# Blast2GO: A Comprehensive Suite for Functional Analysis in Plant Genomics

Authors: Ana Conesa; Stefan Götz
Journal: International Journal of Plant Genomics
 (2008)

---

## Abstract

Functional annotation of novel sequence data is a primary requirement for the utilization of functional genomics approaches in plant research. In this paper, we describe the Blast2GO suite as a comprehensive bioinformatics tool for functional annotation of sequences and data mining on the resulting annotations, primarily based on the gene ontology (GO) vocabulary. Blast2GO optimizes function transfer from homologous sequences through an elaborate algorithm that considers similarity, the extension of the homology, the database of choice, the GO hierarchy, and the quality of the original annotations. The tool includes numerous functions for the visualization, management, and statistical analysis of annotation results, including gene set enrichment analysis. The application supports InterPro, enzyme codes, KEGG pathways, GO direct acyclic graphs (DAGs), and GOSlim. Blast2GO is a suitable tool for plant genomics research because of its versatility, easy installation, and friendly use.


---

## Body

## 1. Introduction

Functional genomics research has expanded enormously
in the last decade and particularly the plant biology research community has
extensively included functional genomics approaches in their recent research
proposals. The number of Affymetrix plant GeneChips, for example, has doubled
in the last two years [1] and extensive international genomics consortia exist
for major crops (see last PAG Conference reports for an updated impression on
current plant genomics, http://www.intl-pag.org). Not less importantly, many middle-sized
research groups are also setting up plant EST projects and producing custom
microarray platforms [2]. This massive generation of plant sequence data and
rapid spread of functional genomics technologies among plant research labs has
created a strong demand for bioinformatics resources adapted to vegetative
species. Functional annotation of novel plant DNA sequences is probably one of
the top requirements in plant functional genomics as this holds, to a great
extent, the key to the biological interpretation of experimental results.
Controlled vocabularies have imposed along the way as the strategy of choice
for the effective annotation of the function of gene products. The use of
controlled vocabularies greatly facilitates the exchange of biological
knowledge and the benefit from computational resources that manage this
knowledge. The gene ontology (GO, http://www.geneontology.org)
[3] is probably the most extensive scheme today for the description of gene
product functions but also other systems such as enzyme codes [4], KEGG
pathways [5], FunCat [6], or COG [7] are widely used within
molecular databases. Many bioinformatics tools and
methods have been developed to assist in the assignment of functional terms to
gene products (reviewed in [8]). Fewer resources, however, are available when
it comes to the large-scale functional annotation of novel sequence data of
nonmodel species, as would be specifically required in many plant functional
genomics projects. Web-based tools for the functional annotation of new
sequences include AutoFact [9], GOanna/AgBase [10], GOAnno [11], Goblet [12],
GoFigure + GoDel [13], GoPET [14], Gotcha [15], HT-GO-FAT
(liru.ars.usda.gov/ht-go-fat.htm), InterProScan [16], JAFA [17], OntoBlast [18],
and PFP [19]. Additionally, functional annotation capabilities are usually
incorporated in EST analysis pipelines. A few relevant examples are
ESTExplorer, ESTIMA, ESTree. or JUICE (see [2] for a survey in EST
analysis).  These resources are valuable
tools for the assignment of functional terms to uncharacterized sequences but
usually lack high-throughput and data mining capabilities, in the first case,
or provide automatic solutions without much user interactivity, in the second. In this
paper, we describe the Blast2GO (B2G, www.blast2go.org) application for the
functional annotation, management, and data mining of novel sequence data
through the use of common controlled vocabulary schemas. The philosophy behind
B2G development was the creation of an extensive, user-friendly, and
research-oriented framework for large-scale function assignments. The main
application domain of the tool is the functional genomics of nonmodel organisms
and it is primarily intended to support research in experimental labs where
bioinformatics support may not be strong. Since its release in September 2005
[20], more than 100 labs worldwide have become B2G users and the application
has been referenced in over thirty peer-reviewed publications
(www.blast2go.org/citations). Although B2G has a broad species application
scope, the project originated in a crop genomics research environment and there
is quite some accumulated experience in the use of B2G in plants, which
includes maize, tobacco, citrus, Soybean, grape, or tomato. Projects range from
functional assignments of ESTs [21–24] to GO term annotation of custom or
commercial plant microarrays [25, 26], functional profiling studies [27–29], and
functional characterization of specific plant gene families [30, 31].In the following sections we will explain more
extensively the concepts behind Blast2GO. We will describe in detail main
functionalities of the application and show a use case that illustrates the
applicability of B2G to plant functional genomics research.


## 2. Blast2GO Highlights

Four main driving concepts form the foundation of
the Blast2GO software: biology orientation, high-throughput, annotation
flexibility,and data-mining capability.Biology orientation. The target users of
Blast2GO are biology researchers working on functional genomics projects in
labs where strong bioinformatics support is not necessarily present. Therefore,
the application has been conceived to be easy to install, to have minimal setup
and maintenance requirements, and to offer an intuitive user interface. B2G has
been implemented as a multiplatform Java desktop application made accessible by
Java Webstart technology. This solution employs the higher versatility of a
locally running application while assuring automatic updates provided that an
internet connection is available. This implementation has proven to work very
efficiently in the fast transfer to users of new functionalities and for bug
fixes.  Furthermore, access to data in
B2G is reinforced by graphical parameters that on one hand allow the easy
identification and selection of sequences at various stages of the annotation
process and, on the other hand, permit the joint visualization of annotation
results and highlighting of most relevant features.High-throughput while interactive. Blast2GO strives to
be the application of choice for the annotation of novel sequences in
functional genomics projects where thousands of fragments need to be
characterized. In principle, B2G accepts any amount of records within the
memory resources of the user's work station. Typical data files of 20 to 30
thousand sequences can be easily annotated on a 2 Giga RAM PC (larger projects
may use the graphical interface free version of Blast2GO). During the
annotation process, intermediate results can be accessed and modified by the
user if desired.Flexible annotation. Functional
annotation in Blast2GO is based on homology transfer. Within this framework,
the actual annotation procedure is configurable and permits the design of
different annotation strategies. Blast2GO annotation parameters include the
choice of search database, the strength and number of blast results, the
extension of the query-hit match, the quality of the transferred annotations,
and the inclusion of motif annotation. Vocabularies supported by B2G are gene
ontology terms, enzyme codes (EC), InterPro IDs, and KEGG pathways.Data mining on annotation results. Blast2GO is not a
mere generator of functional annotations. The application includes a wide range
of statistical and graphical functions for the evaluation of the annotation
procedure and the final results. Especially, (relative) abundance of functional
terms can be easily assessed and visualized.The first release of B2G covered basic application
functionalities: high-throughput blast against NCBI or local databases,
mapping, annotation, and gene set enrichment analysis; scalar vector graphics (SVG)
combined graphs and basic distributions charts. Enhanced modules for massive
blast, modification of annotation intensity, curation, additional vocabularies,
high-performing customizable graphs and pathway charts, data mining and
sequence handling, as well as a wide array of input and output formats have
been incorporated into the Blast2GO suite.


## 3. The Blast2GO Application

Figure1 shows the basic components of the Blast2GO
suite. Functional assignments proceed through an elaborate annotation procedure
that comprises a central strategy plus refinement functions. Next,
visualization and data mining engines permit exploiting the annotation results
to gain functional knowledge.Figure 1
Schematic representation of Blast2GO application. GO annotations are generated through a 3-step process: blast,
mapping, annotation. InterPro terms are obtained from InterProScan at EBI, converted and merged to GOs. GO annotation can be
modulated from Annex, GOSlim web services and manual editing. EC and KEGG annotations are generated from GO. Visual tools
include sequence color code, KEGG pathways, and GO graphs with node highlighting and filtering options. Additional annotation
data-mining tools include statistical charts and gene set enrichment analysis functions.


### 3.1. The Annotation Procedure

The Blast2GO annotation procedure consists of three
main steps: blast to find homologous sequences, mapping to collect GO terms
associated to blast hits, and annotation to assign trustworthy information to
query sequences. Once GO terms have been gathered, additional functionalities
enable processing and modification of annotation results.Blast step. The first step in
B2G is to find sequences similar to a query set by blast [32]. B2G accepts
nucleotide and protein sequences in FASTA format and supports the four basic blast programs (blastx,
blastp, blastn, and tblastx). Homology searches can be launched against public
databases such as (the) NCBI nr using a query-friendly version of blast
(QBlast). This is the default option and in this case, no additional
installations are needed. Alternatively, blast can be run locally against a
proprietary FASTA-formatted database, which requires a working www-blast
installation. The Make Filtered Blast-GO-BD function in the Tools menu allows
the creation of customized databases containing only GO-annotated entries,
which can be used in combination with the local blast option. Other
configurable parameters at the blast step are the expectation value (e-value)
threshold, the number of retrieved hits, and the minimal alignment length (hsp length)
which permits the exclusion of hits with short, low e-value matches from the
sources of functional terms. Annotation, however, will ultimately be based on
sequence similarity levels as similarity percentages are independent of
database size and more intuitive than e-values. Blast2GO parses blast results
and presents the information for each sequence in table format. Query sequence
descriptions are obtained by applying a language processing algorithm to hit
descriptions, which extracts informative names and avoids low-content terms
such as “hypothetical protein” or “expressed protein”.Mapping step. Mapping is the
process of retrieving GO terms associated to the hits obtained after a blast search.
B2G performs three different mappings as follows. (1) Blast result accessions are
used to retrieve gene names (symbols) making use of two mapping files provided
by NCBI (geneinfo, gene2accession). Identified gene names are searched in the species-specific
entries of the gene product table of the GO database. (2) Blast result GI identifiers
are used to retrieve UniProt IDs making use of a mapping file from PIR
(Non-redundant Reference Protein database) including PSD, UniProt, Swiss-Prot,
TrEMBL, RefSeq, GenPept, and PDB. (3) Blast result accessions are searched
directly in the DBXRef Table of the GO database.Annotation step. This is the process
of assigning functional terms to query sequences from the pool of GO terms
gathered in the mapping step. Function assignment is based on the gene ontology
vocabulary. Mapping from GO terms to enzyme codes permits the subsequent
recovery of enzyme codes and KEGG pathway annotations. The B2G annotation
algorithm takes into consideration the similarity between query and hit
sequences, the quality of the source of GO assignments, and the structure of
the GO DAG. For each query sequence and each candidate GO term, an annotation
score (AS) is computed (see Figure 2). The AS is composed of two terms. The
first, direct term (DT), represents the highest similarity value among the hit
sequences bearing this GO term, weighted by a factor corresponding to its evidence
code (EC). A GO term EC is present for every annotation in the GO database to
indicate the procedure of functional assignment. ECs vary from experimental
evidence, such as inferred by direct assay (IDA) to unsupervised assignments
such as inferred by electronic annotation (IEA).  The second term (AT) of the annotation rule
introduces the possibility of abstraction into the annotation algorithm.
Abstraction is defined as the annotation to a parent node when several child
nodes are present in the GO candidate pool. This term multiplies the number of
total GOs unified at the node by a user-defined factor or GO weight (GOw) that
controls the possibility and strength of abstraction. When all ECw’s are set to
1 (no EC control) and the GOw is set to 0 (no abstraction is possible), the
annotation score of a given GO term equals the highest similarity value among
the blast hits annotated with that term. If the ECw is smaller than one, the DT
decreases and higher query-hit similarities are required to surpass the
annotation threshold. If the GOw is not equal to zero, the AT becomes
contributing and the annotation of a parent node is possible if multiple child
nodes coexist that do not reach the annotation cutoff. Default values of B2G
annotation parameters were chosen to optimize the ratio between annotation
coverage and annotation accuracy [20]. Finally, the AR selects the lowest terms
per branch that exceed a user-defined threshold.Figure 2
Blast2GO annotation rule.The annotation step in B2G can be further adjusted
by setting additional filters to the hit sequences considered as annotation
source. A lower limit can be set at the e-value parameter to ensure a minimum
confidence at the level of homology. Similarly, %“hit” filter has been implemented to assure
that a given percentage of the hit sequence is actually spanned by the query.
This parameter is of importance to prevent potential function transfer from
nonmatching sequence regions of modular proteins. Additionally, the minimal hsp
length required at the blast step permits control of the length of the matching
region.


### 3.2. Modulation of Annotation

Blast2GO includes different functionalities to
complete and modify the annotations obtained through the above-defined
procedure.Additional vocabularies. Enzyme codes and
KEGG pathway annotations are generated from the direct mapping of GO terms to
their enzyme code equivalents. Additionally, Blast2GO offers InterPro searches
directly from the B2G interface. The user, identified by his/her email address,
has the possibility of selecting different databases available at the
InterProEBI web server [33]. B2G launches sequence queries in batch, and
recovers, parses, and uploads InterPro results. Furthermore, InterPro IDs can
be mapped to GO terms and merged with blast-derived GO annotations to provide
one integrated annotation result. In this process, B2G ensures that only the
lowest term per branch remains in the final annotation set, removing possible
parent-child relationships originating from the merging action.Annotation fine-tuning. Blast2GO
incorporates three additional functionalities for the refinement of annotation
results. Firstly, the Annex function allows annotation augmentation through the
Second Layer concept developed by The Norwegian University of Science and
Technology (http://www.goat.no, [34]). Basically, the Second Layer database is
a collection of manually curated univocal relationships between GO terms from
the different GO categories that permits the inference of biological process
and cellular component terms from molecular function annotations. Up to 15% of
annotation increase and around 30% of GO term confirmations are obtained
through the Annex dataset [20]. Secondly, annotation results can be summarized
through GOSlim mapping. GOSlim consists of a subset of the gene ontology vocabulary
encompassing key ontological terms and a mapping function between the full GO
and the GOSlim. Different GOSlim mappings are available, adapted to specific
biological domains. At present, GOSlim mappings for plant, yeast, from GOA and
Tair, as well as a
generic one are available from the GO through Blast2GO. Thirdly, the manual
curation function means that the user has the possibility of editing annotation
results and manually modifying GO terms and sequence descriptors.


### 3.3. Visualization and Data Mining

One aspect of the uniqueness of the Blast2GO
software is the availability of a wide array of functions to monitor, evaluate,
and visualize the annotation process and results. The purpose of these
functions is to help understand how functional annotation proceeds and to
optimize performance.Statistical charts. Summary statistics
charts are generated after each of the annotation steps. Distribution plots for
e-value and similarity within blast results give an idea of the degree of
homology that query sequences have in the searched database.
Once mapping has been completed, the user can check the distribution of evidence codes in the
recovered GO terms and the original database sources of annotations. These
charts give an indication of suitable values for B2G annotation parameters. For
example, when a good overall level of sequence similarity is obtained for the
dataset, the default annotation cutoff value could be raised to improve
annotation accuracy. Similarly, if evidence code charts indicate a low
representation of experimentally derived GOs, the user might choose to increase
the weight given to electronic annotations. After the final annotation step,
new charts show the distribution of annotated sequences, the number of GOs per
sequence, the number of sequences per GO, and the distribution of annotations
per GO level, which jointly provide a general overview of the performance of
the annotation procedure.Sequence coloring. The visual approach
of B2G is further represented by the color code given to annotated sequences.
During the annotation process, the background color of active sequences changes
according to their analysis status. Nonblasted sequences are displayed in white
and change to light red once a positive blast result is obtained. If the result
was negative, they will stay dark red. Mapped sequences are depicted in green
while annotated sequences become blue. Finally, manually curated sequences can
be labeled and colored purple (see Figure 3(A)). Sequence coloring is a simple
and effective way of identifying sequences that have reached differential
stages during the annotation process. Furthermore, sequences can be selected by
their color. This is a very useful function for the interactive use of the
application. For example, sequences that stayed dark red after blast (no
positive result) can be selected to be launched to InterProScan. Sequences that
remained green (mapping code) after the annotation step can be selected and
reannotated with more permissive parameters.Figure 3
Blast2GO user interface. (A) Main sequence table showing sequence
color codes. (B) Graphical tab showing a combined graph with score
highlighting.Combined graph. A core functionality
of Blast2GO is the joint visualization of groups of GO terms within the
structure of the GO DAG.  The combined
graph function is typically used to study the collective biological meaning of
a set of sequences. Combined graphs are a good alternative to enrichment
analysis (see below) where no reference set is to be considered or the number
of involved sequences is low. B2G includes several parameters to make these combined
graphs easy to analyze and navigate. Firstly, the ZWF format [35], a powerful
scalable vector graphics engine, has been adopted to make zooming and browsing
through the DAG fast and light. Secondly, annotation-rich areas of the
generated DAG can be readily spotted by a node-coloring function. B2G colors
nodes either by the number of sequences gathered at that term (additive
function) or by a node information score (exponential function, ∑GOsseq⋅αdist)  that considers the
places of direct annotation. This B2G score takes into account the amount of
sequences collected at a given term but penalizes by the distance to the node
of actual annotation [20]. The B2G score has shown to be a useful parameter for
the identification of “hot” terms within a specific DAG (see Figure 3(B);
Conesa, unpublished). Thirdly, the extension and density of the plotted DAG can
be modulated by a node filter function. When the number of sequences involved
in the combined graph is large, the resulting DAG can be too big to be
practical. B2G permits filtering out of low informative terms by imposing a
threshold on the number of annotated sequences or B2G scores for a node to be
displayed. In this case, the number of omitted nodes is given for each branch,
which is an indication of the level of local compression applied to that
branch.Enrichment analysis. A typical data
mining approach applied in functional genomics research is the identification
of functional classes that statistically differ between two lists of terms. For
example, one might want to know the functional categories that are over- or
underrepresented in the set of differentially expressed genes of a microarray
experiment, or it could be of interest to find which functions are distinctly
represented between different libraries of an EST collection. Blast2GO has
integrated the Gossip [36] package for statistical assessment of differences in
GO term abundance between two sets of sequences. This package employs the
Fisher's exact test and corrects for multiple testing. For this analysis, the
involved sequences with their annotations must be loaded in the application.
B2G returns the GO terms under- or overrepresented at a specified significance value.
Results are given as a plain table and graphically as a bar chart and as a DAG
with nodes colored by their significance value. Also in this case, graph
pruning and summarizing functions are available.


### 3.4. Other Functionalities

Next to the annotation and data mining functions,
Blast2GO comprises a number of additional functionalities to handle data. In
this section, we briefly comment on some of them.Import and export.  B2G provides
different formats for the exchange of data. Typically, B2G inputs are FASTA-formatted
sequences and returns a tab-delimited file with GO annotations. Other supported
output formats are GOstats and GOSpring. Furthermore, B2G also accepts blast results
in xml format. This option permits skipping the first step of the B2G annotation
procedure when a blast result is already present. Similarly, when accession IDs
or gene symbols are known for the query sequences, these can be directly
uploaded in B2G and the application will query the B2G database for their
annotations. Moreover, the Main Sequence table (see Figure 3) can be saved to a
file at any moment to store intermediate results. Finally, graph and enrichment
analysis results are presented both graphically and as text files.Validation. The “true path
rule” defined by the gene ontology consortium for the GO DAG assures that
all the terms in the pathway from a term up to the root must always be true for
a given gene product. The B2G annotation validation function applies this
property to annotation results by removing any parent term that has a child
within the sequence annotation set. B2G always executes validation after any
modification has been made to the existing annotation, for exaple, after
InterPro merging, Annex augmentation, or manual curation.Comparison of two sets of GO terms. Given two annotation results, Blast2GO can compare their implicit DAG
structures. B2G computes the number of identical nodes, more general and more
specific terms within the same branch, and terms located to different branches
or different GO main categories. Comparison is directional; this means that the
active annotation file is contrasted to a reference or external one. Each GO
term is compared to all terms in the reference set and the best matching
comparison result is recorded. Once a term is matched, it is removed from the
query set.


### 3.5. Some Performance Figures

The annotation accuracy of Blast2GO has been
evaluated by comparing B2G GO annotation results to the existing annotation in
a set of manually annotated Arabidopsis proteins that had been previously
removed from the nr database. This evaluation indicated that using B2G default
parameters, nearly 70% of identical branch recovery was achievable, which is at
the top end of the methods that are based on homology search [20]. More recent
evaluations have shown that Blast2GO annotation behavior is consistent across
species and datasets. In general, the blast step has shown to be decisive in
the annotation coverage. For a great deal of sequences with a positive blast
result, functional
information is available in the GO database and the final annotation success is
related to the length and quality of the query sequence and the strictness of
annotation parameters. Typically and using default parameters, around 50–60%
of annotation success is common for EST datasets and slightly higher values are
obtained for full-length proteins (Table 1).Table 1
Blast2GO performance figures of seven cDNA
datasets. FE: percentage of sequences with some functional evidence (Mapping or
InterProScan positive). BA: percentage of blast-based annotated sequences, #GO:
number of GOs per sequence. GOL: mean GO level. IP: percentage of annotation
increase by InterProScan. Ann: percentage of annotation increase by Annex. TA: total
percentage of annotated sequences (including blast and InterPro). Datasets are
described in [37].
DataSetFEBAno. GOsGOLIPAnnTAC. clementina70.258.24.45.107.911.862.3M. incog70.755.75.64.9511.89.963.9T. harzianum61.147.73.65.2714.416.253.4G. max61.851.14.35.116.111.853.5P. flesus50.134.45.25.0721.910.645.1A. phagocytophilum56.642.53.04.9135.420.949.1Whale metagenome69.550.73.04.4517.61858.8On average, between 3 and 6 GO terms are assigned
per sequence at a mean GO level very close to 5. InterPro, Annex, and GOw
annotation parameters significantly increase annotation intensity—around 15%—and validate
annotation results. Furthermore, default annotation options tend to provide
coherent results and resemble the functional assignment obtained by a human
computational reviewed analysis [37].


### 3.6. Use Case

In this section, we present a typical use case of
Blast2GO to illustrate the major application features described in the previous
sections. We will address the functional annotation of the Soybean Affymetrix
GeneChip. The GeneChip Soybean Genome Array targets over 37,500 Soybean
transcripts (www.affymetrix.com). The array also contains transcripts for
studying two pathogens important for Soybean research. Sequence data and a
detailed annotation sheet for the Soybean Genome Array are provided at the
Blast2GO site (http://blast2go.bioinfo.cipf.es/b2gdata/soybean).Blast
Sequence data in FASTA format were uploaded into the
application from the menu File→ Open File. After selecting the Blast menu, a
dialog opens where we can indicate the parameters for the blast step. In our
case, the easiest option is to select the nr protein database and perform blast
remotely on the NCBI server through Qblast. Additional blast parameters are
kept at default values: e-value threshold of 1e-3 and a recovery of 20 hits per
sequence. These permissive values are chosen to retrieve a large amount of
information at this first time-consuming step. Annotation stringency will be decided
later in the annotation procedure. Furthermore, we set the hsp filter to 33 to
avoid hits where the length of the matching region is smaller than 100
nucleotides. After launching, blast sequences turn red as results arrive, up to
a total of 22,788. Once blast is completed, we can visualize different charts
(similarity, e-value, and species distributions, see supplementary
material available online at
doi:10.1155/2008/619832)
to get an impression of the quality of the query sequences and the blast procedure.
For example, Statistics
→ Blast statistics →
Similarity distribution chart (see Figure 4) shows that most sequences have
blast similarity values of 50–60% or higher. This information is useful for
choosing the annotation cutoff parameter at the annotation step, and suggests
that taking a value of 60 would be adequate. Furthermore, the Species distribution
chart (see Figure 5) shows a great majority of Arabidopsis sequences within the
blast hits, followed by Cotton, Medicago, Glycine, and Nicotiana.Figure 4
Similarity distribution of Soybean GeneChip. Similarity is computed of each query-hot pair as the sum of similarity
values for all matching hsps.Figure 5
Species distribution chart of Soybean GeneChip after blastx to NCBI nr.Mapping
Mapping is a nonconfigurable option launched from
the menu Mapping→ Make Mapping. GO terms could be found for 21,079 sequences
(56%). Mapping charts (menu Statistics → Mapping Statistics) permit the
evaluation of mapping results. The evidence code distribution chart (see Figure 6) shows an overrepresentation of electronic annotations, although other
nonautomatic codes, such as review by computational analysis (RCA), inferred by
mutant phenotype (IMP), or inferred by direct assay (IDA) are also well
represented. This suggests that an annotation strategy that promotes nonelectronic
ECs would be meaningful as it would benefit from the high-quality GO terms
without totally excluding electronic annotations. Therefore, the default EC
weights (menu Annotation → Set Evidence Code Weights) that adjust
proportionally to the reliability of the source annotation will be maintained
at the annotation step.Figure 6
Evidence code distribution chart of Soybean GeneChip after mapping to B2G database.Annotation
Taking into consideration the charts generated by
the previous steps, we have chosen an annotation configuration with ane-value
filter of 1e-6, default gradual EC weights, a GO weight of 15, and an
annotation cutoff of 60. This implies that only sequences with a blast e-value
lower than 1e-6 will be considered in the annotation formula, that the query-hit
similarity value adjusted by the EC weight of the GO term should be at least
60, and that abstraction is strongly promoted. This annotation configuration
resulted in 17,778 successfully GO annotated sequences with a total of 70,035
GO terms at a mean GO level (distance of the GO term to the ontology root term)
of 4.72. Furthermore, 6,345 enzyme codes were mapped to a total of 5,390
sequences. Once annotation has been completed, we can visualize the results at
each step of the annotation process (see Figure 7). Reannotation is possible by
selecting green or red
sequences (Tools → Select Sequences by Color) and rerunning blast, mapping, and
annotation with different, more permissive parameters. In this way, we obtain a
trustworthy annotation for most sequences and behave more permissively only for
those sequences which are hard to annotate. Other charts available at the
Annotation Statistics menu show the distribution of GO levels (see Figure 8),
the length of annotated sequences, and the histogram of GO term abundance.Figure 7
Annotation process results for Soybean Affymetrix GeneChip.Figure 8
GO level distribution chart for Soybean Affymetrix GeneChip. Most sequences have between 3 and 6 GO terms annotated.Annotation augmentation
Blast-based GO annotations can be increased by means
of the integrated InterProScan function available under Annotation→ Run InterProScan.
The user must provide his/her email address and select the motif databases of
interest. An InterProScan search against all EBI databases resulted in the
recovery of motif functional information for 11,347 sequences and a total of
8,046 GO terms. Once merged to the already existing annotation (Annotation →
Add InterProScan GOs to Annotation), 1,189 additional sequences were annotated
(see Figure 9). Once Blast plus InterProScan annotations have been gathered, a
useful step is to complete implicit annotations through the Annex function
(Annotation → Augment Annotation by Annex). After this step, it is recommended
to run the function to remove first-level annotations (under Annotation
menu).  In our use case, the Annex
function resulted in the addition of 8,125 new GO terms and a confirmation of
3,892 annotations, which is an average contribution of the Annex function [37].Figure 9
InterPro merging statistics. Red column: total number of GO
annotations before adding InterPro-based GO terms. Blue: total number of GO annotations after adding InterPro-based GO terms. Green: number of blast-based GO terms confirmed
after InterPro merging. Yellow: too general terms removed after InterPro merging.Manual curation
The manual annotation tool is a useful functionality
when information on the automatically generated annotation needs to be changed.
For example, the target of GmaAffx.69219.1.S1_at probe was found to be the
UDP-glycosyltransferase. The automatic procedure assigned GO terms metabolic
process (GO:0008152) and transferase activity, transferring hexosyl groups
(GO:0016758) to this sequence. However, as we are aware of the ER localization
of this enzyme and its involvement in protein maturation, we would like to add
this information to the existing annotation. The manual curation function is
available at the Sequence Menu which is displayed by mouse right button click
on the selected sequence. From this Menu, the blast and annotation results for
this particular sequence can be visualized. Selection of Change Annotations and
Description edits the annotation record of GmaAffx.69219.1.S1_at. We can now
type in the Annotations box the terms GO:0005783 (endoplasmic reticulum) and
GO:0006464 (protein modification process) and mark the manual annotation box.
The new annotations are then added and the sequence turns purple (manual
annotation color code).GOSlim
As the number of sequences and different GO terms in
the Soybean array is quite large, we are interested in a simpler representation
of the functional content of the data. An appropriate option is to map annotations
into a GOSlim. At Annotation→ Change to GOSlim View, we can select an
appropriate GOSlim (generic_plant) for this dataset. Upon completion of
slimming sequences acquire the yellow GOSlim coding. The original annotations
are stored and can be recovered at any moment. GOSlim mapping generated a set
of 105 different annotating GO terms on 18,820 sequences with a mean GO level
of 3.41. This means around 40 times less functional diversity than in the
original annotation (4533 different terms) and an increase of almost 2 levels
of the mean annotation depth.Combined graph
Once the slimmed annotation is obtained, we can
visualize the functional information of the Soybean Genome Array on the GO DAG.
This functionality is available under Analysis→ Combined Graph. At the Dialog
we must indicate the GO category to display (e.g., biological process). To
obtain a compact representation of the information, two filters can be applied.
For example, by setting the sequence filter to 20, only those nodes with at
least 20 sequence assignments will be displayed. By setting the score filter to
20, additionally, parent nodes that do not annotate more sequences than their
children terms will be omitted from the graph. Node coloring by score value
highlights the areas in the resulting DAG where sequence annotations are most
concentrated. Figure 10 shows the Combined Graph for the Molecular Function
Category. The two most intensively colored terms at the second GO level
indicate the two most abundant functional categories in the Soybean Chip:
catalytic activity and binding. Highlighting at lower levels reveals other,
most informative, highly represented functional terms, such as hydrolase
activity (level 3), kinase activity (level 4), transcription factor activity (level
3), protein binding (level 3), nucleotide binding (level 3), and transporter
activity (level 2). The reader is referred to the annotation sheet URL
(http://blast2go.bioinfo.cipf.es/b2gdata/soybean) for figure navigation.Figure 10
Molecular function combined graph of GOSlim annotation of the Soybean Affymetrix GeneChip.
Nodes are colored by score value.Enrichment analysis
The enrichment analysis function in B2G executes a
statistical assessment of differences in functional classes between two groups
of sequences. To illustrate this function, we have selected all sequences in
the Soybean chip which contain the word “membrane” within their description—132 sequences—and compared their annotations to the whole chip. We go to
Analysis→ Enrichment Analysis → Make Fisher's Exact Test and browse for a text
file containing the test set with the names of membrane sequences. As the
comparison is made against the complete microarray dataset loaded into the
application, no file needs to be selected as Reference. We uncheck the two-tail
box to perform only positive enrichment analysis.  Upon completion a table with test statistical
results is presented in the Statistics tab. This table contains significant GO
terms which are ranked according to their significance. Three different
significance parameters are given for false-positive control: false discovery
rate (FDR), family-wise error rate (FWER), and single test P-value
(Fisher P-value) (see [36] for details). By taking a FDR significance
threshold of 0.05, we obtain those functionalities that are strongly
significant for membrane proteins in the Soybean Chip. These refer to processes
related to transport, protein targeting, and photosynthesis as might be
expected for a plant species. Graphical representations of these results can be
generated at Analysis → Enrichment Analysis → Bar Chart and Analysis →
Enrichment Analysis → Make  Enriched
Graph. The Bar Chart shows, for each significant GO term, frequency differences
between the membrane and the whole chip datasets (see Figure 11). The Enriched
Graph shows the DAG of significant terms with a node-coloring proportional to
the significance value. This representation helps in understanding the
biological context of functional differences and to find pseudoredundancies in
the results—parent-child
relationships within significant terms—(see Figure 12).Figure 11
Bar chart for functional category enrichment analysis
of Soybean membrane proteins. TheY-axis
shows significantly enriched GO terms and the X-axis give the relative frequency of the term.
Red bars correspond to test set (membrane) and blue bars correspond to
the whole Soybean genome array.Figure 12
Enriched graph (biological process) of the Soybean membrane subset of sequences.  Node filter has been set
at FDR < 1e-6. Nodes are colored accordingly to their FDR value in the Fisher exact's test against the whole Soybean genome array.Export results
Once different analyses have been completed the data
can be exported in many different ways. The annotation format (menu File→ Export → Export
Annotations) is the default format for export/import in B2G and simply consists
of a tab-delimited file with two columns, one for sequences and other for
annotation IDs. Another useful export format is GeneSpring, for communication
with this interesting application, which consists of one row per sequence and
three different columns showing the descriptions of the GO terms at the three
main GO categories. Graphs can be saved in png format. Additionally, all
information contained in the Combined Graph can be generated as table
(including sequences, GO IDs, levels, and scores) and exported (Analysis →
Export Graph Information).
The analysis presented in this use case took about
15 days to complete. Four days were necessary to obtain the totality of 37,500
blast results from the NCBI while twelve days were required for the
InterProScan at the EBI web server. Mapping and Annotation were ready within a
few hours and one day was necessary to collect and evaluate charts. This shows
that with the adequate tools and some training, functional annotation of a
plant genome-wide sequence collection is in reach within a couple of weeks.


## 3.1. The Annotation Procedure

The Blast2GO annotation procedure consists of three
main steps: blast to find homologous sequences, mapping to collect GO terms
associated to blast hits, and annotation to assign trustworthy information to
query sequences. Once GO terms have been gathered, additional functionalities
enable processing and modification of annotation results.Blast step. The first step in
B2G is to find sequences similar to a query set by blast [32]. B2G accepts
nucleotide and protein sequences in FASTA format and supports the four basic blast programs (blastx,
blastp, blastn, and tblastx). Homology searches can be launched against public
databases such as (the) NCBI nr using a query-friendly version of blast
(QBlast). This is the default option and in this case, no additional
installations are needed. Alternatively, blast can be run locally against a
proprietary FASTA-formatted database, which requires a working www-blast
installation. The Make Filtered Blast-GO-BD function in the Tools menu allows
the creation of customized databases containing only GO-annotated entries,
which can be used in combination with the local blast option. Other
configurable parameters at the blast step are the expectation value (e-value)
threshold, the number of retrieved hits, and the minimal alignment length (hsp length)
which permits the exclusion of hits with short, low e-value matches from the
sources of functional terms. Annotation, however, will ultimately be based on
sequence similarity levels as similarity percentages are independent of
database size and more intuitive than e-values. Blast2GO parses blast results
and presents the information for each sequence in table format. Query sequence
descriptions are obtained by applying a language processing algorithm to hit
descriptions, which extracts informative names and avoids low-content terms
such as “hypothetical protein” or “expressed protein”.Mapping step. Mapping is the
process of retrieving GO terms associated to the hits obtained after a blast search.
B2G performs three different mappings as follows. (1) Blast result accessions are
used to retrieve gene names (symbols) making use of two mapping files provided
by NCBI (geneinfo, gene2accession). Identified gene names are searched in the species-specific
entries of the gene product table of the GO database. (2) Blast result GI identifiers
are used to retrieve UniProt IDs making use of a mapping file from PIR
(Non-redundant Reference Protein database) including PSD, UniProt, Swiss-Prot,
TrEMBL, RefSeq, GenPept, and PDB. (3) Blast result accessions are searched
directly in the DBXRef Table of the GO database.Annotation step. This is the process
of assigning functional terms to query sequences from the pool of GO terms
gathered in the mapping step. Function assignment is based on the gene ontology
vocabulary. Mapping from GO terms to enzyme codes permits the subsequent
recovery of enzyme codes and KEGG pathway annotations. The B2G annotation
algorithm takes into consideration the similarity between query and hit
sequences, the quality of the source of GO assignments, and the structure of
the GO DAG. For each query sequence and each candidate GO term, an annotation
score (AS) is computed (see Figure 2). The AS is composed of two terms. The
first, direct term (DT), represents the highest similarity value among the hit
sequences bearing this GO term, weighted by a factor corresponding to its evidence
code (EC). A GO term EC is present for every annotation in the GO database to
indicate the procedure of functional assignment. ECs vary from experimental
evidence, such as inferred by direct assay (IDA) to unsupervised assignments
such as inferred by electronic annotation (IEA).  The second term (AT) of the annotation rule
introduces the possibility of abstraction into the annotation algorithm.
Abstraction is defined as the annotation to a parent node when several child
nodes are present in the GO candidate pool. This term multiplies the number of
total GOs unified at the node by a user-defined factor or GO weight (GOw) that
controls the possibility and strength of abstraction. When all ECw’s are set to
1 (no EC control) and the GOw is set to 0 (no abstraction is possible), the
annotation score of a given GO term equals the highest similarity value among
the blast hits annotated with that term. If the ECw is smaller than one, the DT
decreases and higher query-hit similarities are required to surpass the
annotation threshold. If the GOw is not equal to zero, the AT becomes
contributing and the annotation of a parent node is possible if multiple child
nodes coexist that do not reach the annotation cutoff. Default values of B2G
annotation parameters were chosen to optimize the ratio between annotation
coverage and annotation accuracy [20]. Finally, the AR selects the lowest terms
per branch that exceed a user-defined threshold.Figure 2
Blast2GO annotation rule.The annotation step in B2G can be further adjusted
by setting additional filters to the hit sequences considered as annotation
source. A lower limit can be set at the e-value parameter to ensure a minimum
confidence at the level of homology. Similarly, %“hit” filter has been implemented to assure
that a given percentage of the hit sequence is actually spanned by the query.
This parameter is of importance to prevent potential function transfer from
nonmatching sequence regions of modular proteins. Additionally, the minimal hsp
length required at the blast step permits control of the length of the matching
region.


## 3.2. Modulation of Annotation

Blast2GO includes different functionalities to
complete and modify the annotations obtained through the above-defined
procedure.Additional vocabularies. Enzyme codes and
KEGG pathway annotations are generated from the direct mapping of GO terms to
their enzyme code equivalents. Additionally, Blast2GO offers InterPro searches
directly from the B2G interface. The user, identified by his/her email address,
has the possibility of selecting different databases available at the
InterProEBI web server [33]. B2G launches sequence queries in batch, and
recovers, parses, and uploads InterPro results. Furthermore, InterPro IDs can
be mapped to GO terms and merged with blast-derived GO annotations to provide
one integrated annotation result. In this process, B2G ensures that only the
lowest term per branch remains in the final annotation set, removing possible
parent-child relationships originating from the merging action.Annotation fine-tuning. Blast2GO
incorporates three additional functionalities for the refinement of annotation
results. Firstly, the Annex function allows annotation augmentation through the
Second Layer concept developed by The Norwegian University of Science and
Technology (http://www.goat.no, [34]). Basically, the Second Layer database is
a collection of manually curated univocal relationships between GO terms from
the different GO categories that permits the inference of biological process
and cellular component terms from molecular function annotations. Up to 15% of
annotation increase and around 30% of GO term confirmations are obtained
through the Annex dataset [20]. Secondly, annotation results can be summarized
through GOSlim mapping. GOSlim consists of a subset of the gene ontology vocabulary
encompassing key ontological terms and a mapping function between the full GO
and the GOSlim. Different GOSlim mappings are available, adapted to specific
biological domains. At present, GOSlim mappings for plant, yeast, from GOA and
Tair, as well as a
generic one are available from the GO through Blast2GO. Thirdly, the manual
curation function means that the user has the possibility of editing annotation
results and manually modifying GO terms and sequence descriptors.


## 3.3. Visualization and Data Mining

One aspect of the uniqueness of the Blast2GO
software is the availability of a wide array of functions to monitor, evaluate,
and visualize the annotation process and results. The purpose of these
functions is to help understand how functional annotation proceeds and to
optimize performance.Statistical charts. Summary statistics
charts are generated after each of the annotation steps. Distribution plots for
e-value and similarity within blast results give an idea of the degree of
homology that query sequences have in the searched database.
Once mapping has been completed, the user can check the distribution of evidence codes in the
recovered GO terms and the original database sources of annotations. These
charts give an indication of suitable values for B2G annotation parameters. For
example, when a good overall level of sequence similarity is obtained for the
dataset, the default annotation cutoff value could be raised to improve
annotation accuracy. Similarly, if evidence code charts indicate a low
representation of experimentally derived GOs, the user might choose to increase
the weight given to electronic annotations. After the final annotation step,
new charts show the distribution of annotated sequences, the number of GOs per
sequence, the number of sequences per GO, and the distribution of annotations
per GO level, which jointly provide a general overview of the performance of
the annotation procedure.Sequence coloring. The visual approach
of B2G is further represented by the color code given to annotated sequences.
During the annotation process, the background color of active sequences changes
according to their analysis status. Nonblasted sequences are displayed in white
and change to light red once a positive blast result is obtained. If the result
was negative, they will stay dark red. Mapped sequences are depicted in green
while annotated sequences become blue. Finally, manually curated sequences can
be labeled and colored purple (see Figure 3(A)). Sequence coloring is a simple
and effective way of identifying sequences that have reached differential
stages during the annotation process. Furthermore, sequences can be selected by
their color. This is a very useful function for the interactive use of the
application. For example, sequences that stayed dark red after blast (no
positive result) can be selected to be launched to InterProScan. Sequences that
remained green (mapping code) after the annotation step can be selected and
reannotated with more permissive parameters.Figure 3
Blast2GO user interface. (A) Main sequence table showing sequence
color codes. (B) Graphical tab showing a combined graph with score
highlighting.Combined graph. A core functionality
of Blast2GO is the joint visualization of groups of GO terms within the
structure of the GO DAG.  The combined
graph function is typically used to study the collective biological meaning of
a set of sequences. Combined graphs are a good alternative to enrichment
analysis (see below) where no reference set is to be considered or the number
of involved sequences is low. B2G includes several parameters to make these combined
graphs easy to analyze and navigate. Firstly, the ZWF format [35], a powerful
scalable vector graphics engine, has been adopted to make zooming and browsing
through the DAG fast and light. Secondly, annotation-rich areas of the
generated DAG can be readily spotted by a node-coloring function. B2G colors
nodes either by the number of sequences gathered at that term (additive
function) or by a node information score (exponential function, ∑GOsseq⋅αdist)  that considers the
places of direct annotation. This B2G score takes into account the amount of
sequences collected at a given term but penalizes by the distance to the node
of actual annotation [20]. The B2G score has shown to be a useful parameter for
the identification of “hot” terms within a specific DAG (see Figure 3(B);
Conesa, unpublished). Thirdly, the extension and density of the plotted DAG can
be modulated by a node filter function. When the number of sequences involved
in the combined graph is large, the resulting DAG can be too big to be
practical. B2G permits filtering out of low informative terms by imposing a
threshold on the number of annotated sequences or B2G scores for a node to be
displayed. In this case, the number of omitted nodes is given for each branch,
which is an indication of the level of local compression applied to that
branch.Enrichment analysis. A typical data
mining approach applied in functional genomics research is the identification
of functional classes that statistically differ between two lists of terms. For
example, one might want to know the functional categories that are over- or
underrepresented in the set of differentially expressed genes of a microarray
experiment, or it could be of interest to find which functions are distinctly
represented between different libraries of an EST collection. Blast2GO has
integrated the Gossip [36] package for statistical assessment of differences in
GO term abundance between two sets of sequences. This package employs the
Fisher's exact test and corrects for multiple testing. For this analysis, the
involved sequences with their annotations must be loaded in the application.
B2G returns the GO terms under- or overrepresented at a specified significance value.
Results are given as a plain table and graphically as a bar chart and as a DAG
with nodes colored by their significance value. Also in this case, graph
pruning and summarizing functions are available.


## 3.4. Other Functionalities

Next to the annotation and data mining functions,
Blast2GO comprises a number of additional functionalities to handle data. In
this section, we briefly comment on some of them.Import and export.  B2G provides
different formats for the exchange of data. Typically, B2G inputs are FASTA-formatted
sequences and returns a tab-delimited file with GO annotations. Other supported
output formats are GOstats and GOSpring. Furthermore, B2G also accepts blast results
in xml format. This option permits skipping the first step of the B2G annotation
procedure when a blast result is already present. Similarly, when accession IDs
or gene symbols are known for the query sequences, these can be directly
uploaded in B2G and the application will query the B2G database for their
annotations. Moreover, the Main Sequence table (see Figure 3) can be saved to a
file at any moment to store intermediate results. Finally, graph and enrichment
analysis results are presented both graphically and as text files.Validation. The “true path
rule” defined by the gene ontology consortium for the GO DAG assures that
all the terms in the pathway from a term up to the root must always be true for
a given gene product. The B2G annotation validation function applies this
property to annotation results by removing any parent term that has a child
within the sequence annotation set. B2G always executes validation after any
modification has been made to the existing annotation, for exaple, after
InterPro merging, Annex augmentation, or manual curation.Comparison of two sets of GO terms. Given two annotation results, Blast2GO can compare their implicit DAG
structures. B2G computes the number of identical nodes, more general and more
specific terms within the same branch, and terms located to different branches
or different GO main categories. Comparison is directional; this means that the
active annotation file is contrasted to a reference or external one. Each GO
term is compared to all terms in the reference set and the best matching
comparison result is recorded. Once a term is matched, it is removed from the
query set.


## 3.5. Some Performance Figures

The annotation accuracy of Blast2GO has been
evaluated by comparing B2G GO annotation results to the existing annotation in
a set of manually annotated Arabidopsis proteins that had been previously
removed from the nr database. This evaluation indicated that using B2G default
parameters, nearly 70% of identical branch recovery was achievable, which is at
the top end of the methods that are based on homology search [20]. More recent
evaluations have shown that Blast2GO annotation behavior is consistent across
species and datasets. In general, the blast step has shown to be decisive in
the annotation coverage. For a great deal of sequences with a positive blast
result, functional
information is available in the GO database and the final annotation success is
related to the length and quality of the query sequence and the strictness of
annotation parameters. Typically and using default parameters, around 50–60%
of annotation success is common for EST datasets and slightly higher values are
obtained for full-length proteins (Table 1).Table 1
Blast2GO performance figures of seven cDNA
datasets. FE: percentage of sequences with some functional evidence (Mapping or
InterProScan positive). BA: percentage of blast-based annotated sequences, #GO:
number of GOs per sequence. GOL: mean GO level. IP: percentage of annotation
increase by InterProScan. Ann: percentage of annotation increase by Annex. TA: total
percentage of annotated sequences (including blast and InterPro). Datasets are
described in [37].
DataSetFEBAno. GOsGOLIPAnnTAC. clementina70.258.24.45.107.911.862.3M. incog70.755.75.64.9511.89.963.9T. harzianum61.147.73.65.2714.416.253.4G. max61.851.14.35.116.111.853.5P. flesus50.134.45.25.0721.910.645.1A. phagocytophilum56.642.53.04.9135.420.949.1Whale metagenome69.550.73.04.4517.61858.8On average, between 3 and 6 GO terms are assigned
per sequence at a mean GO level very close to 5. InterPro, Annex, and GOw
annotation parameters significantly increase annotation intensity—around 15%—and validate
annotation results. Furthermore, default annotation options tend to provide
coherent results and resemble the functional assignment obtained by a human
computational reviewed analysis [37].


## 3.6. Use Case

In this section, we present a typical use case of
Blast2GO to illustrate the major application features described in the previous
sections. We will address the functional annotation of the Soybean Affymetrix
GeneChip. The GeneChip Soybean Genome Array targets over 37,500 Soybean
transcripts (www.affymetrix.com). The array also contains transcripts for
studying two pathogens important for Soybean research. Sequence data and a
detailed annotation sheet for the Soybean Genome Array are provided at the
Blast2GO site (http://blast2go.bioinfo.cipf.es/b2gdata/soybean).Blast
Sequence data in FASTA format were uploaded into the
application from the menu File→ Open File. After selecting the Blast menu, a
dialog opens where we can indicate the parameters for the blast step. In our
case, the easiest option is to select the nr protein database and perform blast
remotely on the NCBI server through Qblast. Additional blast parameters are
kept at default values: e-value threshold of 1e-3 and a recovery of 20 hits per
sequence. These permissive values are chosen to retrieve a large amount of
information at this first time-consuming step. Annotation stringency will be decided
later in the annotation procedure. Furthermore, we set the hsp filter to 33 to
avoid hits where the length of the matching region is smaller than 100
nucleotides. After launching, blast sequences turn red as results arrive, up to
a total of 22,788. Once blast is completed, we can visualize different charts
(similarity, e-value, and species distributions, see supplementary
material available online at
doi:10.1155/2008/619832)
to get an impression of the quality of the query sequences and the blast procedure.
For example, Statistics
→ Blast statistics →
Similarity distribution chart (see Figure 4) shows that most sequences have
blast similarity values of 50–60% or higher. This information is useful for
choosing the annotation cutoff parameter at the annotation step, and suggests
that taking a value of 60 would be adequate. Furthermore, the Species distribution
chart (see Figure 5) shows a great majority of Arabidopsis sequences within the
blast hits, followed by Cotton, Medicago, Glycine, and Nicotiana.Figure 4
Similarity distribution of Soybean GeneChip. Similarity is computed of each query-hot pair as the sum of similarity
values for all matching hsps.Figure 5
Species distribution chart of Soybean GeneChip after blastx to NCBI nr.Mapping
Mapping is a nonconfigurable option launched from
the menu Mapping→ Make Mapping. GO terms could be found for 21,079 sequences
(56%). Mapping charts (menu Statistics → Mapping Statistics) permit the
evaluation of mapping results. The evidence code distribution chart (see Figure 6) shows an overrepresentation of electronic annotations, although other
nonautomatic codes, such as review by computational analysis (RCA), inferred by
mutant phenotype (IMP), or inferred by direct assay (IDA) are also well
represented. This suggests that an annotation strategy that promotes nonelectronic
ECs would be meaningful as it would benefit from the high-quality GO terms
without totally excluding electronic annotations. Therefore, the default EC
weights (menu Annotation → Set Evidence Code Weights) that adjust
proportionally to the reliability of the source annotation will be maintained
at the annotation step.Figure 6
Evidence code distribution chart of Soybean GeneChip after mapping to B2G database.Annotation
Taking into consideration the charts generated by
the previous steps, we have chosen an annotation configuration with ane-value
filter of 1e-6, default gradual EC weights, a GO weight of 15, and an
annotation cutoff of 60. This implies that only sequences with a blast e-value
lower than 1e-6 will be considered in the annotation formula, that the query-hit
similarity value adjusted by the EC weight of the GO term should be at least
60, and that abstraction is strongly promoted. This annotation configuration
resulted in 17,778 successfully GO annotated sequences with a total of 70,035
GO terms at a mean GO level (distance of the GO term to the ontology root term)
of 4.72. Furthermore, 6,345 enzyme codes were mapped to a total of 5,390
sequences. Once annotation has been completed, we can visualize the results at
each step of the annotation process (see Figure 7). Reannotation is possible by
selecting green or red
sequences (Tools → Select Sequences by Color) and rerunning blast, mapping, and
annotation with different, more permissive parameters. In this way, we obtain a
trustworthy annotation for most sequences and behave more permissively only for
those sequences which are hard to annotate. Other charts available at the
Annotation Statistics menu show the distribution of GO levels (see Figure 8),
the length of annotated sequences, and the histogram of GO term abundance.Figure 7
Annotation process results for Soybean Affymetrix GeneChip.Figure 8
GO level distribution chart for Soybean Affymetrix GeneChip. Most sequences have between 3 and 6 GO terms annotated.Annotation augmentation
Blast-based GO annotations can be increased by means
of the integrated InterProScan function available under Annotation→ Run InterProScan.
The user must provide his/her email address and select the motif databases of
interest. An InterProScan search against all EBI databases resulted in the
recovery of motif functional information for 11,347 sequences and a total of
8,046 GO terms. Once merged to the already existing annotation (Annotation →
Add InterProScan GOs to Annotation), 1,189 additional sequences were annotated
(see Figure 9). Once Blast plus InterProScan annotations have been gathered, a
useful step is to complete implicit annotations through the Annex function
(Annotation → Augment Annotation by Annex). After this step, it is recommended
to run the function to remove first-level annotations (under Annotation
menu).  In our use case, the Annex
function resulted in the addition of 8,125 new GO terms and a confirmation of
3,892 annotations, which is an average contribution of the Annex function [37].Figure 9
InterPro merging statistics. Red column: total number of GO
annotations before adding InterPro-based GO terms. Blue: total number of GO annotations after adding InterPro-based GO terms. Green: number of blast-based GO terms confirmed
after InterPro merging. Yellow: too general terms removed after InterPro merging.Manual curation
The manual annotation tool is a useful functionality
when information on the automatically generated annotation needs to be changed.
For example, the target of GmaAffx.69219.1.S1_at probe was found to be the
UDP-glycosyltransferase. The automatic procedure assigned GO terms metabolic
process (GO:0008152) and transferase activity, transferring hexosyl groups
(GO:0016758) to this sequence. However, as we are aware of the ER localization
of this enzyme and its involvement in protein maturation, we would like to add
this information to the existing annotation. The manual curation function is
available at the Sequence Menu which is displayed by mouse right button click
on the selected sequence. From this Menu, the blast and annotation results for
this particular sequence can be visualized. Selection of Change Annotations and
Description edits the annotation record of GmaAffx.69219.1.S1_at. We can now
type in the Annotations box the terms GO:0005783 (endoplasmic reticulum) and
GO:0006464 (protein modification process) and mark the manual annotation box.
The new annotations are then added and the sequence turns purple (manual
annotation color code).GOSlim
As the number of sequences and different GO terms in
the Soybean array is quite large, we are interested in a simpler representation
of the functional content of the data. An appropriate option is to map annotations
into a GOSlim. At Annotation→ Change to GOSlim View, we can select an
appropriate GOSlim (generic_plant) for this dataset. Upon completion of
slimming sequences acquire the yellow GOSlim coding. The original annotations
are stored and can be recovered at any moment. GOSlim mapping generated a set
of 105 different annotating GO terms on 18,820 sequences with a mean GO level
of 3.41. This means around 40 times less functional diversity than in the
original annotation (4533 different terms) and an increase of almost 2 levels
of the mean annotation depth.Combined graph
Once the slimmed annotation is obtained, we can
visualize the functional information of the Soybean Genome Array on the GO DAG.
This functionality is available under Analysis→ Combined Graph. At the Dialog
we must indicate the GO category to display (e.g., biological process). To
obtain a compact representation of the information, two filters can be applied.
For example, by setting the sequence filter to 20, only those nodes with at
least 20 sequence assignments will be displayed. By setting the score filter to
20, additionally, parent nodes that do not annotate more sequences than their
children terms will be omitted from the graph. Node coloring by score value
highlights the areas in the resulting DAG where sequence annotations are most
concentrated. Figure 10 shows the Combined Graph for the Molecular Function
Category. The two most intensively colored terms at the second GO level
indicate the two most abundant functional categories in the Soybean Chip:
catalytic activity and binding. Highlighting at lower levels reveals other,
most informative, highly represented functional terms, such as hydrolase
activity (level 3), kinase activity (level 4), transcription factor activity (level
3), protein binding (level 3), nucleotide binding (level 3), and transporter
activity (level 2). The reader is referred to the annotation sheet URL
(http://blast2go.bioinfo.cipf.es/b2gdata/soybean) for figure navigation.Figure 10
Molecular function combined graph of GOSlim annotation of the Soybean Affymetrix GeneChip.
Nodes are colored by score value.Enrichment analysis
The enrichment analysis function in B2G executes a
statistical assessment of differences in functional classes between two groups
of sequences. To illustrate this function, we have selected all sequences in
the Soybean chip which contain the word “membrane” within their description—132 sequences—and compared their annotations to the whole chip. We go to
Analysis→ Enrichment Analysis → Make Fisher's Exact Test and browse for a text
file containing the test set with the names of membrane sequences. As the
comparison is made against the complete microarray dataset loaded into the
application, no file needs to be selected as Reference. We uncheck the two-tail
box to perform only positive enrichment analysis.  Upon completion a table with test statistical
results is presented in the Statistics tab. This table contains significant GO
terms which are ranked according to their significance. Three different
significance parameters are given for false-positive control: false discovery
rate (FDR), family-wise error rate (FWER), and single test P-value
(Fisher P-value) (see [36] for details). By taking a FDR significance
threshold of 0.05, we obtain those functionalities that are strongly
significant for membrane proteins in the Soybean Chip. These refer to processes
related to transport, protein targeting, and photosynthesis as might be
expected for a plant species. Graphical representations of these results can be
generated at Analysis → Enrichment Analysis → Bar Chart and Analysis →
Enrichment Analysis → Make  Enriched
Graph. The Bar Chart shows, for each significant GO term, frequency differences
between the membrane and the whole chip datasets (see Figure 11). The Enriched
Graph shows the DAG of significant terms with a node-coloring proportional to
the significance value. This representation helps in understanding the
biological context of functional differences and to find pseudoredundancies in
the results—parent-child
relationships within significant terms—(see Figure 12).Figure 11
Bar chart for functional category enrichment analysis
of Soybean membrane proteins. TheY-axis
shows significantly enriched GO terms and the X-axis give the relative frequency of the term.
Red bars correspond to test set (membrane) and blue bars correspond to
the whole Soybean genome array.Figure 12
Enriched graph (biological process) of the Soybean membrane subset of sequences.  Node filter has been set
at FDR < 1e-6. Nodes are colored accordingly to their FDR value in the Fisher exact's test against the whole Soybean genome array.Export results
Once different analyses have been completed the data
can be exported in many different ways. The annotation format (menu File→ Export → Export
Annotations) is the default format for export/import in B2G and simply consists
of a tab-delimited file with two columns, one for sequences and other for
annotation IDs. Another useful export format is GeneSpring, for communication
with this interesting application, which consists of one row per sequence and
three different columns showing the descriptions of the GO terms at the three
main GO categories. Graphs can be saved in png format. Additionally, all
information contained in the Combined Graph can be generated as table
(including sequences, GO IDs, levels, and scores) and exported (Analysis →
Export Graph Information).
The analysis presented in this use case took about
15 days to complete. Four days were necessary to obtain the totality of 37,500
blast results from the NCBI while twelve days were required for the
InterProScan at the EBI web server. Mapping and Annotation were ready within a
few hours and one day was necessary to collect and evaluate charts. This shows
that with the adequate tools and some training, functional annotation of a
plant genome-wide sequence collection is in reach within a couple of weeks.


## 4. Conclusions

Functional annotation of novel sequence data is a
key requirement for the successful generation of functional genomics in
biological research. The Blast2GO suite has been developed to be a useful
support to these approaches, especially (but not exclusively) in nonmodel
species. This bioinformatics tool is ideal for plant functional genomics
research because of the following: (1) it is suitable for any species but can
be also customized for specific needs, (2) it combines high throughput with
interactivity and curation, and (3) it is user-friendly and requires low
bioinformatics efforts to get it running. In our opinion, the major B2G
strength is the combination of functional annotation and data mining on
annotation results, which means that, within one tool, researchers can generate
functional annotation and assess the functional meaning of their experimental
results. Further developments of Blast2GO will reinforce this second aspect
thought the integration of the tool with the Babelomics (www.babelomics.org,
[38]) and GEPAS suites (www.gepas.org, [39]) for the statistical analysis of
functional profiling data.



---

*Source: 619832-2008-04-30.xml*