GreenPhylDB v2.0: comparative and functional
genomics in plants
Mathieu Rouard1,*, Valentin Guignon1,2, Christelle Aluome1,2, Marie-Ange´lique Laporte2,
Gae¨tan Droc2, Christian Walde1, Christian M. Zmasek3, Christophe Pe´rin2 and
Matthieu G. Conte1
1Bioversity International - CfL programme Parc Scientifique Agropolis II, 34397 Montpellier,
2CIRAD, Department BIOS, UMR DAP - TA40/03, 34398 Montpellier, France and 3Sanford-Burnham Medical
Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA
Received June 24, 2010; Revised August 19, 2010; Accepted August 23, 2010
ABSTRACT
GreenPhylDB is a database designed for compara-
tive and functional genomics based on complete
genomes. Version 2 now contains sixteen full
genomes of members of the plantae kingdom,
ranging from algae to angiosperms, automatically
clustered into gene families. Gene families are
manually annotated and then analyzed phylogenet-
ically in order to elucidate orthologous and paralo-
gous relationships. The database offers various lists
of gene families including plant, phylum and species
specific gene families. For each gene cluster or
gene family, easy access to gene composition,
protein domains, publications, external links and
orthologous gene predictions is provided. Web
interfaces have been further developed to improve
the navigation through information related to gene
families. New analysis tools are also available, such
as a gene family ontology browser that facilitates
exploration. GreenPhylDB is a component of the
South Green Bioinformatics Platform (http://
southgreen.cirad.fr/) and is accessible at http://
greenphyl.cirad.fr. It enables comparative genomics
in a broad taxonomy context to enhance the under-
standing of evolutionary processes and thus tends
to speed up gene discovery.
INTRODUCTION
In recent years, an impressive number of advances in
genomics and biotechnologies have emerged, leading to
an increase in our knowledge of plant genomes.
According to the Genome On-Line Database (GOLD)
(1), 233 plant genome sequencing projects have been
recorded, mainly due to the advances in high-throughput
sequencing technologies. Thus, it is important to provide
molecular biologists with a fast and reliable way of
applying accumulated genomics knowledge of plant
models to plants of agronomic interest. Comparative
genomics provides a starting point for understanding the
molecular basis of biological diversity among plant species
(2) and has a great potential to enhance gene discovery
related to economically important traits and plant
breeding (3,4). The development of GreenPhylDB v 1.0
(5) was motivated by the sequencing of the Arabidopsis
thaliana and Oryza sativa genomes that paved the way
for comparative genomics in plants at the whole genome
level. Since then, an objective in molecular biology has
been to transfer functional information of genes
obtained in one species to another species, thus reducing
research time and costs. Orthologous genes (genes that
diverged by a speciation event) (6) may have conserved a
similar function. Thus analyzing genes between species in
order to identify orthologous genes is a reliable strategy
for functional annotation (7,8). However, establishing
orthology in divergent species is not a trivial exercise.
Also, identifying orthologs in plants, in particular in
flowering plants, is further complicated by the fact that
most plants are paleopolyploids, having experienced
extensive gene duplications and evolved faster than
animals (9–11). With version 2, we broadened the
taxonomy range by adding 14 new genomes representative
of major phylum of the plantae kingdom including
rodophytes (red algae) (12), chlorophytes (green algae)
(13,14), mosses (15), lycophytes and flowering plants
with monocotyledons (16–19) and dicotyledons (20–25).
This enables a broader view of orthologs shared in
plants, and provides a powerful yet simple way to follow
function diversification among land plants related to
*To whom correspondence should be addressed. Tel: +33 467 611 302; Fax: +33 467 610 334; Email: m.rouard@cgiar.org
Published online 22 September 2010 Nucleic Acids Research, 2011, Vol. 39, Database issue D1095–D1102
doi:10.1093/nar/gkq811
 The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

molecular evolution of underlying genes. Thus,
GreenPhylDB not only facilitates comparative and func-
tional genomics, but also provides fundamental insights in
plant gene family evolution.
METHODOLOGY AND RESULTS
Sequence data and clustering
In GreenPhylDB (v1.0), the clustering was originally
performed on the protein-coding genes of the model
plants, O. sativa and A. thaliana using TribeMCL (26).
As these two genomes are so far the most stably annotated
plant genomes, we decided to enrich gene families classi-
fication manually annotated in version 1 (5) and not to
perform a new clustering from scratch with larger number
of genomes of lower quality. A two-step approach was
used to extend the original clustering of plant proteins.
Both O. sativa and A. thaliana clustering was updated
with new releases. Then, using a BlastP (27) approach,
protein sequences of 14 additional full genomes of the
plantae kingdom were allocated to existing gene families.
The resulting unclassified genes, which for most of them
correspond to either genus- or species-specific protein
missing in A. thaliana and O. sativa, were clustered using
the same procedure described initially for GreenPhylDB
v1.0 (5). (See Supplementary Data 1 and 2 for a descrip-
tion of the full procedure).
Gene family annotation
Gathering high-quality information to support
annotation. We used high-quality information sources on
protein domains and metabolic pathways including
InterPro signatures (28), UniProtKB-SwissProt entries
(29) and KEGG pathway entries (30). We considered
these databases as they include manually annotated data
such as PFAM families and PIRSF homeomorphic gene
families. All the protein sequences were scanned with
InterProScan. Locus tags correspondences were estab-
lished or searched with stringent blast parameter
(>90% identity) on UniProtKB-SwissProt. Locus tags
were made with KEGG using mapping files downloaded
on their web site. Pubmed identifiers proposed in
UniProtKB-SwissProt annotation were also incorporated.
To support annotation and to complement lack of infor-
mation in these sources, we added sequence annotation
from dedicated databases (e.g. TAIR).
Annotation tool. Overall consistency of gene families
has been manually checked. For that purpose, to help
curators to characterize clusters, we developed an annota-
tion tool that provides a synthetic view of these external
data sources, including a summary of the InterPro
signatures of the cluster (e.g. number of occurrences,
percentage, type and specificity inside the clusters).
According to the representation of the protein domains
in other gene families, curators may consider the domain
specificity (Supplementary Data 3). Focus was given to
gene families with multi sources or specific InterPro
domains. During curation, registered annotators use col-
lected information to harmonize names, allocate
synonyms and decide if the cluster may be considered
as a superfamily, family, subfamily or group. Relevant
Pubmed identifiers can also be added. Finally a qualitative
confidence indicator is given. Curators submit their anno-
tations which are versioned and monitored by an admin-
istrator before they appear online.
Annotation confidence. Graphical signs indicate to users
the status of the gene cluster annotation process, as well
as a confidence level based on the data available for this
group of genes. There are five signs indicating the confi-
dence of curation applied: ‘High’ for very confident
curation; ‘Normal’ for confident curation; ‘Unknown’
when either there is no information or the information is
too scarce to conclude; ‘Dubious’ and ‘Clustering error’.
‘High’ status is given when several data sources converge
to a similar annotation. So, it is likely that even with the
availability of new genomes, or new gene annotations, this
annotation will remain roughly stable.
Phylogenetic-based analyses and orthology inference
The previous methodology applied to the annotated gene
families (31) has been conserved but the phylogenomic
pipeline code was overhauled to become more robust
and faster, and it was updated with more recent versions
of required external programs. The phylogenomic pipeline
starts with a multi-fasta file containing sequences of a
given gene family. As phylogenetic software is time con-
suming for large-scale analyses, the pipeline was settled
on new high-performance computing facilities, allowing
high-throughput analyses. The pipeline is composed of
usual steps required for phylogenomics (Figure 1)
including:
(i) a filtering procedure based on E-value calculated on
domain architecture using MEME and MAST
software v4.4 (32). A cut-off is automatically
calculated for sequences with a too low E-value with
regards to the median E-value of sequences
composing the cluster. The filtering procedure also
removes alternatively spliced products as well as
mis-annotated (e.g. truncated genes) and too highly
divergent sequences. Filtered sequences are not
excluded from the cluster but they are not taken into
account in the next steps to avoid long-branch
attraction effect and misleading ortholog identifica-
tion.
(ii) Multiple alignment using MAFFT v6.240 of the full
length protein-coding genes (33).
(iii) Masking of the multiple alignments using AL2CO
(34) to optimize the alignment for phylogenetic con-
struction, by removing poorly informative amino
acid positions. Then, the number of conserved
sites after masking and the ratio of the masked
columns on total columns are assessed to check
the quality of the masking step.
(iv) Phylogenetic construction using PhyML v3 with
100 bootstraps (35).
(v) Gene rooting and orthologous scoring using RIO
implemented in a new version (v2 alpha) of the
forester package (36) that produces gene trees at
D1096 Nucleic Acids Research, 2011, Vol. 39, Database issue

the phyloXML standard (37). Ortholog predictions
are based on the concepts (e.g. orthology, sub-tree
neighbor, super-orthologs) described by Zmasek
and Eddy (36). Bootstrapped rooted trees are
used to assign an orthologs score for each sequence.
Using GreenPhylDB
With version 2, the web site has received a face-lift. It now
takes advantage of the AJAX technology to increase the
speed of user interaction, which is particularly important
due to the increasing amount of searchable data within the
database. New user-friendly features were developed to
facilitate the understanding and interpretation of data.
Search facilities or menus on the homepage lead users to
lists of gene families or the dedicated webpage for a given
gene family or sequence.
The ‘Gene family page’ is the central page that contains
concise information divided in several tabs. A typical gene
family overview is shown in Figure 2.
(i) Gene family description: a central table gives a
unique identifier, the name and synonyms, some
cross-references and the status of the curation and
of phylogenetic analyses (Figure 2a).
(ii) Gene family structure: this section allows the user
to explore the GreenPhylDB classification,
including the number of loci belonging to each
cluster (Figure 2b). Sequences were clustered at
four levels of stringency, taking into account poten-
tial sub-classification (e.g. superfamily, family, sub-
family and group). A cluster is often subdivided
into different subgroups at a higher stringency level.
(iii) Gene family composition: using the bar chart, users
can see the composition of the gene family by
species at a glance (Figure 2c). Each bar is clickable
and thus produces the list of sequences and their
associated cross references (InterPro, KEGG,
UniProt, PubMed, GO, etc.).
(iv) Protein domains: in this section, the database
provides information about protein domains.
Protein-coding genes contained in the database
were scanned by InterProscan (38) and Meme
suite (Meme and Mast) (32) in order to assess the
domain conservation consistency and the specificity
of the clustered groups. For each cluster (or gene
family), we identified the specificity of InterPro sig-
natures (i.e. found only—or not—in a given cluster)
(see Figure 2c and Supplementary Data 3). Meme
and Mast analyses may be informative when no
InterPro domain exists for a gene family. By
sorting on the clustering levels, different profiles
are often visible. Even, if biological conclusions
cannot easily be reached, it allows assessment of
the confidence of the clustering and consideration
of those conserved regions not (yet) identified via
Protein
Coding genes
(full genome)
Clustering Manual annotation Gene families
PIRSF mapping
InterPro signatures
Filtering
Multiple alignement
Masking
Viewer
(jalview)
Orthology inference
Viewer
(Archeopteryx)Phylogeny
DB
Clusters
Motifs discovery
Gene Ontology
Family GOBrowser Phylogenomic pipeline
KEGG mapping
UniProt-SP mapping
Data overlay
Pubmed mapping
Orthologs
Figure 1. Flowchart of the GreenPhylDB analyses. The input file is a multi-fasta file containing complete plant proteomes. In a first step, an
automatic clustering aggregates all proteins in previously defined families. Sequences are classified as orphans if they cannot be regrouped in a
cluster. Sequences composing the clusters are analyzed in order to overlay clusters with cross-references (e.g. UniProtKB, Pubmed, InterPro, MEME
motifs, KEGG pathways data). Based on this information, clusters are manually curated in order to identify gene families. Finally, gene family
sequences are analyzed via a phylogenetic-based pipeline to infer ortholog relationships. The procedure can be iterated for each new released genome
using a lighter procedure. This ensures a cumulative and safe growth of the database. The data are stored in the database and can be easily accessed
using dedicated visualizing tools including a gene tree viewer, a gene family browser and ortholog extracting tools.
Nucleic Acids Research, 2011, Vol. 39, Database issue D1097

interPro. Finally, as it may be informative to
graphically view the position of specific protein
patterns for a given gene family, we also imple-
mented a consensus pattern graphical view of
gene families, based on the Prosite MyDomains
Image Creator (39).
(v) Protein list: this section is convenient for displaying
a list of genes and their associated sequences.
The user can sort associated cross-references for
any customized gene list. The final list can be
exported in various formats, such as fasta, Excel
and CSV.
(vi) Phylogenomics analyses: multiple alignments are
available, powered by the latest version of Jalview
applet (40), and gene trees can be visualized with
the Archeopteryx tree viewer [formerly known
as ATV (41)], that was modified to highlight
sequence relationships and to display confidence
scores for predicted orthologous genes (Figure 2e).
The scores in the applet correspond to the
orthology score as described in the RIO procedure.
An advanced ortholog search bar allows you to
display orthologous groups filtered on orthology
and Subtree-neighbor scores and distance. Results
can be downloaded in various formats such as
CSV, XML and Excel.
(vii) Chromosome view: the position of gene family se-
quences along chromosomes (when the genome has
ordered loci) is visible through Flash GViewer
(http://gmod.org/wiki/Flashgviewer/) provided by
Figure 2. Global overview of the family entry page for the Pollen Allergen/Expansin Superfamily (fid=20923). (a) At a glance, users can view that
the family is curated (green light) and is plant specific. (b) Annotated gene families at the different levels are underlined. Gene families are colored in
blue when the phylogenetic analyses are being performed. Here, three gene families are annotated at level 2 (names pop up when you mouse-over)
and two of them were analyzed (gene tree and orthologs are available). (c) This superfamily contains genes from 15 out of the 16 species. Indeed,
there is no representative in the Cyanidioschyzon merolae, a red algae and a large expansion is predicted starting in the embryophytes. (d) The
Expansin/Lol pI InterPro family entry is specific to the Pollen Allergen/Expansin Superfamily. Several other representative domains are listed and
graphically represented in a consensus schema. (e) Multiple alignment and gene tree Java applets (Jalview and Archeopteryx) including orthology
scores can be launched. (f) Gene positions on several genomes are available using GViewer. A zoom on chromosome 10 of Zea mays is visible.
D1098 Nucleic Acids Research, 2011, Vol. 39, Database issue

the GMOD consortium. It acts as a simple synteny
viewer, and may help to identify gene duplications
or gene clusters (Figure 2f), allowing for visual
comparison between plants.
Sequence page
Each sequence stored in GreenPhylDB is accessible
through a specific page. Gene identifiers are usually locus
tags provided by sequencing consortia, but for some
genomes, we prefixed given identifiers by a species code
to make them more meaningful (e.g. Phypa_150356 for a
sequence of Physcomitrella patens). The
UniProtKB-SwissProt recommended gene name is also
provided if any. To facilitate functional genomic studies,
we provided GO annotation and established cross-links
with relevant database including microarray (42–45),
mutant (46,47) and genome databases (47–49). Links
between orthology predictions and gene expressions
are suitable for functional studies (Figure 3). Similarly to
the gene family page, information is split in tabs containing
detailed information around protein sequence, graphical
representation of the genomic structure (exons–introns),
protein domains and the orthology scores. For the latter,
we display all the predicted orthologs for an orthology
score >30. It is recommended to also take into consider-
ation subtree-neighbor scores.
Search tools
Search facilities. A keyword search menu is present at
the top of every page. In comparison with version 1,
auto-completion was added to facilitate searches.
Sequence search with BLASTP or BLASTX is available
to help users with sequences from other species to extract
useful information from GreenPhylDB. In addition, as we
produced HMM profiles for all the gene families, users
can also use their own sequences to perform a motif struc-
tural search using Metameme to compare the domain
architecture of the query with the most similar sequence
of the database.
Gene family ontology browser. The Gene Ontology (GO)
(50) aims at standardizing the representation of gene and
gene product attributes across species and databases. It
is often used to assign functional annotation to specific
genes as implemented with Amigo (51), but large-scale
GO term assignments to gene families is also a relevant
way to search for a comprehensive set of homologous
Figure 3. This example illustrates a putative study of a rice gene (Os10g35050.1) and its predicted orthologs in other species of GreenPhylDB. One
ortholog gene is found in sorghum (Sb01g018430.1) and in brachypodium (Super_8.1280_1). The query sequence has also two co-orthologs in
Arabidopsis (At1g17810.1 in red, At1g73190.1 in blue) that are cross-linked to Genevestigator expression data tools (v3). Os10g35050.1 is
over-expressed at the dough stage while At1g17810.1 and At1g73190.1 are expressed in the silique. This may indicate a role in seed development.
Moreover, it might be interesting to note that these genes are all over-expressed under drought conditions or in presence of abscissic acid (ABA).
This is consistent with the fact that tonoplast-type aquaporins (TIPs) facilitate osmotic water transport across membranes and it suggests a role in
response to drought stress.
Nucleic Acids Research, 2011, Vol. 39, Database issue D1099

genes involved in the same process or pathway. Therefore,
we developed a light web client to search gene families
containing a significant number of genes annotated with
the same GO term (http://greenphyl.cirad.fr/cgi-bin/
plantslim.cgi) based on GO annotation of InterPro signa-
tures and UniProtKB-SwissProt entries. The web interface
displays a digest list of terms defined in the Plant GO
(GOslim plants). Users can select a specific GO entry
and access a list of families potentially involved in a bio-
logical process, a cellular component or a molecular
function. More precisely, for each identified family, a
user could have access to the sub classification and see if
a GO belongs to a specific annotated subgroup.
GreenPhyl database content
To date, GreenPhylDB contains 8231 gene clusters
composed of at least five sequences resulting from the
clustering of 16 full genome sequences at the clustering
level 1. Out of these, 2771 gene families have been
annotated with an emphasis on those having specific
InterPro protein domains. By default, gene clusters has
been tagged as non-curated, however due of the annota-
tion in version 1, some un-annotated clusters may have
been assigned a gene family name already. More than
2000 gene families were analyzed with the phylogenomic
pipeline. All these numbers will be increasing constantly.
Manual annotation will continue in GreenPhyl as new
information from high-quality databases increases.
Global statistics are provided (http://greenphyl.cirad
.fr/v2/cgi-bin/stats.cgi).
Between GreenPhylDB version 1 and this version, we
have added a significant number of genomes and made
several releases of some of them. Overall, existing
clusters remained consistent. Almost all of them were
enriched with new sequences. The addition of 14 new
genomes modified in some cases orthologous relation-
ships, showing if necessary, that a large-taxonomic
sample is suitable for orthologous predictions. We noted
an increase of 25% (2000) of multiple species gene
families. Among new clusters, we noticed are species or
phylum specific (e.g. of algae or mosses) but a significant
part corresponds to clusters composed of monogenic
families (Ex: Photosystem I PsaO Family: 43176). We
also noticed that orphan genes (i.e. unclassified sequences)
in our classification system were quite often sequences
that became obsolete in a later genome release.
Multiple-genome clustering is not only useful for com-
parative genomics, but is also a way to detect low
quality sequences in genome annotations.
CONCLUSIONS AND FUTURE DIRECTIONS
The GreenPhyl database has been considerably enriched
with additional genomes, and the website has been also
overhauled to provide much more user friendly features
and tools. It comprises manually annotated gene families
with orthology results supported by confidence scores, and
connected to key external links such as data expression
databases. On a regular basis, we will update the manual
annotation of gene families and their phylogenetic
analyses. With the increasing numbers of plant sequencing
projects, GreenPhylDB will continue to include new
genomes, in particular those having a key position in
plant taxonomy. Some improvements related to the gene
family clustering will be made in order to create seed
members. We will also work on new methodologies to
propose an orthologous scoring system considering
syntenic information.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENTS
We thank Vincent Lefort for his support with PhyML
and Guilhem Sempere for his modification on the
Archeopteryx applet that can now display sequence rela-
tionships and its associated scores. We are grateful to
Nade`ge Lanau who contributed to the gene family anno-
tation and to Valentina Barbiero for the homepage species
tree. We also thank Manuel Ruiz for his support to the
project.
FUNDING
CGIAR Generation Challenge Programme (http://www
.generationcp.org) (grant G4008.21). Funding for open ac-
cess charge: Generation Challenge Programme; National
institutes of Health R01 GM087218-01 (to C.M.Z.).
Conflict of interest statement. None declared.
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