Algal Functional Annotation Tool

UCLA
UCLA Previously Published Works
Title
Algal Functional Annotation Tool: a web-based analysis suite to functionally interpret
large gene lists using integrated annotation and expression data
Permalink
https://escholarship.org/uc/item/6zm9j7bj
Journal
BMC Bioinformatics, 12(1)
ISSN
1471-2105
Authors
Lopez, David
Casero, David
Cokus, Shawn J
et al.
Publication Date
2011-07-12
DOI
http://dx.doi.org/10.1186/1471-2105-12-282
Peer reviewed
eScholarship.org Powered by the California Digital Library
University of California

DATABASE Open Access
Algal Functional Annotation Tool: a web-based
analysis suite to functionally interpret large
gene lists using integrated annotation and
expression data
David Lopez1
, David Casero1
, Shawn J Cokus1
, Sabeeha S Merchant2,3
and Matteo Pellegrini1,3*
Abstract
Background: Progress in genome sequencing is proceeding at an exponential pace, and several new algal
genomes are becoming available every year. One of the challenges facing the community is the association of
protein sequences encoded in the genomes with biological function. While most genome assembly projects
generate annotations for predicted protein sequences, they are usually limited and integrate functional terms from
a limited number of databases. Another challenge is the use of annotations to interpret large lists of ‘interesting’
genes generated by genome-scale datasets. Previously, these gene lists had to be analyzed across several
independent biological databases, often on a gene-by-gene basis. In contrast, several annotation databases, such as
DAVID, integrate data from multiple functional databases and reveal underlying biological themes of large gene
lists. While several such databases have been constructed for animals, none is currently available for the study of
algae. Due to renewed interest in algae as potential sources of biofuels and the emergence of multiple algal
genome sequences, a significant need has arisen for such a database to process the growing compendiums of
algal genomic data.
Description: The Algal Functional Annotation Tool is a web-based comprehensive analysis suite integrating
annotation data from several pathway, ontology, and protein family databases. The current version provides
annotation for the model alga Chlamydomonas reinhardtii, and in the future will include additional genomes. The
site allows users to interpret large gene lists by identifying associated functional terms, and their enrichment.
Additionally, expression data for several experimental conditions were compiled and analyzed to provide an
expression-based enrichment search. A tool to search for functionally-related genes based on gene expression
across these conditions is also provided. Other features include dynamic visualization of genes on KEGG pathway
maps and batch gene identifier conversion.
Conclusions: The Algal Functional Annotation Tool aims to provide an integrated data-mining environment for
algal genomics by combining data from multiple annotation databases into a centralized tool. This site is
designed to expedite the process of functional annotation and the interpretation of gene lists, such as those
derived from high-throughput RNA-seq experiments. The tool is publicly available at http://pathways.mcdb.
ucla.edu.
* Correspondence: matteop@mcdb.ucla.edu
1
Department of Molecular, Cell, and Developmental Biology, University of
California, Los Angeles, CA, USA
Full list of author information is available at the end of the article
Lopez et al. BMC Bioinformatics 2011, 12:282
http://www.biomedcentral.com/1471-2105/12/282
© 2011 Lopez 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.

Background
Next-generation sequencers are revolutionizing our abil-
ity to sequence the genomes of new algae efficiently and
in a cost effective manner. Several assembly tools have
been developed that take short read data and assemble
it into large continuous fragments of DNA. Gene pre-
diction tools are also available which identify coding
structures within these fragments. The resulting tran-
scripts can then be analyzed to generate predicted pro-
tein sequences. The function of these protein sequences
are subsequently determined by searching for close
homologs in protein databases and transferring the
annotation between the two proteins. While some ver-
sions of the previously described data processing pipe-
line have become commonplace in genome projects, the
resulting functional annotation is typically fairly minimal
and includes only limited biological pathway information
and protein structure annotation. In contrast, the inte-
gration of a variety of pathway, function and protein
databases allows for the generation of much richer and
more valuable annotations for each protein.
A second challenge is the use of these protein-level
annotations to interpret the output of genome-scale
profiling experiments. High-throughput genomic tech-
niques, such as RNA-seq experiments, produce mea-
surements of large numbers of genes relevant to the
biological processes being studied. In order to inter-
pret the biological relevance of these gene lists, which
commonly range in size from hundreds to thousands
of genes, the members must be functionally classified
into biological pathways and cellular mechanisms.
Traditionally, the genes within these lists are exam-
ined using independent annotation databases to assign
functions and pathways. Several of these annotation
databases, such as the Kyoto Encyclopedia of Genes
and Genomes (KEGG) [1], MetaCyc [2], and Pfam [3],
include a rich set of functional data useful for these
purposes.
However, presently researchers must explore these dif-
ferent knowledge bases separately, which requires a sub-
stantial amount of time and effort. Furthermore,
without systematic integration of annotation data, it
may be difficult to arrive at a cohesive biological picture.
In addition, many of these annotation databases were
designed to accommodate a single gene search, a meth-
odology not optimal for functionally interpreting the
large lists of genes derived from high-throughput geno-
mic techniques. Thus, while modern genomic experi-
ments generate data for many genes in parallel, their
output must often still be analyzed on a gene-by-gene
basis across different databases. This fragmented analysis
approach presents a significant bottleneck in the pipe-
line of biological discovery.
One approach to solving this problem is integrating
information from multiple annotation databases and
providing access to the combined biological data from a
single comprehensive portal that is equipped with the
proper statistical foundations to effectively analyze large
gene lists. For example, the DAVID database integrates
information from several pathway, ontology, and protein
family databases [4]. Similarly, Ingenuity Pathway Analy-
sis (IPA) provides an integrated knowledge base derived
from published literature for the human genome [5].
The integrated functional information and annotation
terms are then assigned to lists of genes and for some
analyses, enrichment tests are performed to determine
which biological terms are overrepresented within the
group of genes. By combining the information found in
a number of knowledge bases and performing the analy-
sis of lists of genes, these tools permit the efficient pro-
cessing of high-throughput genomic experiments and
thus expedite the process of biological discovery. How-
ever, most of these integrated databases have been
developed for the analysis of well-annotated and thor-
oughly studied organisms, and are lacking for many
newly genome-enabled organisms.
One large group of organisms for which integrated
functional databases are lacking are the algae. The algae
constitute a branch in the plant kingdom, although they
form a polyphyletic group as they do not include all the
descendants of their last common ancestor. As many as
10 algal genomes have been sequenced, including those
of a red alga and several chlorophyte algae, with several
more in the pipeline [6-11]. Algal genomic studies have
provided insights into photosymbiosis, evolutionary rela-
tionships between the different species of algae, as well
as their unique properties and adaptations. Recently,
there has been a renewed interest in the study of algal
biochemistry and biology for their potential use in the
development of renewable biofuels [reviewed in [12]].
This has promoted the study of varied biochemical pro-
cesses in diverse algae, such as hydrogen metabolism,
fermentation, lipid biosynthesis, photosynthesis and
nutrient assimilation [13-20]. One of the most studied
algae is Chlamydomonas reinhardtii. It has a sequenced
genome that has been assembled into large scaffolds
that are placed on to chromosomes [6]. For many years,
Chlamydomonas has served as a reference organism for
the study of photosynthesis, photoreceptors, chloroplast
biology and diseases involving flagellar dysfunction
[21-25]. Its transcriptome has recently been profiled by
RNA-seq experiments under various conditions of nutri-
ent deprivation [[26,27], unpublished data (Castruita M.,
et al.)].
While Chlamydomonas has been extensively charac-
terized experimentally, annotation of its genome is still
Page 2 of 10

approximate. Although KEGG categorizes some C. rein-
hardtii gene models into biological pathways, other
databases - such as Reactome [28] - do not directly pro-
vide information for proteins of this green alga. Compli-
cating the analysis of Chlamydomonas genes is the fact
that there are two assemblies of the genome in use (ver-
sion 3 and version 4) and multiple sets of gene models
have been developed that are catalogued under diverse
identifiers: Joint Genome Institute (JGI) FM3.1 protein
IDs for the version 3 assembly, and JGI version FM4
protein IDs and Augustus version 5 IDs for the version
4 assembly [11,29]. The differences between these
assemblies are significant; for example, the version 3
assembly contains 1,557 continuous segments of
sequence while the fourth version contains 88. Although
the version 3 assembly is superseded by version 4, users
presently access version 3 because of the richer user-
based functional annotations. In addition, other sets of
gene predictions have been generated using a variety of
additional data, including ESTs and RNA-seq data, to
more accurately delineate start and stop positions and
improve upon existing gene models. One such gene pre-
diction set is Augustus u10.2. As such, there are a vari-
ety of gene models between different assemblies being
simultaneously used by researchers, presenting compli-
cations in genomics studies. To facilitate the analysis of
Chlamydomonas genome-scale data, we developed the
Algal Functional Annotation Tool, which provides a
comprehensive analysis suite for functionally interpret-
ing C. reinhardtii genes across all available protein iden-
tifiers. This web-based tool provides an integrative data-
mining environment that assigns pathway, ontology, and
protein family terms to proteins of C. reinhardtii and
enables term enrichment analysis for lists of genes.
Expression data for several experimental conditions are
also integrated into the tool, allowing the determination
of overrepresented differentially expressed conditions.
Additionally, a gene similarity search tool allows for
genes with similar expression patterns to be identified
based on expression levels across these conditions.
Construction and Content
Integration of Multiple Annotation Databases
The Algal Functional Annotation Tool integrates anno-
tation data from the biological knowledge bases listed in
Table 1. Publically available flat files containing annota-
tion data were downloaded and parsed for each indivi-
dual resource. Chlamydomonas reinhardtii proteins
were assigned KEGG pathway annotations by means of
sequence similarity to proteins within the KEGG genes
database [1]. MetaCyc [2], Reactome [28], and Panther
[30] pathway annotations were assigned to C. reinhardtii
proteins by sequence similarity to subsets of UniProt
IDs annotated in each corresponding database. In all
cases, sequence similarity was determined by BLAST.
BLAST results were filtered to contain only best hits
with an E-value < 1e-05.
Gene Ontology (GO) [31] terms were downloaded
from the Chlamydomonas reinhardtii annotation pro-
vided by JGI. These GO terms were associated with
their respective ancestors in the hierarchical ontology
structure to include broader functional terms and pro-
vide a complete annotation set. Pfam domain annota-
tions were assigned by direct search against protein
domain signatures provided by Pfam. InterPro [32] and
user-submitted manual annotations are based on those
contained within JGI’s annotation of the C. reinhardtii
genome [11]. These methods were applied to four types
of gene identifiers commonly used for C. reinhardtii
proteins: JGI protein identifiers (versions 3 and 4) and
Augustus gene models (versions 5 and 10.2). In total,
over 12,600 unique functional annotation terms were
assigned to 65,494 C. reinhardtii gene models spanning
four different gene identifier types by these methods
(Table 2). These assigned annotations may be explored
for single genes using a built-in keyword search tool as
well as an integrated annotation lookup tool which dis-
plays all annotations for a particular identifier.
Assignment of Annotation from Arabidopsis thaliana
To extend the terms associated with C. reinhartdii
genes, functional terms were inferred by homology to
the annotation set of the plant Arabidopsis thaliana
(thale cress). Identification of orthologous proteins was
based on sequence similarity and subsequent filtering of
the results by retaining only mutual best hits between
the two sets of protein sequences. The corresponding
Arabidopsis thaliana annotation was used to supple-
ment GO terms and was similarly expanded to contain
term ancestry. The A. thaliana annotations of the Map-
Man Ontology [33] and MetaCyc Pathway database [2]
Table 1 List of annotation resources integrated into the
Algal Functional Annotation Tool
Resource URL Reference
KEGG http://www.genome.jp/kegg/ [1]
MetaCyc http://www.metacyc.org/ [2]
Pfam http://pfam.sanger.ac.uk [3]
Reactome http://www.reactome.org/ [28]
Panther http://www.pantherdb.org/pathway [30]
Gene Ontology http://www.geneontology.org/ [31]
InterPro http://www.ebi.ac.uk/interpro [32]
MapMan
Ontology
http://mapman.gabipd.org/ [33]
KOG http://www.ncbi.nlm.nih.gov/COG/grace/
shokog.cgi
[35]
Primary databases used to functionally annotate gene models and integrated
into the Algal Functional Annotation Tool.
Page 3 of 10

were also used to provide more complete annotation
coverage of the C. reinhardii genome.
Functional Term Enrichment Testing
The hypergeometric distribution is commonly used to
determine the significance of functional term enrich-
ment within a list of genes. In this test, the occurrence
of a functional term within a gene list is compared to
the background level of occurrence across all genes in
the genome to determine the degree of enrichment. A
p-value based on this test can be calculated from four
parameters: (1) the number of genes within the list, (2)
the frequency of a term within the gene list, (3) the
total number of genes within the genome, and (4) the
frequency of a term across all genes in the genome.
This test effectively distinguishes truly overrepresented
terms from those occurring at a high frequency across
all genes in the genome and therefore within the gene
list as well. The cumulative hypergeometric test assigns
a p-value to each functional term associated with genes
within a given list, and all functional terms are ranked
by ascending p-value (i.e. by descending levels of enrich-
ment). Huang et al. reviews the use of the hypergeo-
metric test for functional term enrichment [34]. The
Algal Functional Annotation Tool computes hypergeo-
metric p-values using a Perl wrapper for the GNU
Scientific Library cumulative hypergeometric function
written in C to provide a quick and accurate implemen-
tation of this statistical test.
Dynamic Visualization of KEGG Pathway Maps
Individual pathway maps from KEGG provide informa-
tion on protein localization within the cell, compart-
mentalization into different cellular components, or of
reactions within a larger metabolic process. Visualization
of proteins from gene lists onto pathway maps is useful
for their interpretation. The Algal Functional Annota-
tion Tool utilizes the publicly available KEGG applica-
tion programming interface (API) for pathway
highlighting. The information linking C. reinhardtii pro-
teins to identifiers within the KEGG database is used to
determine the subset of KEGG IDs within the supplied
gene list associated with a particular pathway. The Algal
Functional Annotation Tool also deduces which proteins
within the pathway are located within the genome of C.
reinhardtii but not found in the gene list and sends the
corresponding identifiers to the KEGG API to be high-
lighted in a different background color. This API inter-
face is implemented using the SOAP architecture for
web applications.
Integration of Expression Data
The expression levels of C. reinhardtii genes have been
experimentally characterized under numerous conditions
using high-throughput methods such as RNA-seq
[[26,27], unpublished data (Castruita M., et al.)]. These
expression data were compiled and analyzed to deter-
mine which genes are over- and under-expressed in
each experimental condition. The expression data was
preprocessed to normalize the counts for uniquely map-
pable reads in any experiment. Genes exhibiting greater
than a two-fold change in expression compared to aver-
age expression across all conditions with a Poisson
cumulative p-value of less than 0.05 were considered
differentially expressed. Using this data, C. reinhardtii
genes were associated with conditions in which they
were over- and under-expressed.
The compiled expression data was also analyzed to
find functionally related genes based on their expression
levels across the different experimental conditions
[[26,27], unpublished data (Castruita M., et al.)]. Genes
demonstrating low variance of expression across all
samples were not considered. This analysis was per-
formed for three representations of the expression data:
absolute counts, log counts, and log ratios of expression.
By this method, C. reinhardtii genes are each associated
with 100 genes with the most similar expression pat-
terns to determine potentially functionally related genes.
Gene Identifier Conversion
Due to the existence of several protein identifier types
(FM3.1, FM4, Au5, Au10.2), different identifiers are
associated with an individual protein within the Chlamy-
domonas genome. In order to extend annotations from
one identifier type to another, matching protein identi-
fiers are deduced by sequence similarity filtering for
mutual best hits between identifiers using BLAST.
Matching identifiers with 100% sequence coverage are
kept, and the rest of the mutual best hits are filtered to
include only those proteins with matches with at least
Table 2 Number of gene identifiers associated with annotation databases
Identifier Type Total Gene IDs KEGG Reactome Panther Gene Ontology MapMan KOG Pfam InterPro
JGI v3.0 14598 5348 2740 1147 6563 5214 9139 7166 7532
JGI v4.0 16706 4232 1949 1085 7568 3171 9973 7305 8151
Augustus v5.0 16888 4686 2983 1673 4334 3160 5123 8202 5202
Augustus u10.2 17302 4583 3326 1913 6956 3892 8977 8691 7464
Number of Chlamydomonas reinhardtii identifiers with at least one functional annotation for each primary database, shown per identifier type.
Page 4 of 10

75% coverage. Potential ambiguities involving proteins
similar to multiple other proteins are resolved by con-
sidering only the reciprocal best hit from the BLAST
query in the opposite direction. The information derived
by this analysis is used to convert gene identifiers
between different types, which allows the Algal Annota-
tion Tool to work with multiple protein identifier types.
Web-Based Interface and Updates
The web interface of the Algal Functional Annotation
Tool consists of a set of portals that give access to the
different types of analyses available. Results are shown
within expandable/collapsible HTML tables that display
annotation information along with the statistical results
of the analysis. When expanded, the results table shows
which gene identifiers contain a specific annotation along
with further information regarding matching gene identi-
fiers and BLAST E-values. Updates to the Algal Func-
tional Annotation Tool are semi-automated using a set of
Perl scripts that parse and process updated flat files from
the various integrated annotation databases at regular
intervals. Currently, functional data from the primary
annotation databases is set to be updated every 4 months.
Utility and Discussion
Comprehensive, Integrated Data-Mining Environment
The Algal Functional Annotation Tool is composed of
three main components - functional term enrichment
tests (which are separated by type), a batch gene identi-
fier conversion tool, and a gene similarity search tool. A
‘Quick Start’ analysis is provided from the front page, fea-
turing enrichment analysis using a sample set of data-
bases containing the richest set of annotations (Figure 1).
From any page, the sidebar provides access to the ‘Quick
Start’ function of the tool.
Numerous other enrichment analyses - including
enrichment using pathway, ontology, protein family, or
differential expression data - are available within the
Algal Functional Annotation Tool. Enrichment results
are always sorted by hypergeometric p-value and when-
ever possible contain links to the primary database’s
entry for that annotation or to the protein page of the
gene identifier. The number of hits to a certain annota-
tion term are also displayed alongside the p-value, and
results may always be expanded to show additional
details, such as the specific gene IDs within the list
matching a certain annotation (Figure 2). These results
Figure 1 Algal Functional Annotation Tool. The front page of the Algal Functional Annotation Tool. A ‘Quick Start’ analysis is available to test
for enrichment using the richest annotation databases included in the tool. Other features accessible from the sidebar include more specific
enrichment tests (based on biological pathways, ontology terms, or protein families), a gene identifier conversion tool, a manual annotation
search tool, and an expression similarity search tool.
Page 5 of 10

are downloadable as tab-delimited text files which may
then be further analyzed or used in conjunction with
other databases.
Dynamic visualization of KEGG pathway maps may be
accessed from the results table for KEGG pathway
enrichment by clicking on any pathway name. The pro-
teins in the list that are members of the particular biolo-
gical pathway will appear in red, while those proteins
existing in Chlamyomonas reinhardtii but not in the list
appear in green (Figure 3). Alternatively, by expanding
the pathway results and following the link at the bottom,
the user may select a custom color scheme for visualizing
the proteins on pathway maps. These custom color
schemes may be designed on a gene-by-gene basis
(choosing colors individually for genes) or in a group-by-
group fashion (such as choosing a color for those pro-
teins found within the organism but not in the gene list).
A list of genes may also be converted into a list of
gene identifiers of another type. This feature allows easy
transformation of gene IDs into corresponding models
for use in other databases that may have additional
annotation information. Additionally, the resulting list
of gene identifiers may be used as a new starting point
for enrichment analysis. Because of the different annota-
tions associated with other gene identifier types (albeit
of the same proteins), enrichment results using a
converted set of gene IDs may yield new biological
information.
The gene similarity search tool, the third component
of the Algal Functional Annotation Tool, accepts single
genes and returns functionally related genes (based on
gene expression across different experimental condi-
tions) using user-specified distance metrics and thresh-
olds. Presently, functionally related genes may be
determined using correlation distance based on absolute
counts, log counts, or log ratios of expression. The
results page shows the original query gene at the top in
gray and any resulting genes, sorted by similarity, are
shown below the query gene (Figure 4). A colormap
based on gene expression is generated for the different
genes across the conditions, and this colormap may be
changed to display absolute expression, log expression,
or log ratios of expression. The distance between any
gene and the original query gene is displayed by hover-
ing the mouse over the gene identifier of interest. Quan-
titative expression data (e.g. absolute counts) are
provided for each experiment by hovering over the col-
ormap. Whenever a description of a gene is available,
this is displayed when hovering over the gene identifier
as well. Links to external databases (e.g. JGI, KEGG)
providing more information about the genes are pro-
vided with the results.
Figure 2 Annotation Enrichment Results. Annotation enrichment results, sorted by ascending hypergeometric p-values, are shown in
expandible/collapsible HTML tables such as the one shown. When expanded, the genes within the user-submitted list containing the expanded
annotation are shown alongside additional statistical information. All results are downloadable as tab-delimited text files.
Page 6 of 10

Ability to Re-Run Analysis for Subsets of Genes
Once a gene list is supplied and enrichment results have
been returned, a subset of genes corresponding to those
that contain a particular annotation may be isolated and
re-run through the tool to be analyzed as a separate,
smaller gene list. This allows users to select a particu-
larly interesting group of functionally related genes and
isolate them to see if they are also enriched for other
functional terms. This also allows the user to prune
large gene lists into more focused lists of functionally
similar genes and removing some of the inherent noise
associated with high-throughput experimental techni-
ques and their resulting gene lists. This feature of the
tool may be accessed by expanding the enrichment
results of a particular annotation and selecting to re-run
the analysis using only that subset of proteins. From this
step, users may select which database types to query for
enrichment (e.g. pathway, ontology, protein family).
Expanded Annotation Coverage
The methods described to compensate for the incom-
plete annotation coverage of Chlamydomonas reinhardtii
genes resulted in the addition of a vast number of unique
annotations to the genome. While there is a strong over-
lap between pre-existing annotations and those assigned
by inference, many new terms have also been added. The
annotations derived by orthology, however, are not
mixed with the annotations attained directly to decrease
the possibility of false positive associations of functional
terms that may distort the analysis, and to permit a com-
parison with the functional terms derived directly from
the Chlamydomonas annotation.
Example - Sulfur-Related Genes
Using a filtered list of C. reinhardtii genes derived from
transcriptome sequencing of the green alga under sul-
fur-depleted conditions [26], the Algal Functional Anno-
tation Tool found enrichment for annotations related to
sulfur metabolism, cysteine and methionine metabolism,
and sulfur compound biosynthesis. For each annotation,
the results may be expanded to reveal the genes con-
taining that particular annotation. Furthermore, there is
significant overlap between terms directly assigned to C.
reinhardtii proteins and those inferred from A. thaliana
orthology. Visualization of the sulfur metabolism KEGG
pathway shows that a majority of the enzymes involved
Figure 3 Dynamic Visualization of Gene Lists onto KEGG Pathway Maps. Dynamic KEGG pathway maps may be visualized to show the
different proteins within a user-submitted gene list. Shown is the ‘Sulfur Metabolism’ dynamic pathway with the matching proteins submitted
highlighted in red. In this example, the submitted gene list is drawn from literature characterizing Chlamydomonas under sulfur-deprived
conditions [26].
Page 7 of 10

in this biological process is in the sample list, and the
reactions they catalyze may be seen on the pathway
map. The results for any enrichment analysis may be
downloaded as a tab-delimited text file. Taking a gene
found to be associated with the KEGG pathway ‘Sulfur
metabolism’ by this enrichment analysis (JGI v. 3 ID
206154) as a starting input into the gene similarity
search tool, the genes corresponding to sulfate transpor-
ter, methionine synthase reductase, and cysteine dioxy-
genase were found within the top 15 results using the
correlation metric between log counts.
Future Directions
As with all tools that integrate data from multiple exter-
nal sources, the power of analysis using the Algal Func-
tional Annotation Tool is ultimately limited by the
quality of the annotations within the primary databases.
With the steady growth of knowledge in these annotation
databases, the utility of the analyses provided is expected
to increase in the future as more biological associations
are assigned to genes. Additionally, as Chlamydomonas
reinhardtii genes continue to be experimentally charac-
terized, the assignment of manual annotations will also
fill in the gaps left by automated annotation assignment
and thus expand the annotation coverage throughout the
genome, further improving the results generated by our
portal. Lastly, the extensible nature of the Algal Func-
tional Annotation Tool will allow us to add other algal
organisms in the future using the same platform so that
genomic data from other algal model organisms may be
analyzed in a similar fashion as that currently available
for Chlamydomonas reinhardtii.
Conclusions
The Algal Functional Annotation Tool is intended as a
comprehensive analysis tool to elucidate biological
Figure 4 Expression Similarity Search Tool Results. An example of the results from the Gene Similarity Search Tool. Pairwise distances
between resulting genes and the submitted gene are shown in the lower right corner when the mouse hovers over a gene of interest.
Whenever applicable, a short description of the resulting gene is also shown when hovering over a gene. Expression data is shown when
hovering over a point of the colormap.
Page 8 of 10

meaning from gene lists derived from high-throughput
experimental techniques. Annotation sets from a num-
ber of biological databases have been pre-processed and
assigned to gene identifiers of the green alga Chlamydo-
monas reinhardtii, and this annotation data may be
explored in multiple ways, including the use of enrich-
ment tests designed for large gene lists. Furthermore,
the site enables the visualization of proteins within path-
way maps. Using several methods, such as inferring
annotations from orthologous proteins of other organ-
isms, the initially sparse annotation coverage of C. rein-
hardtii is alleviated, allowing for a more effective
functional term enrichment analysis. Other functions of
the tool include a batch gene identifier conversion tool
and a manual annotation search tool. Lastly, similar
genes based on expression across several conditions may
be explored using the gene similarity search tool.
Availability and Requirements
Project name: Algal Functional Annotation Tool
• Public web service: http://pathways.mcdb.ucla.edu;
Free and no registration.
• Programming language: Perl/CGI
• Database: MySQL
• Software License: GNU General Public License
List of Abbreviations Used
API: Application Programming Interface; BLAST: Basic Local Alignment Search
Tool; CGI: Common Gateway Interface; DAVID: Database for Annotation,
Visualization, and Integrated Discovery; GO: Gene Ontology; KEGG: Kyoto
Encyclopedia of Genes and Genomes; JGI: Joint Genome Institute; SOAP:
Simple Object Access Protocol.
Acknowledgements and Funding
We acknowledge funding of this work by the US Department of Energy
under contract DE-EE0003046 awarded to the National Alliance for
Advanced Biofuels and Bioproducts.
Author details
1
Department of Molecular, Cell, and Developmental Biology, University of
California, Los Angeles, CA, USA. 2
Department of Chemistry and
Biochemistry, University of California, Los Angeles, CA, USA. 3
Institute of
Genomics and Proteomics, University of California, Los Angeles, CA, USA.
Authors’ contributions
MP conceived the analysis and main features of the tool. DL wrote and
tested the code, constructed the annotation database, designed the user
interface, and wrote the initial draft of the manuscript. SC provided the
implementation of the hypergeometric distribution function. DC provided
Pfam data and compiled the expression data. SM provided access to the
expression data and tested the tool. All authors read, edited and approved
the final manuscript.
Received: 8 February 2011 Accepted: 12 July 2011
Published: 12 July 2011
References
1. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for
representation and analysis of molecular networks involving diseases
and drugs. Nucleic Acids Res 2010, , 38 Database: D355-360.
2. Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, Kaipa P,
Karthikeyan AS, Kothari A, Krummenacker M, et al: The MetaCyc database
of metabolic pathways and enzymes and the BioCyc collection of
pathway/genome databases. Nucleic Acids Res 2010, , 38 Database:
D473-479.
3. Finn RD, Mistry J, Tate J, Coggill P, Heger A, Pollington JE, Gavin OL,
Gunasekaran P, Ceric G, Forslund K, et al: The Pfam protein families
database. Nucleic Acids Res 2010, , 38 Database: D211-222.
4. Huang da W, Sherman BT, Lempicki RA: Systematic and integrative
analysis of large gene lists using DAVID bioinformatics resources. Nat
Protoc 2009, 4(1):44-57.
5. Ingenuity Pathway Analysis (IPA), Ingenuity Systems. [http://www.
ingenuity.com].
6. Merchant SS, Prochnik SE, Vallon O, Harris EH, Karpowicz SJ, Witman GB,
Terry A, Salamov A, Fritz-Laylin LK, Marechal-Drouard L, et al: The
Chlamydomonas genome reveals the evolution of key animal and plant
functions. Science 2007, 318(5848):245-250.
7. Derelle E, Ferraz C, Rombauts S, Rouze P, Worden AZ, Robbens S,
Partensky F, Degroeve S, Echeynie S, Cooke R, et al: Genome analysis of
the smallest free-living eukaryote Ostreococcus tauri unveils many
unique features. Proc Natl Acad Sci USA 2006, 103(31):11647-11652.
8. Palenik B, Grimwood J, Aerts A, Rouze P, Salamov A, Putnam N, Dupont C,
Jorgensen R, Derelle E, Rombauts S, et al: The tiny eukaryote Ostreococcus
provides genomic insights into the paradox of plankton speciation. Proc
Natl Acad Sci USA 2007, 104(18):7705-7710.
9. Worden AZ, Lee JH, Mock T, Rouze P, Simmons MP, Aerts AL, Allen AE,
Cuvelier ML, Derelle E, Everett MV, et al: Green evolution and dynamic
adaptations revealed by genomes of the marine picoeukaryotes
Micromonas. Science 2009, 324(5924):268-272.
10. Blanc G, Duncan G, Agarkova I, Borodovsky M, Gurnon J, Kuo A, Lindquist E,
Lucas S, Pangilinan J, Polle J, et al: The Chlorella variabilis NC64A genome
reveals adaptation to photosymbiosis, coevolution with viruses, and
cryptic sex. Plant Cell 2010, 22(9):2943-2955.
11. Chlamydomonas reinhardtii v4.0, Joint Genome Institute. [http://genome.
jgi-psf.org/Chlre4/].
12. Rupprecht J: From systems biology to fuel–Chlamydomonas reinhardtii
as a model for a systems biology approach to improve biohydrogen
production. J Biotechnol 2009, 142(1):10-20.
13. Grossman AR, Croft M, Gladyshev VN, Merchant SS, Posewitz MC,
Prochnik S, Spalding MH: Novel metabolism in Chlamydomonas through
the lens of genomics. Curr Opin Plant Biol 2007, 10(2):190-198.
14. Beer LL, Boyd ES, Peters JW, Posewitz MC: Engineering algae for
biohydrogen and biofuel production. Curr Opin Biotechnol 2009,
20(3):264-271.
15. Ghirardi ML, Dubini A, Yu J, Maness PC: Photobiological hydrogen-
producing systems. Chem Soc Rev 2009, 38(1):52-61.
16. Hemschemeier A, Melis A, Happe T: Analytical approaches to
photobiological hydrogen production in unicellular green algae.
Photosynth Res 2009.
17. Finazzi G, Moreau H, Bowler C: Genomic insights into photosynthesis in
eukaryotic phytoplankton. Trends Plant Sci 2010, 15(10):565-572.
18. Kruse O, Hankamer B: Microalgal hydrogen production. Curr Opin
Biotechnol 2010, 21(3):238-243.
19. Scott SA, Davey MP, Dennis JS, Horst I, Howe CJ, Lea-Smith DJ, Smith AG:
Biodiesel from algae: challenges and prospects. Curr Opin Biotechnol
2010, 21(3):277-286.
20. Radakovits R, Jinkerson RE, Darzins A, Posewitz MC: Genetic engineering
of algae for enhanced biofuel production. Eukaryot Cell 2010,
9(4):486-501.
21. Eberhard S, Finazzi G, Wollman FA: The dynamics of photosynthesis. Annu
Rev Genet 2008, 42:463-515.
22. Rochaix JD: Genetics of the biogenesis and dynamics of the
photosynthetic machinery in eukaryotes. Plant Cell 2004, 16(7):1650-1660.
23. Harris EH: Chlamydomonas as a model organism. Annu Rev Plant Physiol
Plant Mol Biol 2001, 52:363-406.
24. Marshall WF: Basal bodies platforms for building cilia. Curr Top Dev Biol
2008, 85:1-22.
25. Scholey JM, Anderson KV: Intraflagellar transport and cilium-based
signaling. Cell 2006, 125(3):439-442.
26. Gonzalez-Ballester D, Casero D, Cokus S, Pellegrini M, Merchant SS,
Grossman AR: RNA-seq analysis of sulfur-deprived Chlamydomonas cells
reveals aspects of acclimation critical for cell survival. Plant Cell 2010,
22(6):2058-2084.
Page 9 of 10

27. Miller R, Wu G, Deshpande RR, Vieler A, Gartner K, Li X, Moellering ER,
Zauner S, Cornish AJ, Liu B, et al: Changes in transcript abundance in
Chlamydomonas reinhardtii following nitrogen deprivation predict
diversion of metabolism. Plant Physiol 2010, 154(4):1737-1752.
28. Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B,
Garapati P, Hemish J, Hermjakob H, Jassal B, et al: Reactome
knowledgebase of human biological pathways and processes. Nucleic
Acids Res 2009, , 37 Database: D619-622.
29. Chlamydomonas reinhardtii v3.0, Joint Genome Institute. [http://genome.
jgi-psf.org/Chlre3/].
30. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R,
Diemer K, Muruganujan A, Narechania A: PANTHER: a library of protein
families and subfamilies indexed by function. Genome Res 2003,
13(9):2129-2141.
31. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP,
Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the
unification of biology. The Gene Ontology Consortium. Nat Genet 2000,
25(1):25-29.
32. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P,
Das U, Daugherty L, Duquenne L, et al: InterPro: the integrative protein
signature database. Nucleic Acids Res 2009, , 37 Database: D211-215.
33. Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J,
Muller LA, Rhee SY, Stitt M: MAPMAN: a user-driven tool to display
genomics data sets onto diagrams of metabolic pathways and other
biological processes. Plant J 2004, 37(6):914-939.
34. Huang da W, Sherman BT, Lempicki RA: Bioinformatics enrichment tools:
paths toward the comprehensive functional analysis of large gene lists.
Nucleic Acids Res 2009, 37(1):1-13.
35. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV,
Krylov DM, Mazumder R, Mekhedov SL, Nikolskaya AN, et al: The COG
database: an updated version includes eukaryotes. BMC Bioinformatics
2003, 4:41.
doi:10.1186/1471-2105-12-282
Cite this article as: Lopez et al.: Algal Functional Annotation Tool: a
web-based analysis suite to functionally interpret large gene lists using
integrated annotation and expression data. BMC Bioinformatics 2011
12:282.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit
Page 10 of 10

Algal Functional Annotation Tool

More Related Content

Similar to Algal Functional Annotation Tool (20)

More from Sarah Adams (20)

Recently uploaded (20)

Algal Functional Annotation Tool