for comparative
analyses. Such studies generally require the non-trivial
task of first assigning genes to protein families according
to a criterion reflecting the observed sequence diversity.
The most common metrics for this classification are either
the percent identity deduced from pair-wise amino acid
sequence alignments or the BLAST e-value. The most common methods to produce sets of orthologous proteins are
generalized simple link classifications, generalized bidirectional best-hit, or more sophisticated algorithms like
the Markov Cluster Algorithm [4,5]. However, the choice
of a clustering algorithm may greatly impact subsequent
analyses [6]. This step can be influenced by biases like the
quality of genome annotation (i.e. the accuracy of gene
prediction) and the presence of multi-domain proteins
which can possibly generate artificial clusters of homologous sequences.
A growing number of online resources are providing
access to genome sequences, such as the Fungal Genome
Intiative (FGI) at the Broad Institute, the Eukaryotic
Genomics Database at the JGI, the TIGR fungal database,
the NCBI Entrez database, or the MIPS fungal database, to
name a few. Several databases have been recently developed to specifically facilitate comparative analysis in
fungi. Most of these resources are dedicated to a particular
taxonomic group, such as hemi-ascomycetes [7], including yeast (Saccharomyces Genome Database) and Candida
[8]. A few are generalist resources integrating all public
fungal genomes, including the e-Fungi repository [9]. This
latter database includes virtually all fungal genomes and
ESTs regardless of their sequence quality and annotation
reliability. Finally, the AFTOL (Assembling the Fungal
Tree Of Life, http://aftol.org/) database was recently developed to provide easy access to the fungal tree life database
via the WASABI (Web Accessible Sequence Analysis for
Biological Inference) system [10]. One of the goals of
AFTOL is to make sequence data, alignments, and other
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types of data rapidly and broadly available to the scientific
community.
The increasing number of available fungal genome
sequences is also very valuable to efforts in robust phylogenetic reconstruction. Indeed, the reliability of the species trees to depict actual evolutionary relationships
increases when using multiple independent loci, while
phylogenies based on one or a few genes can be misleading [11]. Several recent studies have used complete
genome sequences to build robust fungal phylogenies
[3,11-14]. However, if we are to reconstruct phylogenetic
relationships among fungal species whose complete
genomes are not sequenced, only a limited number of
DNA fragments can be practically sequenced. It is therefore useful to many studies if individual genes can be
identified that would best reflect the phylogenetic tree
based upon the proper alignment of the genome as a
whole. Additionally, if we aim to estimate phylogenies
among closely related species, or among isolates from a
single species, it is useful to know which genes have a high
rate of divergence or which ones have an optimal evolutionary rate for resolving relationships at particular taxonomic scales [15].
Here, we present a novel online database and analysis
gateway, FUNYBASE, useful for comparative genomics
and phylogenetic analyses of Fungi, which does not focus
on any particular group or phylum of the kingdom. We
have used a robust approach based on BLAST comparisons and followed by a Markov Cluster Algorithm classification to determine reliable clusters of single-copy
orthologous genes in fungi that are necessary for comparative and evolutionary genomics. Furthermore, the database provides a measure of the informative value of each
gene for phylogenetic reconstruction, i.e. the ability of
each gene to yield a phylogenetic tree reflecting largerscale genome relatedness [11]. Unlike other fungal databases, we also provide data from phylogenetic analyses,
such as alignment statistics, estimated tree, and evolutionary model fitting for each ortholog cluster.
Construction and content
Data sources
Our initial dataset contained 30 fungal genomes (ascomycetes, basidiomycetes, and zygomycetes) (see Table 1).
Genome sources were: NCBI, JGI, BROAD, and Washington University. This dataset corresponds to 275,948 predicted proteins.
Construction of protein families
A BLASTP search of each predicted protein sequences
against the entire assembled protein sequences database
was performed using the NCBI BLAST2 software [16].
Alignments were considered non-spurious after HSP-til-
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Table 1: Fungal genome sources
Species
Source
Nb proteins Release or Date Online database
Ashbya gossypii
Aspergillus fumigatus
Aspergillus nidulans
Aspergillus oryzae
Botrytis cinerea
Candida glabrata
Candida lusitaniae
Chaetomium globosum
Coccidioides immitis
Cryptococcus neoformans
Debaryomyces hansenii
Eremothecium gossypii
Fusarium graminearum
Kluyveromyces lactis
Magnaporthe grisea
Neurospora crassa
Phanerochaete chrysosporium
Rhizopus oryzae
Saccharomyces bayanus
AGD
NCBI
BROAD
NITE
BROAD
NCBI
BROAD
BROAD
BROAD
NCBI
NCBI
NCBI
BROAD
NCBI
BROAD
BROAD
JGI
BROAD
MIT
4726
9923
10701
12074
16448
5181
5941
11124
10457
6475
6317
4718
11640
5331
12841
10620
10048
17467
9424
2.1
06/30/2006
1
07/13/2006
1
07/05/2006
1
1
2
07/30/2006
07/30/2006
07/30/2006
1
07/30/2006
5
7
2.1
3
07/13/2006
Saccharomyces castellii
WashU
4677
07/13/2006
Saccharomyces cerevisiae
Saccharomyces kluyveri
NCBI
WashU
5869
2968
07/30/2006
07/13/2006
Saccharomyces kudriavzevi
WashU
3768
07/13/2006
Saccharomyces mikatae
MIT
9057
07/13/2006
Saccharomyces paradoxus
MIT
8955
07/30/2006
Schizosaccharomyces pombe
Sclerotinia sclerotiorum
Stagonospora nodorum
Trchoderma reesei
Ustilago maydis
Yarrowia lipolytica
NCBI
BROAD
BROAD
JGI
BROAD
NCBI
5045
14522
16597
9997
6522
6520
07/30/2006
1
1
1.2
1
07/30/2006
ing if they met three criteria: (i) coverage of at least 70%
of the query sequence, (ii) identity of at least 30%, and
(iii) E-value cutoff of 6e-6. The BLAST results were analyzed with the program Tribe-MCL obtained from http://
micans.org/mcl[17]. The program Tribe-MCL uses
Markov Clustering (MCL) by creating a similarity matrix
from BLAST e-values and then clusters proteins into
related groups. The main parameter that influences the
size of a cluster in Tribe-MCL is the inflation value, which
can be adjusted from 1.1 (fewer clusters are formed but
with more proteins in each) to 5.0 (more but smaller clusters are formed and proteins with high similarity remain
clustered together). In order to obtain robust ortholog
clusters corresponding to single copy genes present in all
fungal genomes, we used the stringent inflation value of I
= 4 and filtered clusters that contain exactly one protein
per fungal genome (hereafter referred to as single-copy
clusters).
http://agd.vital-it.ch/index.html
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
http://www.broad.mit.edu/annotation/fungi/fgi/
http://www.nite.go.jp/index-e.html
http://www.broad.mit.edu/annotation/fungi/fgi/
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
http://www.broad.mit.edu/annotation/fungi/fgi/
http://www.broad.mit.edu/annotation/fungi/fgi/
http://www.broad.mit.edu/annotation/fungi/fgi/
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
http://www.broad.mit.edu/annotation/fungi/fgi/
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
http://www.broad.mit.edu/annotation/fungi/fgi/
http://www.broad.mit.edu/annotation/fungi/fgi/
http://genome.jgi-psf.org/
http://www.broad.mit.edu/annotation/fungi/fgi/
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/sequence/
fungal_genomes/S_bayanus/MIT/
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/sequence/
fungal_genomes/S_castellii/WashU/
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/sequence/
fungal_genomes/S_kluyveri/WashU/
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/sequence/
fungal_genomes/S_kudriavzevii/WashU/
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/sequence/
fungal_genomes/S_mikatae/MIT/
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/sequence/
fungal_genomes/S_paradoxus/MIT/
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
http://www.broad.mit.edu/annotation/fungi/fgi/
http://www.broad.mit.edu/annotation/fungi/fgi/
http://genome.jgi-psf.org/
http://www.broad.mit.edu/annotation/fungi/fgi/
ftp://ftp.ncbi.nih.gov/genomes/Fungi/
Database design
FUNYBASE is implemented on the relational database
system PostgreSQL (version 8.2.4). Custom-made parsers
have been developed to integrate genomes, annotations,
BLAST results and MCL clusters in the database. All parsers
were developed in Perl using standard modules, such as
BioPerl, DBI and POD documentation (available on
request). The Web interface is designed using the standard
Perl modules DBI and CGI.
Content
FUNYBASE includes two sets of data:
- the complete protein clusters dataset, including
orthologs and paralogs, built from the 30 available fungal
genomes,
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- the subset of 246 families of single-copy orthologs
obtained from 21 genomes with which further phylogenetic analyses were performed (Fig. 1) [11]. This subset of
21 genomes was chosen as a set of fungal genome
sequences with reliable gene prediction (see Ref. [11] for
more details). For each of these 246 ortholog clusters,
FUNYBASE provides the amino-acid substitution model
that best fits the data, the available annotation for the
family, the mean identity percentage of the sequences in
the family, the number of variable sites, the aligned proteins, the corresponding phylogenetic tree, and its similarity with the tree resulting from the concatenated dataset
(i.e., its topological score, and index going from 0 to 100,
see Ref. [11] for more details).
Web interface (Fig. 2 and 3)
The database can be accessed through two main Web
pages:
- the "Orthologs" page provides detailed information on
the 246 families of single copy orthologs obtained from
the 21 genomes with reliable gene annotations (Fig. 2),
- the "Advanced Search" page provides addition methods
(detailed below) for accessing protein families defined
from the 31 public complete fungal genomes (Fig. 3).
The "Orthologs" page
The "Orthologs" page contains detailed information on
the 246 families of single-copy orthologs described previously [11]. These families contain orthologs common to
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the subset of 21 genomes. By clicking on the "Orthologs"
link in the main banner, a table can be obtained which
describes the 246 single-copy families. The families can be
sorted out using different criteria by clicking on the column titles of the table. For each single-copy family, the
following information can be obtained:
(1) the family name,
(2) the mean identity percentage within the family (based
on the ClustalW aligment),
(3) the best model of evolution: a probabilistic model
that describes the different probabilities of change from
one amino-acid, or codon, to another. The different
parameters of the model aim at integrating the factors
involved in the substitution process. In order to choose
the best model for a given dataset (multiple sequence
alignment), we used the program ProtTest that ranks the
models according to the AIC or BIC criteria [18].
(4) the protein cluster annotation.
By clicking on a family name, it is possible to obtain
detailed information on one cluster, including:
- Topological Scores [19]: this index is estimated by pairing all the branches that are shared between the gene tree
and species tree based on the concatenated dataset and
building a 1-to-1 optimum map that takes into account
Figure 1 Pipeline
FUNYBASE
FUNYBASE Pipeline. Scheme showing the main steps in the construction of the ortholog clusters and their subsequent
phylogenetic analysis (for more details see [10]).
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Figure
a)
FUNYBASE
2
Orthologs Page
a) FUNYBASE Orthologs Page. Entries include "Ortholog Family Name", "Mean Identity", "Best Model of evolution" and
"Annotation".
the differences in terms of topology and branch lengths
(see Ref. [11] for more details).
- Average Rates: the mean posterior estimation of the
number of substitutions per site, as obtained by maximum likelihood using the PAML software [20].
The Advanced Search page
The five ways of accessing data on ortholog clusters in the
"Advanced search" mode are:
(1) 'Species selection': This section allows selecting either
a single family of orthologous genes or all families for a
given group of species.
- List of proteins from a family and their annotations.
- ClustalW aligments, which can be downloaded (Phylip
format).
- Phylogenetic trees, which can be download (in Cladogram or Newick format).
(2) 'Protein name': This section makes it possible to find
a family containing a given protein identified by a protein
ID.
(3) 'Keywords': This interface allows the user to find all
the families that contain at least one protein whose annotation matches the queried keyword.
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Figure
a)
FUNYBASE
3
Orthologs Page
b) FUNYBASE Advanced Search Page. One can select the orthologs from a specific species, or group of species.
Options for viewing include "Single-Copy Families Only" or "All Families".
(4) 'Family name': This section allows the user accessing a
family (or families) using its name.
(5) 'BLAST': This section allows performing a BLAST
(either BLASTP or BLASTX) comparison between a query
sequence and the complete FUNYBASE comprising all the
proteins deduced from the 31 public fungal genome
sequences. The produced BLAST results contain links with
an access to the protein family corresponding to the hit.
Utility and Discussion
Reliability of the ortholog clusters
To identify clusters of orthologous genes, we used MCL
clustering methods to recover the maximum number of
orthologous gene clusters with sufficiently stringent
parameters to avoid families containing hidden paralogs.
This approach differs significantly from those used to
develop other databases and interactive web tools. The
trade-off involved in recovering reliable ortholog clusters
is best handled with MCL because this method can be
finely tuned with respect to the dataset [21,22]. We chose
a value of the inflation parameter that had been shown to
produce an optimal number of clusters containing orthologous single-copy genes [4,7,13]. According to Robbertse
et al. [13], the number of orthologous gene clusters found
in available fungal genomes reaches a constant value
when increasing the inflation parameter over three, suggesting that the value of four we chose experimentally is
appropriate. Other studies used rather ad hoc methods to
obtain clusters of orthologous genes, either identifying
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families with related genes present as a single copy in each
genome analyzed [12] or inferring orthology based on the
KOG database http://www.ncbi.nlm.nih.gov/COG/grace/
shokog.cgi[17].
We consider that these ad hoc methods are not efficient in
detecting clusters of reliable single-copy orthologous
genes. For instance, definitions of orthology can be too
liberal if all that they require is that a gene be present only
once in all compared genomes, as hidden paralogy can
pose a serious problem. On the other hand, some methods can be too conservative if they are based on similarity
searches using more general databases, such as KOG,
which currently includes only two fungal genomes (S. cerevisiae and S. pombe) and requires similarity with more
distantly related eukaryotes, resulting in the systematic
exclusion of the orthologs shared exclusively by fungi.
Also, many artefacts can be produced if methods fail to
take into account the modular structure of proteins, which
may result in the false-positive clustering of orthologs,
especially in the case of multi-domain proteins.
Clustering methods come in two general flavors, as they
are either based on similarity searches (e.g., BBH, KOGs,
INPARANOID [23], RSD [24], Tribe-MCL, Ortho-MCL),
or are tree-based (i.e., they take into account the phylogenetic relationships between orthologs and paralogs). If a
reliable species phylogeny is available, tree-based methods may be more accurate in the resolution of homology
relationships because phylogenies naturally portray information on lineage-specific duplications and losses. The
most significant drawback of tree-based methods is the
intensive computation time required and the expert curation needed to evaluate the correct phylogenetic inference
of gene families. A recently proposed method may alleviate some of these burdens by using a mixed approach,
including similarity searches and tree-based methods at
different stages of the analysis (e.g., SYNERGY [25]).
However, tree-based methods rely on the assumption that
there is a robust species tree available. Since many studies
do not have any a priori species tree, it is often essential to
take advantage of the best clustering method that makes
no assumptions about a pre-specified phylogeny (i.e.,
MCL clustering methods).
Usefulness for genomics
FUNYBASE provides an important resource for fungal
comparative genomics, as it allows the retrieval of clusters
of orthologs shared among 21 species, representing the
major fungal taxonomic groups across a large phylogenetic scale. This information can serve multiple purposes,
including:
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Gene comparison
gene sequences, general descriptions, statistics and alignments of the 246 clusters of orthologous genes are available for direct comparison. The molecular evolution of a
given gene, or set of genes, can be obtained at any taxonomic level. Moreover, it is possible to highlight different
levels of gene conservation and/or divergence among fungal lineages in order to assess lineage-specific or gene-specific evolutionary patterns.
Tree comparison
the phylogenetic gene trees corresponding to the 246 clusters of orthologous genes are available and can be directly
employed to test different evolutionary hypotheses. Comparisons of the tree topologies can be used for different
evolutionary studies, such as finding evidence for incomplete lineage sorting, horizontal gene transfers, or accelerated evolutionary rates in some gene families.
Gene searching
FUNYBASE allows BLAST searches against the set of protein sequences corresponding to the 246 clusters of
orthologous genes. Alignments of protein sequences from
one cluster can be used to construct Hidden Markov
Model (HMM) profiles for HMM-based searches of the
corresponding orthologous genes in novel genome
sequences.
Gene function prediction
it is possible to use proteins from novel genomes as queries to find matching annotated sequences in FUNYBASE.
Finding candidate genes for phylogeny reconstruction
based on the topological scores available in FUNYBASE,
one can choose the genes with the appropriate genetic
diversity according to the phylogenetic scale sampled (see
"Usefulness for phylogenetics").
Finding genes with particular evolutionary trends
genes that produce discordant topologies are likely candidates for accelerated evolution or horizontal transfers,
which may be associated with important functional divergences. FUNYBASE provides the topological comparison
data enabling the detection of such interesting candidate
genes.
Usefulness for phylogenetics
The novelty of FUNYBASE is that it provides a measure for
the performance of each gene in estimating the phylogeny
of the included species, i.e. the ability of a gene family to
yield a robust phylogenetic tree reflecting relatedness
defined by larger-scale genomic data and at a variety of
taxonomic scales [11]. Several factors may influence this
performance, such as the size of the encoded protein, the
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rate and mode of evolution of the gene and its demographic and selective histories.
We have shown in a previous study that the phylogenetic
performance of individual genes is highly variable.
Indeed, among the 246 clusters of orthologs, only two
gene families yielded, individually, exactly the same
topology as the tree based on concatenation of roughly
half of the 246 clusters [11]. Interestingly, the genes typically used for fungal phylogenies, encoding gamma and
beta tubulins or elongation factors, were not among the
best performing genes, as they yielded phylogenies very
different from the reference species tree [11]. For studies
integrating new fungal samples, genes providing the informational value for phylogenetic reconstruction can be
selected [11], economizing on costs of sequencing and
improving the accuracy of phylogenies. Genes with high
phylogenetic performance will also be of great interest for
bar coding (i.e. species identification based on a few DNA
sequences).
The phylogenetic performance of the 246 clusters of
orthologs was assessed at a large taxonomic scale (Fig. 1),
but FUNYBASE can also be used for finding useful genes
for building phylogenies at a lower taxonomic scale, such
as closely related species or even within species. For this
goal, genes with a sufficient degree of divergence at the
appropriate taxonomic scale should be chosen, and not
necessarily the genes that were found to have the highest
phylogenetic performance at the scale of the Fungi. The
alignments in FUNYBASE can be used to design primers.
We briefly present below two examples of such studies
(complete results will be reported elsewhere).
The phylogeny of the genus Botrytis, encompassing 22
phytopathogenic species including B. cinerea, responsible
for the grey mould on many crops, has recently been
revised using a phylogeny built based on three nuclear
genes [26]. However, several nodes remained poorly supported. In addition, B. cinerea was recently shown to be
subdivided into two cryptic sympatric species [27], temporarily named B. cinerea Group I and Group II, the first
being not included in the phylogeny of the genus [26]. We
therefore wanted to improve the phylogeny of the Botrytis
genus, and we searched the complete genome sequences
of Botrytis cinerea on the websites of URGI http://urgi.ver
sailles.inra.fr/projects/Botrytis/ and of the Broad Institute
http://www.broad.mit.edu/ for genes homologous to
FUNYBASE single-copy orthologs that were sufficiently
variable for our purpose. Among the 246 single-copy clusters of FUNYBASE, we identified 42 genes from Botrytis
cinerea (Bc) displaying less than 40% identity at the protein sequence level compared to the corresponding
orthologous genes from Sclerotinia sclerotiorum (Ss). We
designed primers for 3 among the most variable genes:
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MS401, MS547 and FG1020 (Bc-Ss proteic identities of
23.4%, 25%, and 28.4%, respectively) by aligning nucleotide sequences for each candidate ortholog, extracted
from the B. cinerea and S. sclerotiorum complete genomes,
and targeting conserved regions. PCR amplification and
sequencing were successful. We sequenced a 808-bp fragment from FG1020 and a 942-bp fragment from MS547
in 23 Botrytis species. Both genes exhibit sequence differences among these species, except B. pelargoni that was
identical to the B. cinerea Group II. A well-resolved phylogeny of Botrytis species could then be built, with a wellsupported placement of the new species B. cinerea Group
II.
The usefulness of the FUNYBASE database for fungal phylogenetics was also tested using species from Penicillium
(and Talaromyces, the name for the sexual form of Penicillium). This group contains mainly soil fungi, and the
opportunistic human pathogen, Penicillium marneffei. The
single previous phylogenetic analysis of this group used
the internal transcribed spacers and 5.8S rRNA (ITS1-5.8SITS2) sequences [28]. Our aim was to evaluate the extant
phylogeny of this group using single-copy sequences and
to find genes which could be used for the specific detection of these species which are not always discriminated
using their ITS sequences, the common "barcode" in
fungi. We used FUNYBASE to retrieve single-copy
orthologs with different rates of evolution and we estimated their performance at different taxonomic scales
within Penicillium. We chose five orthologs with a topological score higher than 91 and with different levels of
variability among fungal species: MS277, MS456, MS501,
FG610 and FG813. The corresponding protein sequences
from Aspergillus fumigatus, the closest species to Penicillium
available in FUNYBASE, were used to retrieve their homologues in the sequences of Penicillium marneffei and Penicillium emmonsii (= Talaromyces stipitatus) available in
GenBank. Nucleotide sequences from each candidate
ortholog family retrieved in A. fumigatus, P. marneffei and
P. emmonsii were aligned and conserved regions were targeted for designing PCR primers. We successfully amplified and sequenced MS456 and FG610 in all the strains
available, while MS501, MS277 and FG813 could be
amplified only in some species. Using the sequences
obtained, phylogenetic trees were constructed using maximum likelihood for each family of orthologs. MS456, the
best gene for recovering a larger-scale phylogeny across
fungal groups [11] was not variable enough within the
genus Penicillium. In contrast, FG610, MS501 and MS277
yielded well-supported trees and should be useful for phylogenetics and bar coding within this genus.
Conclusion
FUNYBASE constitutes a useful resource for facilitating
two types of analyses: (i) comparative studies with reliable
Page 8 of 10
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BMC Bioinformatics 2008, 9:456
clusters of orthologs from a user-defined dataset of fungal
genomes, and (ii) phylogenetic reconstruction by choosing the genes with the highest informative value at the
desired taxonomic level to be studied in a user-defined
fungal group.
http://www.biomedcentral.com/1471-2105/9/456
2.
3.
4.
Availability and requirements
The database is available at http://genome.jouy.inra.fr/
funybase.
Abbreviations
AFTOL: Assembling the Fungal Tree of Life; AGD: Ashbya
genome database; AIC: Akaike information criterion;
BBH: Best bi-directional hit; BIC: Bayesian information
criterion; BLASTP: Basic local alignment search tool for
proteins; BLASTX: Basic local alignment search tool:
search protein database using a protein query; BROAD:
Broad Institute; COG: Clusters of orthologous groups;
EST: Expressed Sequence Tags; FGI: Fungal Genome Initiative; HMM: Hidden Markov model; InPARANOID: Clusters of Orthologous Groups by the Stockholm
Bioinformatics Centre; ITS: Intergenic Transcibed Spacer;
JGI: Joint Genome Institute; KOG: Eukaryotic orthologous groups; MCL: Markov Cluster Algorithm; MIPS:
Munich information center for protein sequences; MIT:
Massachusetts Institute of Technology; NCBI: National
Center for Biotechnology Information; NITE: National
institute of technology evaluation; PAML: Phylogenetic
analysis with maximum likelihood; Perl CGI: Common
gateway interface for perl; Perl DBI: Database interface
(Standard database interface module for perl); Perl POD:
Plain old documentation for perl; RSD: Reciprocal smallest distance algorithm; TIGR: The institute of genomic
research; URGI: Genomic-Info research unit; WASABI:
Web Accessible Sequence Analysis for Biological Inference; WashU: Washington University.
Authors' contributions
SM, AGJ and HC were involved in the construction of the
protein clusters and in the design and implementation of
the database. GA performed the phylogenomics analyses.
TG, EF, HC, FR and MHL participated in the design of the
study. EF, AG and MLV used the database for building
phylogenies. HC, TG, EF, GA and MLV wrote the paper.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
Acknowledgements
This study was funded by the French Bureau des Ressources Génétiques
(BRG 2005–2008), an "ANR Blanc" (ANR-06-BLAN-0201) and an "ANR
Biodiversity" (ANR-07-BDIV-003). G. A. acknowledges CNRS and U. PSUD
post-doctoral grants. We thank Michael Hood for valuable comments on
this manuscript.
References
1.
Goffeau A, Barrell B, Bussey H, Davis R, Dujon B, Feldmann H, Galibert F, Hoheisel J, Jacq C, Johnston M, et al.: Life with 6000 genes.
Science 1996, 274:563-547.
23.
24.
25.
26.
Galagan JE, Henn MR, Ma LJ, Cuomo CA, Birren B: Genomics of the
fungal kingdom: Insights into eukaryotic biology. Genome Res
2005, 15(12):1620-1631.
Soanes D, Alam I, Cornell M, Wong H, Hedeler N, Paton C, Rattray
M, Hubbard3 S, Oliver S, Talbot N: Comparative genome analysis of filamentous fungi reveals gene family expansions associated with fungal pathogenesis. PLoS ONE 2008, 3:e2300.
Enright AJ, Van Dongen S, Ouzounis CA: An efficient algorithm
for large-scale detection of protein families. Nucleic Acid Res
2002, 30:1575-1584.
Li L, Stoeckert CJ, Roos D: OrthoMCL: identification of
ortholog groups for eukaryotic genomes. Genome Res 2003,
13:2178-2189.
Chen F, Mackey A, Vermunt J, Roos D: Assessing performance of
orthology detection strategies applied to eukaryotic
genomes. PLoS ONE 2007, 2:e383.
Dujon B, et al.: Genome evolution in yeast. Nature 2004,
430:35-44.
Rossignol T, Lechat P, Cuomo C, Zeng Q, Moszer I, d'Enfert C: CandidaDB: a multi-genome database for Candida species and
related Saccharomycotina. Nucleic Acids Res 2008, 36:D557-561.
Hedeler C, Wong H, Cornell M, Alam I, Soanes D, Rattray M, Hubbard S, Talbot N, Oliver S, Paton N: e-Fungi: a data resource for
comparative analysis of fungal genomes. BMC Genomics 2007,
8:426.
Kauff F, Cox CJ, Lutzoni F: WASABI: an automated sequence
processing system for multigene phylogenies. Syst Biol 2007,
56(3):523-531.
Aguileta G, Marthey S, Chiapello H, Lebrun M-H, Rodolphe F,
Fournier E, Gendrault-Jacquemard A, Giraud T: Assessing the Performance of Single-Copy Genes for Recovering Robust Phylogenies. Syst Biol 2008, 57:613-627.
Fitzpatrick D, Logue M, Stajich J, Butler G: A fungal phylogeny
based on 42 complete genomes derived from supertree and
combined gene analysis. BMC Evol Biol 2006, 6(1):99.
Robbertse B, Reeves JB, Schoch CL, Spatafora JW: A phylogenomic
analysis of the Ascomycota.
Fung Genet Biol 2006,
43(10):715-725.
Rokas A, Williams BL, King N, Carroll SB: Genome-scale
approaches to resolving incongruence in molecular phylogenies. Nature 2003, 425(6960):798-804.
Townsend JP: Profiling phylogenetic informativeness. Syst Biol
2007, 56:222-231.
Abascal F, Zardoya R, Posada D: ProtTest: selection of best-fit
models of protein evolution.
Bioinformatics 2005,
21(9):2104-2105.
Nye T, Li P, Gilks W: A novel algorithm and web-based tool for
comparing two alternative phylogenetic trees. BIOINFORMATICS 2005.
Yang Y: PAML: a program package for phylogenetic analysis
by maximum likelihood. Comput Appl Biosci 1997, 13:555-556.
Brohee S, van Helden J: Evaluation of clustering algorithms for
protein-protein interaction networks. BMC Bioinformatics 2006,
7:488.
Costa GGL, Digiampietri LA, Ostroski EH, Setúbal JC: Evaluation of
graph-based protein clustering methods. Proceedings of the Fifth
Brazilian Symposium on Mathematical and Computational Biology
(BIOMAT2005) 2005.
Kuramae EE, Robert V, Snel B, Weiss M, Boekhout T: Phylogenomics reveal a robust fungal tree of life. FEMS Yeast Research 2006,
6:1213-1220.
Remm M, Storm CEV, Sonnhammer ELL: Automatic clustering of
orthologs and in-paralogs from pairwise species comparisons. J Mol Biol 2001, 314(5):1041-1052.
Wall DP, Fraser HB, Hirsh AE: Detecting putative orthologs. Bioinformatics 2003, 19(13):1710-1711.
Wapinski I, Pfeffer A, Friedman N, Regev A: Automatic genomewide reconstruction of phylogenetic gene trees. Bioinformatics
2007, 23(13):i549-558.
Staats M, van Baarlen P, van Kan JAL: Molecular phylogeny of the
plant pathogenic genus Botrytis and the evolution of host
specificity. Mol Biol Evol 2005, 22(2):333-346.
Fournier E, Giraud T, Albertini C, Brygoo Y: Partition of the Botrytis cinerea complex in France using multiple gene genealogies. Mycologia 2005, 97:1251-1267.
Page 9 of 10
(page number not for citation purposes)
BMC Bioinformatics 2008, 9:456
27.
28.
http://www.biomedcentral.com/1471-2105/9/456
LoBuglio K, Taylor J: Phylogeny and PCR identification of the
human pathogenic fungus Penicillium marneffei. J Clin Microbiol
1995, 33:85-89.
Altschul S, Madden T, Schäffer A, Zhang J, Zhang Z, Miller W, Lipman
D: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997,
25:3389-3402.
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