# Bioinformatic Tools for Inferring Functional Information from Plant Microarray Data II: Analysis Beyond Single Gene

Authors: Issa Coulibaly; Grier P. Page
Journal: International Journal of Plant Genomics
 (2008)

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## Abstract

While it is possible to interpret microarray experiments a single gene at a time, most studies generate long lists of differentially expressed genes whose interpretation requires the integration of prior biological knowledge. This prior knowledge is stored in various public and private databases and covers several aspects of gene function and biological information. In this review, we will describe the tools and places where to find prior accurate biological information and how to process and incorporate them to interpret microarray data analyses. Here, we highlight selected tools and resources for gene class level ontology analysis (Section 2), gene coexpression analysis (Section 3), gene network analysis (Section 4), biological pathway analysis (Section 5), analysis of transcriptional regulation (Section 6), and omics data integration (Section 7).  The overall goal of this review is to provide researchers with tools and information to facilitate the interpretation of microarray data.


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## Body

## 1. Introduction

Microarray analysis is exploratory and very high dimen-  sional,
and the primary purpose is to generate a list of differentially regulated genes
that can provide insight into the biological phenomena under investigation. However,
analysis should not stop with a list, it should be the starting point for secondary
analyses that aim at deciphering the molecular mechanisms underlying the
biological phenotypes analyzed. Combining microarray data with prior biological
knowledge is a fundamental key to the interpretation of the list of genes. This
prior knowledge is stored in various public and private databases and covers
several aspects of genes functions and biological information such as
regulatory sequence analysis, gene ontology, and pathway information. In this review,
we will describe the tools and places where to find prior accurate biological
information and how to incorporate them into the analysis of microarray data. The
plant genome outreach portal (http://www.plantgdb.org/PGROP/pgrop.php?app=pgrop)
list many of these resources and other tools and resources such as EST
resources and BLAST that are not covered in this review. We also address some theoretical
aspects and methodological issues of the algorithms implemented in the tools that
have been recently developed for bioinformatic and what needs to be considered
when selecting a tool for use.


## 2. Class Level Functional Annotation Tools

The goal of these class level functional annotation tools is to relate
the expression data to other attributes such as cellular localization,
biological process, and molecular function for groups of related genes. The
most common way to functionally analyze a gene list is to gather information
from the literature or from databases covering the whole genome. The recent
developments in technologies and instrumentation enabled a rapid accumulation
of large amount of in silico data in the area of genomics, transcriptomics, and
proteomics as well. The gene ontology (GO) consortium was created to develop consistent
descriptions of gene products in different databases [1]. The GO provides researchers
with a powerful way to query and analyze this information in a way that is
independent of species [2]. GO allows for the annotation
of genes at different levels of abstraction due to the directed acyclic graph
(DAG) structure of the GO. In this particular hierarchical structure, each term
can have one or more child terms as well as one or more parent terms. For
instance, the same gene list is annotated with a more general GO term such as
“cell communication” at a higher level of abstraction, whereas the lowest level
provides a more specific ontology term such as “intracellular signaling cascade.”In recent years, various tools have been developed to assess the
statistical significance of association of a list of genes with GO annotations
terms, and new ones are being regularly released [3]. There has been extensive
discussion of the most appropriate methods for the class level analysis of microarray
data [4–6].  The methods and tools are based on different methodological
assumptions. There are two key points to consider: (1) whether the method uses gene
sampling or subject sampling and (2) whether the method uses competitive or self-contained procedures. The subject sampling methods are preferred
and the competitive versus self-contained debate continues. Gene sampling
methods base their calculation of the p-value for the geneset
on a distribution in which the gene is the unit of sampling, while
the subject sampling methods take the subject as the sampling unit. The latter
is more valid for the unit of randomization is the subjects not the genes  [7–9].Competitive tests, which encompass most of the existing tools, test whether a gene class,
defined by a specific GO term or pathway or similar, is overrepresented in the
list of genes differentially expressed compared to a reference set of genes. A self-contained test compares the gene set toa fixed
standard that does not depend on the measurements of genes outside
the gene set. Goeman et al. [10, 11], Mansmann and Meister [7], and Tomfohr et al. [9] applied the self-contained methods.Another important aspect of ontological analysis regardless
of the tool or statistical method is the choice of the reference gene list
against which the list of differentially regulated genes is compared. Inappropriate
choice of reference genes may lead to false functional characterization of the
differentiated gene list. Khatri and Drǎghici [3]  pointed out that only the
genes represented on the array, although quite incomplete, should be used as
reference list instead of the whole genome as it is a common practice. In addition correct, up to date, and complete
annotation of genes with GO terms is critical. The competitive and gene sample-based
procedures tend to have better and more complete databases. GO allows for the annotation
of genes at different levels of abstraction due to the directed acyclic graph
(DAG) structure of the GO. In this particular hierarchical structure, each term
can have one or more child terms as well as one or more parent terms. For
instance, the same gene list is annotated with a more general GO term such as
“cell communication” at a higher level of abstraction, whereas the lowest level
provides a more specific ontology term such as “intracellular signaling
cascade.” It is important to integrate the hierarchical structure of the GO in
the analysis since various levels of abstraction usually give different p-values. The large number (hundreds or
thousands) of tests performed during ontological analysis may lead to spurious
associations just by chance, thus correction for multiple testing is a
necessary step to take. We present here a nonexhaustive list of tools available
that can be used to perform functional annotation of gene list and attempt to
compare their functionalities (Table 1). All tools accept input data from Arabidopsis thaliana, the most used
model organism in plant studies, as well as many animal organism models.Table 1
Recapitulative list of GO
annotations tools.
Tool nameStatistical modelGO abstraction levelGO visualizationMultiple testingType of arrayOther annotationOSOnto-Expresshypergeometric, Fisher’s exact test, binomial,𝒳2AvailableDAGBonferroni, Holm, Sidak, FDR172 commercial arraysChromosomal positionAnyFatiGO+Fisher’s exact testAvailableOne level at a timeFDRUser-providedKEGG pathways, SwissPROT keywordsAnyFuncAssociateFisher’s exact testNot availableNot availableMonte
Carlo simulationUser-providedNot availableWeb-basedGoToolBoxhypergeometric test, Fisher’s
exact test or binomialAvailableOne level at a timeBonferroniUser-providedNot availableAnyCLENCH2Hypergeometric, binomial,𝒳2Static globalDAGNoneUser-providedNot availableWindowsBiNGOHypergeometric, binomialAvailable, GOSlimDAGFDR, Bonferronicommercial arraysNot availableGoSurfer𝒳2Lowest levelDAGFDRAffymetrix onlyNot availableWindowsOnto-Express (OE):http://vortex.cs.wayne.edu/projects.htm#Onto-Express
Onto-Express is a software application used to translate a
list of differentially regulated genes into a functional profile [12, 13]. Onto-Express constructs a
profile for each of the GO categories: cellular component, biological process,
molecular function, and chromosome location as well. Onto-Express implements
hypergeometric, binomial, 𝒳2 and Fisher’s exact tests. The results
are displayed in a graphical form that allows the user to collapse or expand GO
node and visualize the p-values
associated with each level of GO abstraction. Onto-Express performs Bonferroni,
Holm, Sidak, and FDR corrections to adjust for multiple testing. Users have an
option of either providing their own reference gene list or selecting a
microarray platform as reference gene list. An extensive list of up to date annotations is provided for many arrays.FuncAssociate:http://llama.med.harvard.edu/cgi/func/funcassociate
FuncAssociate is a web-based tool that
characterizes large sets of genes with GO terms using the Fisher’s exact test [14]. Among all annotation tools
FuncAssociate stands out in that it implements a Monte
Carlo simulation to correct for multiple testing. In addition the
tools can conduct analysis on ranked list of query genes. Although FuncAssociate
supports 10 organisms, it does not provide visualization or level information
for the GO annotation.SAFE (Significance
Analysis of Function and Expression)
SAFE is a Bioconductor/R algorithm  that first computes gene-specific statistics in
order to test for association between gene expression and the phenotype of
interest [15]. Gene-specific statistics are used to estimate
global statistics that detects shifts in the local statistics within a gene
category. The significance of the global statistics is assessed by repeatedly
permuting the response values. SAFE implements a rank-based global statistics that
enables a better use of marginally significant genes than those based on a p-value cutoff.Global Test
Global test is a Bioconductor/R package that tests the association
of expression pattern of a group of genes with selected phenotypes of interest using
self-contained methods [10]. The method is based on a
penalized regression model that shrinks regression coefficient between gene
expression and phenotype toward a common mean. The algorithm allows the users
to test biological hypothesis or to search GO databases for potential pathways.
The results of gene lists of various sizes can be compared.FatiGO+ (Fast Assignment
and Transference OfInformation):http://babelomics2.bioinfo.cipf.es/fatigoplus/cgi-bin/fatigoplus.cgi
FatiGO+ tests for significant difference in distribution of
GO terms between any two groups of genes (ideally a group of interest and a
reference set of genes) using a Fisher’s exact test for 2 by 2 contingency
table [16]. FatiGO+ implements an
inclusive analysis in which at a given level in the GO DAG hierarchy, genes
annotated with child GO terms take the annotation from the parent. This
increases the power of the test. The software returns adjusted p-values using the FDR method [17].GOToolBox:http://burgundy.cmmt.ubc.ca/GOToolBox/
GOToolBox identifies over-or under-represented GO terms in a
gene set using either hypergeometric distribution-based tests or binomial test [18]. The user has the option of
choosing between the total set of genes in the genome as reference or provides
his own list of reference genes. The software implements Bonferroni correction
to adjust for multiple testing. Its also allows the user to select a specific
level of GO abstraction prior to the analysis.CLENCH2 (CLuster ENriCHment):http://www.stanford.edu/~nigam/cgi-bin/dokuwiki/doku.php?id=clench
Clench is used to calculate cluster enrichment for GO terms [19]. The program accepts two lists
of genes: a reference set of genes and the list of changed genes. CLENCH performs
hypergeometric, binomial and  𝒳2 tests to estimate GO terms enrichment. The
program allows the user to choose an FDR cutoff in order to account for
multiple testing.BiNGO (Biological Network Gene Ontology Tool):http://www.psb.ugent.be/cbd/papers/BiNGO/
BiNGO is a Java-based tool to determine which gene ontology
(GO) categories are statistically overrepresented in a set of genes or a subgraph
of a biological network [20]. BiNGO is implemented as a
plugin for Cytoscape, which is an
open source bioinformatics software platform for visualizing and integrating
molecular interaction networks. The
program implements hypergeometric test and binomial test and performs FDR to
control multiple testing. BiNGO maps predominant functional themes of the
tested genes on the GO hierarchy. It allows a customizable visual
representation of the results. One limitation is that the user can only choose
between the whole genome or the network under study as reference set of gene
for the enrichment test.GoSurfer:http://bioinformatics.bioen.uiuc.edu/gosurfer/
GoSurfer is used to visualize and compare gene sets by
mapping them onto gene ontology (GO) information in the form of a hierarchical
tree [21]. Users can manipulate the
tree output by various means, like setting heuristic thresholds or using
statistical tests. Significantly important GO terms resulting from a 𝒳2 test
can be highlighted. The software controls for false discovery rate.


## 3. Gene Coexpression Analysis Tools

In most microarray studies, gene expressions are measured on
a small number of arrays or samples; however, large collections of arrays are available
in microarray database that contain transcript levels data from thousands of
genes across a wide variety of experiments and samples. These tools provide scientists with the
opportunity to analyze the transcriptome by pooling gene expression information
from multiple data sets. This meta-analytic approach allows biologists to test
the consistency of gene expression patterns across different studies. Most
importantly, the analysis of concerted changes in transcript levels between
genes can lead to biological function discovery. It has been demonstrated that
genes which protein products cooperate in the same pathway or are in a
multimeric protein complex display similar expression patterns across a variety
of experimental conditions [22, 23]. Using the
guilt-by-association principle, investigators can functionally characterize a previously
uncharacterized gene when it displays expression pattern similar to that of known
genes. The coexpression relationship between two genes is usually assessed by computing
the Pearson’s correlation coefficient or other distance measures. Prior to the coexpression
analysis, a set of “bait-genes” is selected based on previous biological or literature
information. Then the genes which expression is significantly correlated with
bait-genes expression are analyzed to identify new potential actors in a given
pathway or biological process. However, coexpression between two genes does not
necessarily translate into similar function between both genes. Some statistically
significant correlations may occur by chance. Some authors suggest that to be
sustainable the gene coexpressions observed in one species should be confirmed
in other evolutionary close species [24]. Tools have been developed
that make use of the large sample size available in these databases to identify
more reliable concerted changes in transcripts levels as well as to examine the
coordinated change of gene expression levels.Cress-Express:http://www.cressexpress.org/
Cress-express estimates
the coexpression between a user-provided list of genes and all genes from
Affymetrix Ath1 platform using up to 1779 arrays.  Cress-express also performs pathway-level  coexpression (PLC) [25]. PLC identifies and ranks
genes based on their coexpression with a group of genes.  Cress-express also delivers results in “bulk”
formats suitable for downstream data mining via web services. The tool
generates files for easy import into Cytoscape for visualization. The tool has
the data processed with a variety of image processing methods: RMA, MAS5, and
GCRMA. Investigators can select which of over 100 experiments to include in  coexpression analysis.ATTED-II
(Arabidopsis thaliana Transfactor and Cis-Element
Prediction Database): http://www.atted.bio.titech.ac.jp/
ATTED-II provides coregulated
gene relationships inArabidopsis thaliana to estimate gene functions.
In addition, it can predict overrepresented cis-elements based upon all
possible heptamers. There is also several visualization tools and databases of
annotations attached to the coexpression.Genevestigator:https://www.genevestigator.ethz.ch/
Genevestigator is a web-based discovery tool to study the expression and
regulation of genes, pathways, and networks [26, 27]. Among other applications,
the software allows the user to look at individual gene expression or group of genes
coexpression in many different tissues, at multiple developmental stages, or in
response to large sets of stimuli, diseases, drug treatments, or mutations. In
addition, electronic northern blots and other analyses may be conducted.BAR (the Botany Array Resource) Expression
ANGLER:http://www.bar.utoronto.ca/
The expression anger
allows the user to identify genes with similar expression profile with the user
provided gene across multiple samples [28]. The user can specify the Pearson
correlation coefficient threshold and the array database to use for the  coexpression analysis.AthCor@CSB.DB (A. thaliana Coresponse Database): http://csbdb.mpimp-golm.mpg.de/csbdb/dbcor/ath.html
AthCor is a coexpression tool that allows the use of functional ontology
filter to identify genes  coexpressed
with a gene of interest filtering the search by functional ontologies [29]. The user can select between parametric
and nonparametric correlation tests.PLEXdb (Plant Expression Database):http://www.plexdb.org/
PLEXdb serves as a comprehensive public repository for gene
expression for plants and plant pathogens [30]. PLEXdb integrates new gene
expression datasets with traditional genomics and phenotypic data. The
integrated tools of PLEXdb allow plant investigators to perform comparative and
functional genomics analyses using large-scale expression data sets.ACT (Arabidopsis  Coexpression Data Mining Tool):http://www.arabidopsis.leeds.ac.uk/act/index.php
ACT estimates the  coexpression of 21 891Arabidopsis genes based on Affymetrix ATH1 platform using a simple
correlation test [31]. The web server includes a
database that stores precalculated correlation results from over 300 arrays of
the NASC/GARNet dataset. A “clique finder” tool allows the user to identify
groups of consistently  coexpressed genes
within a user-defined list of genes. The identification of  genes with a known function within a cluster
allows inference to be made about the other genes. Users can also visualize the
coexpression scatter plots of all genes
against a group of genes.


## 4. Gene Network Analysis

Genes and their protein products are related to each other
through a complex network of interactions. In higher metazoa, on average each
gene is estimated to interact with five other genes [32], and to be involved in ten different
biological functions during development [33]. On a molecular level, the
function of a gene depends on its cellular context, and the activity of a cell
is determined by which genes are being expressed and which are not and how they
interact with each other. In such high interconnectedness, analyzing a network
as a whole is essential to understanding the complex molecular processes
underlying biological systems. The traditional reductionist approach that
investigates biological phenomena by analyzing one gene at a time cannot
address this complexity. By using systems biology approach and network
theories, investigators can analyze the behavior and relationships of all of
the elements in a particular biological system to arrive at a more complete
description of how the system functions [34]. High-throughput gene
expression profiling offers the opportunity to analyze gene interrelationships at
the genome scale.  Clustering analysis on
microarray expression data only extracts lists of coregulated genes out of a
large-scale expression data. It does not tell us who is regulating whom and
how. However, the task of modeling dynamic systems with large number of
variables can be computationally challenging. In gene regulatory networks,
genes, mRNA, or proteins correspond to the network nodes and the links among
the nodes stand for the regulatory interactions (activations or inhibitions). In
this section, we will describe some of the methods and tools used to reconstruct,
visualize, and explore gene networks.


### 4.1. Gene Network Reconstruction Algorithms

Two main approaches have been used to develop models for gene regulatory
networks [35]. One method is based on
Bayesian inference theory which seeks to find the most probable network given
the observed expression patterns of the genes to be included in the network. The
regulatory interactions among genes and their directions are derived from
expression data. Several network structures are proposed and scored on the
basis of how well they explain the data as it has been successfully implemented
in yeast [36]. The second approach is based
on “mutual information” as a measure of correlation between gene expression
patterns [37]. A regulatory interaction
between two genes is established if the mutual information on their expression
patterns is significantly larger than a p-threshold
value calculated from the mutual information between random permutations of the
same patterns. Unlike the Bayesian theory, which tries out whole networks and
selects the one that best explains the observed data, the mutual information
method constructs a network by selecting or rejecting regulatory interactions
between pairs of genes. This method does not provide the direction of
regulatory interactions. We present below selected tools that implement either of
the aforementioned approaches to reverse-engineer gene regulatory networks.BNArray (Bayesian Network Array):http://www.cls.zju.edu.cn/binfo/BNArray/
BNArray is a tool
developed in R for inferring gene regulatory networks from DNA microarray data
by using a Bayesian network [38]. It allows the reconstruction of significant
submodules within regulatory networks using an extended subnetwork mining
algorithm. BNArray can handle microarray data with missing values.BANJO (Bayesian Network Inference with Java Objects):http://www.cs.duke.edu/~amink/software/banjo/
Banjo is a tool
developed in Java for inferring gene networks [39]. Banjo implements Bayesian and dynamic
Bayesian networks to infer networks from both steady-state and time-series expression
data. A “proposer” component of Banjo uses heuristic approaches to
search the network space for potential network structures. Each network
structure is explored and an overall network’s score is computed based on the
parameters of the conditional probability density distribution. The network
with the best overall score is accepted by a “decider” component of the
software. The network retained is processed by Banjo to compute influence
scores on the edges indicating the direction of the regulation between genes. The
software displays the output network.GNA (Genetic Network Analyzer):http://www-helix.inrialpes.fr/article122.html
GNA is a freely available software used for modeling and simulating
genetic regulatory networks from gene expression data and regulatory
interaction information [40]. In GNA, the dynamics of a regulatory
network is modeled by a class of piecewise-linear differential equations. The
biological data are transformed into mathematical formalism. Thus the software
uses qualitative constraints in the form of algebraic inequalities instead of
numerical values.PathwayAssisthttp://www.ariadnegenomics.com/products/pathway-studio
PathwayAssist allows the users to create their own pathways
by combining the user-submitted
microarray expression data with knowledge from biological databases such as BIND, KEGG, DIP [41]. The software provides a
graphical user interface and publication quality figures.


### 4.2. Network Visualization Tools

As a result
of the explosion and advances in experimental technologies that allow
genome-wide characterization of molecular states and interactions among
thousands of genes, researchers are often faced with the need for tools for the
visualization, display, and evaluation of large structure data.  The main aim of these tools is to provide a
summarized yet understandable view of large amount of data while integrating
additional information regarding the biological processes and functions. Several
network visualization tools have been developed of which we will describe some
of the most popular.Cytoscape—http://www.cytoscape.org/
Cytoscape is a general-purpose, open-source software
environment for the large scale integration of molecular interaction network data
[42]. Dynamic states on molecules
and molecular interactions are handled as attributes on nodes and edges,
whereas static hierarchical data, such as protein-functional ontologies, are
supported by use of annotations. The Cytoscape core handles basic features such
as network layout and mapping of data attributes to visual display properties. Many
Cytoscape plug-ins extend this core functionality.CellDesignerhttp://www.celldesigner.org/
CellDesigner is a structured diagram editor for drawing
gene-regulatory and biochemical networks based on standardized technologies and
with wide transportability to other systems biology markup language (SBML)
compliant applications and systems biology workbench (SBW) [43]. Networks are drawn based on
the process diagram, with graphical notation system. The user can browse and
modify existing SBML models with references to existing databases, simulate and
view the dynamics through an intuitive graphical interface. CellDesigner runs
on Windows, MacOS X, and Linux.VANTED (Visualization and Analysis
of Networks with Related Experimental Data):http://vanted.ipk-gatersleben.de/
Vanted is a freely available tool for network visualization that
allows users to map their own experimental data on networks drawn in the tool,
downloaded from KEGG pathway database, or imported using standard imported
formats [44]. The software graphically represents
the genes in their underlying metabolic context. Statistical methods
implemented in VANTED allow the comparison between treatments or groups of
genes, the generation of correlation matrix, or the clustering of genes based
on expression pattern.Ospreyhttp://biodata.mshri.on.ca/osprey/servlet/Index
Osprey is a software for visualization and manipulation of
complex interaction networks [45].  Osprey allows user defined colors to indicate
gene function, experimental systems, and data sources. Genes are colored by
their biological process as defined by standardized gene ontology (GO)
annotations. As a network complexity increases, Osprey simplifies network
layouts through user-implemented node relaxation, which disperses nodes and
edges according to anyone of a number of layout options.VisANT (Integrative Visual Analysis Tool for Biological Networks and Pathways):http://visant.bu.edu/
VisANT is a freely available open-source tool for integrating
biomolecular interaction data into a cohesive, graphical interface [45–47]. VisANT offers an online
interface for a large range of published datasets on biomolecular interactions,
as well as databases for organized annotation, including GenBank, KEGG, and
SwissProt.


### 4.3. Network Exploration Tools

One of the main focuses in the postgenomic era is to study the
network of molecular interactions in order to reveal the complex roles played
by genes, gene products, and the cellular environments in different biological
processes. The nodes (genes) of a network can be associated with additional
information regarding the gene products, gene positions in the chromosome, or
the gene functional annotation. The edges in the network symbolize specific
interaction that can be associated with a transcription factor-promoter bond for
instance. This information can be automatically retrieved in a number of
specialized and publicly accessible databases containing data about the nodes
and the interactions. Network exploration tools enable the user to perform
analysis on single genes, gene families, patterns of molecular interactions, as
well as on the global structure of the network. These tools are able to
incorporate both microscale and macroscale analysis using heterogenous data. They
can connect to a large number of disparate databases. The user usually has an
option to construct interaction networks either by curation or by computation
and to associate microarray expression data with known metabolic pathways. Here,
we describe some of the most popular network exploration tools.MetNet (Metabolic Networking Database):http://www.metnetdb.org/
MetNet is a publicly available software for analysis of genome-wide
mRNA, protein, and metabolite profiling data [48]. The software is designed to
enable the biologist to visualize, statistically analyze, and model a metabolic
and regulatory network map of Arabidopsis, combined with gene expression
profiling data. MetNet provides a framework for the formulation of testable
hypotheses regarding the function of specific genes. The tools within MetNet allow
the user to map metabolic and regulatory networks; to integrate and visualize
data together; to explore and model the metabolic and regulatory flow in the
network.BiologicalNetworks:http://biologicalnetworks.net/
BiologicalNetworks
is a bioinformatics and systems biology software platform for visualizing
molecular interaction networks, sequence and 3D structure information [49]. The tool performs easy
retrieval, construction, and visualization of complex biological networks,
including genome-scale integrated networks of protein-protein, protein-DNA, and genetic
interactions. BiologicalNetworks also allow the analysis and the mapping of
expression profiles of genes or proteins onto regulatory, metabolic, and
cellular networks.PaVESy (Pathway
Visualization Editing System):http://pavesy.mpimp-golm.mpg.de/PaVESy.htm
PaVESy is a data managing system for editing and
visualization of biological pathways [50]. The main component of PaVESy
is a relational SQL database system that stores biological objects, such as
metabolites, proteins, genes, and their interrelationships. The user can annotate
the biological objects with specific attributes that are integrated in the
database. The specific roles of the objects are derived from these attributes
in the context of user-defined interactions. PaVESy can display an
individualized view on the database content that facilitates user customization.Genevestigator:https://www.genevestigator.ethz.ch
Genevestigator provides a detailed analysis and navigation
through biochemical and/or regulatory pathways. It combines automatically
produced or user-created graphical representations of networks (e.g., gene
modules or pathways) for the exploratory analysis of a large compendium of gene
expression profiles. Effects on gene expression can be projected onto these networks
for the following ontologies: anatomy, development, stimulus, and mutation, in
form of comparison sets.


## 4.1. Gene Network Reconstruction Algorithms

Two main approaches have been used to develop models for gene regulatory
networks [35]. One method is based on
Bayesian inference theory which seeks to find the most probable network given
the observed expression patterns of the genes to be included in the network. The
regulatory interactions among genes and their directions are derived from
expression data. Several network structures are proposed and scored on the
basis of how well they explain the data as it has been successfully implemented
in yeast [36]. The second approach is based
on “mutual information” as a measure of correlation between gene expression
patterns [37]. A regulatory interaction
between two genes is established if the mutual information on their expression
patterns is significantly larger than a p-threshold
value calculated from the mutual information between random permutations of the
same patterns. Unlike the Bayesian theory, which tries out whole networks and
selects the one that best explains the observed data, the mutual information
method constructs a network by selecting or rejecting regulatory interactions
between pairs of genes. This method does not provide the direction of
regulatory interactions. We present below selected tools that implement either of
the aforementioned approaches to reverse-engineer gene regulatory networks.BNArray (Bayesian Network Array):http://www.cls.zju.edu.cn/binfo/BNArray/
BNArray is a tool
developed in R for inferring gene regulatory networks from DNA microarray data
by using a Bayesian network [38]. It allows the reconstruction of significant
submodules within regulatory networks using an extended subnetwork mining
algorithm. BNArray can handle microarray data with missing values.BANJO (Bayesian Network Inference with Java Objects):http://www.cs.duke.edu/~amink/software/banjo/
Banjo is a tool
developed in Java for inferring gene networks [39]. Banjo implements Bayesian and dynamic
Bayesian networks to infer networks from both steady-state and time-series expression
data. A “proposer” component of Banjo uses heuristic approaches to
search the network space for potential network structures. Each network
structure is explored and an overall network’s score is computed based on the
parameters of the conditional probability density distribution. The network
with the best overall score is accepted by a “decider” component of the
software. The network retained is processed by Banjo to compute influence
scores on the edges indicating the direction of the regulation between genes. The
software displays the output network.GNA (Genetic Network Analyzer):http://www-helix.inrialpes.fr/article122.html
GNA is a freely available software used for modeling and simulating
genetic regulatory networks from gene expression data and regulatory
interaction information [40]. In GNA, the dynamics of a regulatory
network is modeled by a class of piecewise-linear differential equations. The
biological data are transformed into mathematical formalism. Thus the software
uses qualitative constraints in the form of algebraic inequalities instead of
numerical values.PathwayAssisthttp://www.ariadnegenomics.com/products/pathway-studio
PathwayAssist allows the users to create their own pathways
by combining the user-submitted
microarray expression data with knowledge from biological databases such as BIND, KEGG, DIP [41]. The software provides a
graphical user interface and publication quality figures.


## 4.2. Network Visualization Tools

As a result
of the explosion and advances in experimental technologies that allow
genome-wide characterization of molecular states and interactions among
thousands of genes, researchers are often faced with the need for tools for the
visualization, display, and evaluation of large structure data.  The main aim of these tools is to provide a
summarized yet understandable view of large amount of data while integrating
additional information regarding the biological processes and functions. Several
network visualization tools have been developed of which we will describe some
of the most popular.Cytoscape—http://www.cytoscape.org/
Cytoscape is a general-purpose, open-source software
environment for the large scale integration of molecular interaction network data
[42]. Dynamic states on molecules
and molecular interactions are handled as attributes on nodes and edges,
whereas static hierarchical data, such as protein-functional ontologies, are
supported by use of annotations. The Cytoscape core handles basic features such
as network layout and mapping of data attributes to visual display properties. Many
Cytoscape plug-ins extend this core functionality.CellDesignerhttp://www.celldesigner.org/
CellDesigner is a structured diagram editor for drawing
gene-regulatory and biochemical networks based on standardized technologies and
with wide transportability to other systems biology markup language (SBML)
compliant applications and systems biology workbench (SBW) [43]. Networks are drawn based on
the process diagram, with graphical notation system. The user can browse and
modify existing SBML models with references to existing databases, simulate and
view the dynamics through an intuitive graphical interface. CellDesigner runs
on Windows, MacOS X, and Linux.VANTED (Visualization and Analysis
of Networks with Related Experimental Data):http://vanted.ipk-gatersleben.de/
Vanted is a freely available tool for network visualization that
allows users to map their own experimental data on networks drawn in the tool,
downloaded from KEGG pathway database, or imported using standard imported
formats [44]. The software graphically represents
the genes in their underlying metabolic context. Statistical methods
implemented in VANTED allow the comparison between treatments or groups of
genes, the generation of correlation matrix, or the clustering of genes based
on expression pattern.Ospreyhttp://biodata.mshri.on.ca/osprey/servlet/Index
Osprey is a software for visualization and manipulation of
complex interaction networks [45].  Osprey allows user defined colors to indicate
gene function, experimental systems, and data sources. Genes are colored by
their biological process as defined by standardized gene ontology (GO)
annotations. As a network complexity increases, Osprey simplifies network
layouts through user-implemented node relaxation, which disperses nodes and
edges according to anyone of a number of layout options.VisANT (Integrative Visual Analysis Tool for Biological Networks and Pathways):http://visant.bu.edu/
VisANT is a freely available open-source tool for integrating
biomolecular interaction data into a cohesive, graphical interface [45–47]. VisANT offers an online
interface for a large range of published datasets on biomolecular interactions,
as well as databases for organized annotation, including GenBank, KEGG, and
SwissProt.


## 4.3. Network Exploration Tools

One of the main focuses in the postgenomic era is to study the
network of molecular interactions in order to reveal the complex roles played
by genes, gene products, and the cellular environments in different biological
processes. The nodes (genes) of a network can be associated with additional
information regarding the gene products, gene positions in the chromosome, or
the gene functional annotation. The edges in the network symbolize specific
interaction that can be associated with a transcription factor-promoter bond for
instance. This information can be automatically retrieved in a number of
specialized and publicly accessible databases containing data about the nodes
and the interactions. Network exploration tools enable the user to perform
analysis on single genes, gene families, patterns of molecular interactions, as
well as on the global structure of the network. These tools are able to
incorporate both microscale and macroscale analysis using heterogenous data. They
can connect to a large number of disparate databases. The user usually has an
option to construct interaction networks either by curation or by computation
and to associate microarray expression data with known metabolic pathways. Here,
we describe some of the most popular network exploration tools.MetNet (Metabolic Networking Database):http://www.metnetdb.org/
MetNet is a publicly available software for analysis of genome-wide
mRNA, protein, and metabolite profiling data [48]. The software is designed to
enable the biologist to visualize, statistically analyze, and model a metabolic
and regulatory network map of Arabidopsis, combined with gene expression
profiling data. MetNet provides a framework for the formulation of testable
hypotheses regarding the function of specific genes. The tools within MetNet allow
the user to map metabolic and regulatory networks; to integrate and visualize
data together; to explore and model the metabolic and regulatory flow in the
network.BiologicalNetworks:http://biologicalnetworks.net/
BiologicalNetworks
is a bioinformatics and systems biology software platform for visualizing
molecular interaction networks, sequence and 3D structure information [49]. The tool performs easy
retrieval, construction, and visualization of complex biological networks,
including genome-scale integrated networks of protein-protein, protein-DNA, and genetic
interactions. BiologicalNetworks also allow the analysis and the mapping of
expression profiles of genes or proteins onto regulatory, metabolic, and
cellular networks.PaVESy (Pathway
Visualization Editing System):http://pavesy.mpimp-golm.mpg.de/PaVESy.htm
PaVESy is a data managing system for editing and
visualization of biological pathways [50]. The main component of PaVESy
is a relational SQL database system that stores biological objects, such as
metabolites, proteins, genes, and their interrelationships. The user can annotate
the biological objects with specific attributes that are integrated in the
database. The specific roles of the objects are derived from these attributes
in the context of user-defined interactions. PaVESy can display an
individualized view on the database content that facilitates user customization.Genevestigator:https://www.genevestigator.ethz.ch
Genevestigator provides a detailed analysis and navigation
through biochemical and/or regulatory pathways. It combines automatically
produced or user-created graphical representations of networks (e.g., gene
modules or pathways) for the exploratory analysis of a large compendium of gene
expression profiles. Effects on gene expression can be projected onto these networks
for the following ontologies: anatomy, development, stimulus, and mutation, in
form of comparison sets.


## 5. Biological Pathway Resources

One of the downstream applications of the reconstruction of a
gene regulatory networks or the identification of clusters of functionally
related genes is to associate the genes and their interconnections with known metabolic
pathways. Biochemists summarized the sequence of enzyme-catalyzed metabolic reactions
between biomolecules as a network of interactions that results from the
conversion of one organic substance (substrate) to another (product). Depending
on the type of interactions analyzed, several types of biochemical networks are
identified. These biochemical networks represent the potential mechanistic
associations between genes and gene products that are involved in specific
biological processes [52]. Because of the curse of
dimensionality that sometimes hampers the whole network analysis, investigators
often focus on “pathway” rather than “network” when they are investigated a
small number of gene interactions. Many specialized databases are available
that store and summarize large amount of information on metabolic reactions. Increasingly,
identifying and searching the right database is a critical and necessary step in
most biological researches. This task can
be tedious due to the large number of databases available. For a more comprehensive
list of biological pathways resources on the web, the reader is referred to pathguide
(http://www.pathguide.org). Following
is the list of the most popular pathways resources on the web.KEGG (Kyoto Encyclopedia of Genes and Genomes):http://www.genome.jp/kegg
KEGG aims to link lower-level information (genes, proteins,
enzymes, reaction molecules, etc.) with higher-level information (interactions,
enzymatic reactions, pathways, etc.).  Pathways are included for over 100 species.MetaCyc:http://MetaCyc.org/
MetaCyc is a database of metabolic pathways and enzymes [53]. Its goal is to serve as a
metabolic encyclopedia, containing a collection of nonredundant pathways,
enzymatic reactions, enzymes, chemical compounds, genes and review-level
comments. Enzyme information includes substrate specificity, kinetic
properties, activators, inhibitors, cofactor requirements and links to sequence
and structure databases. AracCyc (http://www.arabidopsis.org/biocyc/index.jsp) uses MetaCyc as reference database for visualization of Arabidopsis thaliana biochemical pathways. Table 2 indicates web links
to more online pathways databases.Table 2
Additional links for pathways
databases on the internet.
Database nameDescriptionURLPathDBBiochemical pathways, compounds and
metabolismhttp://www.ncgr.org/pathdbUM-BBDUniversity of Minnesota
biocatalysis and biodegradation
databasehttp://umbbd.ahc.umn.edu/BINDBiomolecular
interaction network databasehttp://www.bind.ca/BRITEBiomolecular relations in
information transmission and expression, part of KEGGhttp://www.genome.ad.jp/brite/PAJEKProgram for large network analysishttp://vlado.fmf.uni-lj.si/pub/networks/pajek/DDIBDatabase of domain interactions and
bindinghttp://www.ddib.org/DIPDatabase of interacting proteins:
experimentally determined protein-protein
interactionshttp://dip.doe-mbi.ucla.edu/IntAct
projectProtein-protein
interaction datahttp://www.ebi.ac.uk/intact/InterDomPutative
protein domain interactionshttp://interdom.i2r.a-star.edu.sg/PSIbaseInteraction of proteins with known
3D structuresReactomeA knowledgebase of biological
pathwayshttp://www.reactome.org/STRINGPredicted functional associations
between proteinshttp://string.embl.de/TRANSPATHGene regulatory networks and
microarray analysishttp://www.biobase-international.com/pages/index.php?id=transpathdatabasesBioCarta:http://www.biocarta.com/genes/index.asp
BioCarta is a web-based resource for exploring biological
pathways. BioCarta catalogs pathways, regulation and interaction information
for over 120,000 genes covering most model organisms. Data in BioCarta are
constantly updated, and new pathways are suggested by the life science research
community.GeneNeT:http://wwwmgs.bionet.nsc.ru/mgs/gnw/genenet/
The GeneNet system is designed for formalized description and
automated visualization of gene networks [54]. The GeneNet system includes
database on gene network components, Java program for the data visualization.
GeneNet allows the users to select entities that are involved in the
functioning of a particular gene network, to describe the regulatory relations
for a particular gene network, and to search for potential transcription
factors.


## 6. Transcription Regulation Analysis Tools

Most organisms encode a large number of DNA-binding proteins
that act as transcription factors. InArabidopsis,
more than 5% of the genes have been estimated to encode  transcription factors [55]. Transcription factors bind
to short conserved DNA motifs (cis-acting regulatory elements CARE) located at
the 5’end of the gene (in a region called promoter) to initiate mRNA
transcription. Thus DNA-binding proteins play a key role in all aspects of
genetic activity within an organism. They participate in promoting or
repressing the transcription of specific genes. Elucidating the mechanisms that
underlie the expression of genomes is one of the major challenges in
bioinformatics. An interesting hypothesis one might formulate after a
successful microarray study is that the genes that are coexpressed may also be coregulated
at the transcriptional level. One way to test this hypothesis is to identify
overrepresented oligonucleotides sequences as potential binding sites for
transcriptions factors in promoter regions of genes clustered in the same group.
The statistical test for overrepresentation of regulatory motifs in intergenic
regions is the general principle implemented in most algorithms for regulatory motif
detection [55]. CAREs can also be predicted
through phylogenetic footprinting that is based on sequence similarity between
orthologous promoters [56]. Some other approaches have
been proposed that integrates comparative, structural, and functional genomics
to identify conserved motifs in coregulated genes. The detailed description of these
approaches is beyond the scope of this chapter. Following is a list of transcription
factors database and tools (Table 3).Table 3
Databases for transcription
factors available on the internet.
Databse
nameDescriptionURLACTIVITYFunctional
DNA/RNA site activityhttp://wwwmgs.bionet.nsc.ru/mgs/systems/activity/DoOPDatabase of orthologous promoters:
chordates and plantshttp://doop.abc.hu/EPDEukaryotic
promoter databasehttp://www.epd.isb-sib.ch/JASPARPSSMs for transcription factor
DNA-binding siteshttp://jaspar.cgb.ki.se/MAPPERPutative transcription factor
binding sites in various genomeshttp://bio.chip.org/mapperTESSTranscription
element search systemhttp://www.cbil.upenn.edu/tess/TRANSCompelComposite regulatory elements
affecting gene transcription in eukaryoteshttp://www.gene-regulation.com/pub/databases.html#transcompelTREDTranscriptional
regulatory element databasehttp://rulai.cshl.edu/tred/TRRDTranscription regulatory regions of
eukaryotic geneshttp://www.bionet.nsc.ru/trrd/AthaMapGenome-wide map of putative
transcription factor binding sites inArabidopsis
thalianahttp://www.athamap.de/DATFDatabase ofArabidopsis transcription factorshttp://datf.cbi.pku.edu.cn/Plant Promoter Database (PlantProm DB):http://mendel.cs.rhul.ac.uk or http://www.softberry.com/
PlantProm is a plant promoter database. The database
represents a collection of annotated, nonredundant proximal promoter sequences
for RNA polymerase II with experimentally determined transcription start site
from various plant species [57].The Arabidopsis Information Resource (TAIR) Motifanalysis Software:http://www.arabidopsis.org/tools/bulk/motiffinder/index.jsp
The motif analysis tool of the TAIR compares the frequency of
6-mer motif in promoter regions of query set of genes with the frequency of the
6-mer motif in the wholeA. thaliana genome. A binomial distribution p-value
is computed for each motif identified. The user can specify the size of the genes
5’upstream region to 500 bp or 1 kb. The tool does not account for multiple
testing.TRANSFAC:http://www.biobase-international.com/pages/index.phid=transfacdatabases
TRANSFAC is an international unique database on eukaryotic
transcriptional regulation [58]. The database contains data
on transcription factors, their target genes and their experimental-proven binding
sites in genes. Tools within TRANSFAC allow the users to automatically
visualize gene-regulatory networks based on interlinked factor and gene entries
in the database.AthaMap:http://www.athamap.de/index.php
AthaMap is a database that organizes a genome-wide map of
potential transcription factor binding sites inArabidopsis thaliana
[59]. AthaMap allows the user
to test for the overrepresentation of transcription factors in a set of query
genes. A colocalization tool performs combinatorial analysis to identify
synchronized binding of pairs of transcription factors.PlantCARE (Plant Cis-Acting Regulatory Elements):http://bioinformatics.psb.ugent.be/webtools/plantcare/html/
PlantCARE is a database of plant cis-acting regulatory
elements and a portal to tools for in silico analysis of promoter sequences [60]. The database can be queried
on names of TF binding sites, function, species, cell type, genes, and
reference literatures. The program returns a list of entries with links to
other information within the database or beyond through accession to TRANSFAC,
EMBL, GenBANK, or MEDLINE.PLACE (Plant Cis-Acting Regulatory DNA Elements):http://www.dna.affrc.go.jp/PLACE/
PLACE is a database of motifs found in plant cis-acting
regulatory DNA elements, all from previously published reports [61]. In addition to the motifs
originally reported their variations in other genes or in other plant species
reported later are also compiled. The PLACE database also contains a brief
description of each motif and relevant literature with PubMed ID numbers.Athena:http://www.bioinformatics2.wsu.edu/cgi-bin/Athena/cgi/home.pl
Athena is a database which contains over 30 000 predicted
Arabidopsis promoters sequences and consensus sequences for 105 previously
characterized TF binding sites [62]. Athena enables the user to
visualize and rapidly inspect key regulatory elements in multiple promoters. The
software includes tools for testing the overrepresentation of TF sites among
subset of promoters. A data-mining tool allows the selection of promoter
sequences containing specific combination of TF binding sites. Athena does not
adjust for multiple testing.AGRIS (Arabidopsis Gene Regulatory Information
Server):http://arabidopsis.med.ohio-state.edu/
AGRIS
is an information resource for retrieving Arabidopsis promoter sequences, transcription
factors and their target genes [63]. AGRIS integrates transcriptional regulatory information
from multiple sources. Users can query the database with a gene name, gene
symbol to retrieve its promoter along with other genes regulated by the same
transcription factor.


## 7. ’Omics Data Integration Tools

Various innovative
and advanced technologies have allowed scientists to rapidly generate
genome-scale or “omics” datasets at virtually every cellular level. These individual
omics provide a wealth of information about living cells and organisms. However,
it is only by integrating genomics, transcriptomics proteomics, metabolomics, and
other recent omics types of data such as “interactomics,” “localizomics,” “lipidomics,”
and “phenomics” that biologists can gain access to a more complete picture of
living organisms and unexplored areas of biology. This challenging task requires
a systems level approach to perform systematic data mining, cross-knowledge
validation, and cross-species interpolation. Some investigators attempted the
integration of genomic data and transcriptomic data [64], and the integration of protein-protein interation
data and transcriptomic data [65] to analyze the dynamics of biological
networks in yeast. The approach commonly used comprises three steps: (1) identification
of the network that describes all interactions between cellular components from
integrating various genome scale data; (2) decomposing the network into its
constituent parts or network modules; (3) building a mathematical model that simulates
biological systems for the purpose of simulation or prediction [66]. We describe below proteomics and metabolomics,
and the potential of their integration with transcritomic data.


### 7.1. Proteomics

Gene mRNA expression
profiling on a global scale in response to specific conditions is not sufficient
to render the complexities and dynamics of systems biology. The ultimate
products of genes are proteins. Furthermore, mRNA levels are not always well
correlated with the levels of the corresponding protein [67] and one gene can produce
several protein species. Indeed, proteins undergo a series of posttranslational
molecular modifications such as glycosylation, phosphorylation, cleavage or
complex formation may also occur that overall influence their function. Proteomics
is the systematic large-scale study of proteins of an organism or a specific
type of tissue, particularly their structure, function, and spatiotemporal
distribution. Thus proteomics is an essential component of any functional
genomics study aiming at understanding biological processes. The integration of
transcriptome and proteome data has not always resulted in consistent results [68]. The methods and techniques used
to measure the transcript level and the protein level may affect the results concordance.
Nonetheless, the interpretation of the data in terms of biological pathways or
functional groups gives better correlation of transcriptome with proteome in
yeast [69].Many
plant proteomics databases have been constructed in recent years. As the plant
model organism of choice, Ara- bidopsis proteome database contains more data
compared to  other species. Protein amino acid sequence databases and  repositories
for two-dimensional polyacrylamide gel electro- phoresis as reference maps of
proteomes are becoming popular as tools for analyzing and comparing the plant
proteome. SWISS-2DPAGE is a two-dimensional polyacrylamide gel electrophoresis
database (http://expasy.org/ch2d). PhytoProt
(http://urgi.versailles.inra.fr/phytoprot)
is a database of clusters of all the plants full-length protein sequences
retrieved from SwissProt/TrEMBL. Proteins are grouped into clusters based on their
peptide sequence similarity in order to track erroneous annotations made at the
genome level. The database can be searched for any protein or group of proteins
using protein ID or words appearing in protein description. Additional plant proteomics
databases are provided in Table 4.Table 4
Proteomics databases available
on the internet.
Databse
nameDescriptionURLRPDRice proteome databasehttp://gene64.dna.affrc.go.jp/RPD/ANPDArabidopsis nucleolar protein
databasehttp://bioinf.scri.sari.ac.uk/cgi-bin/atnopdb/home/AMPDArabidopsis mitochondrial protein
databasehttp://www.plantenergy.uwa.edu.au/applications/ampdb/index.html/PA-GOSUBProtein sequences from model organisma,
GO assignement and subcellular localizationhttp://www.cs.ualberta.ca/~bioinfo/PA/GOSUB/Swiss-ProtA curated protein sequence database
which strives to provide a high level of annotationhttp://expasy.org/sprot/AAindexDatabase of various physicochemical
and biochemical properties of amino acids and pairs of amino acidshttp://www.genome.ad.jp/aaindex/PrositeDatabase of protein domains,
families and functional sites, as well as associated patterns and profileshttp://www.expasy.ch/prosite/PLANT-PIsDatabase of information on the
distribution and functional properties of protease inhibitors in higher
plantshttp://www.ba.itb.cnr.it/PLANT-PIs/GeneFarmAnnotation of Arabidopsis genes and
proteinshttp://urgi.versailles.inra.fr/Genefarm/


### 7.2. Metabolomics

Metabolomics is the
study of all low molecular weight chemicals in a plant as the end products of the
cellular processes. The metabolome represents the collection of all metabolites
in an organism. Metabolic profiling provides an instantaneous snapshot of the
chemistry of a sample and defines the biochemical phenotype of a cell or a
tissue [70]. Similar
to transcript level and protein level, the level of metabolites in an organism
or a tissue is influenced by the biological context [71]. Thus measure of mRNA gene expression and
protein content of a sample do not tell the whole story of biological phenomena
unfolding in that sample. Although plant metabolomics is still in its infancy, recent
advances in mass spectrometry have enabled the accumulation of metabolites data
on a large scale for some species. Applications of metabolomics data to
functional genomics are numerous. Metabolomics provide scientist with the
ability (1) to characterize genotypes, ecotypes, or phenotypes with metabolites
levels; (2) to identify sites within a genetic network where metabolites levels
are regulated; (3) to analyze genes functions at the light of metabolites levels
[70]. Currently, one of the most pressing needs
in the fields of metabolomics for bioinformatics application is the creation of
specific databases and biochemical ontologies. Such tools would help clearly
describe the function, localization, and interaction of metabolites. However, databases
imbedded in KEGG and AraCyc can be useful at least in part for the purpose of
metabolites referencing.


## 7.1. Proteomics

Gene mRNA expression
profiling on a global scale in response to specific conditions is not sufficient
to render the complexities and dynamics of systems biology. The ultimate
products of genes are proteins. Furthermore, mRNA levels are not always well
correlated with the levels of the corresponding protein [67] and one gene can produce
several protein species. Indeed, proteins undergo a series of posttranslational
molecular modifications such as glycosylation, phosphorylation, cleavage or
complex formation may also occur that overall influence their function. Proteomics
is the systematic large-scale study of proteins of an organism or a specific
type of tissue, particularly their structure, function, and spatiotemporal
distribution. Thus proteomics is an essential component of any functional
genomics study aiming at understanding biological processes. The integration of
transcriptome and proteome data has not always resulted in consistent results [68]. The methods and techniques used
to measure the transcript level and the protein level may affect the results concordance.
Nonetheless, the interpretation of the data in terms of biological pathways or
functional groups gives better correlation of transcriptome with proteome in
yeast [69].Many
plant proteomics databases have been constructed in recent years. As the plant
model organism of choice, Ara- bidopsis proteome database contains more data
compared to  other species. Protein amino acid sequence databases and  repositories
for two-dimensional polyacrylamide gel electro- phoresis as reference maps of
proteomes are becoming popular as tools for analyzing and comparing the plant
proteome. SWISS-2DPAGE is a two-dimensional polyacrylamide gel electrophoresis
database (http://expasy.org/ch2d). PhytoProt
(http://urgi.versailles.inra.fr/phytoprot)
is a database of clusters of all the plants full-length protein sequences
retrieved from SwissProt/TrEMBL. Proteins are grouped into clusters based on their
peptide sequence similarity in order to track erroneous annotations made at the
genome level. The database can be searched for any protein or group of proteins
using protein ID or words appearing in protein description. Additional plant proteomics
databases are provided in Table 4.Table 4
Proteomics databases available
on the internet.
Databse
nameDescriptionURLRPDRice proteome databasehttp://gene64.dna.affrc.go.jp/RPD/ANPDArabidopsis nucleolar protein
databasehttp://bioinf.scri.sari.ac.uk/cgi-bin/atnopdb/home/AMPDArabidopsis mitochondrial protein
databasehttp://www.plantenergy.uwa.edu.au/applications/ampdb/index.html/PA-GOSUBProtein sequences from model organisma,
GO assignement and subcellular localizationhttp://www.cs.ualberta.ca/~bioinfo/PA/GOSUB/Swiss-ProtA curated protein sequence database
which strives to provide a high level of annotationhttp://expasy.org/sprot/AAindexDatabase of various physicochemical
and biochemical properties of amino acids and pairs of amino acidshttp://www.genome.ad.jp/aaindex/PrositeDatabase of protein domains,
families and functional sites, as well as associated patterns and profileshttp://www.expasy.ch/prosite/PLANT-PIsDatabase of information on the
distribution and functional properties of protease inhibitors in higher
plantshttp://www.ba.itb.cnr.it/PLANT-PIs/GeneFarmAnnotation of Arabidopsis genes and
proteinshttp://urgi.versailles.inra.fr/Genefarm/


## 7.2. Metabolomics

Metabolomics is the
study of all low molecular weight chemicals in a plant as the end products of the
cellular processes. The metabolome represents the collection of all metabolites
in an organism. Metabolic profiling provides an instantaneous snapshot of the
chemistry of a sample and defines the biochemical phenotype of a cell or a
tissue [70]. Similar
to transcript level and protein level, the level of metabolites in an organism
or a tissue is influenced by the biological context [71]. Thus measure of mRNA gene expression and
protein content of a sample do not tell the whole story of biological phenomena
unfolding in that sample. Although plant metabolomics is still in its infancy, recent
advances in mass spectrometry have enabled the accumulation of metabolites data
on a large scale for some species. Applications of metabolomics data to
functional genomics are numerous. Metabolomics provide scientist with the
ability (1) to characterize genotypes, ecotypes, or phenotypes with metabolites
levels; (2) to identify sites within a genetic network where metabolites levels
are regulated; (3) to analyze genes functions at the light of metabolites levels
[70]. Currently, one of the most pressing needs
in the fields of metabolomics for bioinformatics application is the creation of
specific databases and biochemical ontologies. Such tools would help clearly
describe the function, localization, and interaction of metabolites. However, databases
imbedded in KEGG and AraCyc can be useful at least in part for the purpose of
metabolites referencing.


## 8. Conclusion

The deluge of large-scale biological data in the recent years
has made the development of computational tools critical to biological
investigation. Microarray studies enables scientist to simultaneously
interrogate thousands of genes throughout the genome. A great variety of tools have
been developed for the specific task of drawing biological meaning from
microarray data. Most of the tools available exploit prior biological knowledge
accumulated in numerous publicly available databases in an attempt to provide a
comprehensive view of biological phenomena. Table5 summarizes the main features of each
class of bioinformatics tool described. These tools differ in many respects and
the guidance provided in this review will help biologists with little knowledge
in statistics understand some of the key concepts. The integration of
transcriptomics data with all other omics data is a challenging task that can
be addressed by a systems-level approach.Table 5
Main features of the types of bioinformatics tools used for the analysis of
DNA microarray data.
Tools and resourcesGoalMethodsClass
level functional AnnotationDetermine
a biological meaning to groups of related genes identified by microarray
analysisOverrepresentation
test of gene ontology (GO) termsGene
coexpressionIdentify
common expression patterns between genes in order to infer biological functionCorrelation
tests of gene expressionGene
network AnalysisCapture
the interconnectedness of cellular components in order to explain biological
phenomenaSystems
biology approachGene network reconstructionDevelop
models for gene regulatory networksBayesian
inference theory
Mutual
information theoryNetwork visualizationDisplay
a simplified view of large amount biological components and their interactionsGraph
theoryNetwork explorationAssociate
network nodes and edges with biological informationIncorporate
heterogeneous data from various databasesBiological
pathway resourcesMap
biological pathways information into  inferred networkCollect
and process information from pathway databasesTranscriptional
regulation analysisIdentify
transcription factors that regulate gene expressionOverrepresentation test of regulatory motifs in promoter
regions of related genes



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*Source: 893941-2008-07-03.xml*