# Bioinformatic Tools for Inferring Functional Information from Plant Microarray Data: Tools for the First Steps

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

---

## Abstract

Microarrays are a very powerful tool for quantifying the amount of RNA in samples; however, their ability to query essentially every gene in a genome, which can number in the tens of thousands, presents analytical and interpretative problems. As a result, a variety of software and web-based tools have been developed to help with these issues. This article highlights and reviews some of the tools for the first steps in the analysis of a microarray study. We have tried for a balance between free and commercial systems. We have organized the tools by topics including image processing tools (Section 2), power analysis tools (Section 3), image analysis tools (Section 4), database tools (Section 5), databases of functional information (Section 6), annotation tools (Section 7), statistical and data mining tools (Section 8), and dissemination tools (Section 9).


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## Body

## 1. Introduction

The primary goal of a microarray study is to generate a list
of differentially regulated genes and infer pathways that can provide insight
into the biological question under investigation. Due to the very high
dimensionality of a microarray experiment, running to thousand of genes,
bioinformatics, and statistical tools are essential for the analysis of data. This
review is written to provide plant investigators with a list of tools and web-based
resources designed to help them move from an idea or hypothesis to the conduct
of the study, image analysis, generation of expression data, statistical
analysis, annotation, and then dissemination of the data.The first step in the conduct of a microarray study is the
selection of a microarray platform to use. For many species, there are commercially
available arrays from commercial vendors and academic groups.  Unfortunately, arrays are not available for
all species, while arrays can be used in closely related species, it is usually
better to develop arrays based upon the sequence of the species being studied.
Section2
provides a list of tools for generating useful probe sequences from
genomic data. Once an array has been developed, it is critical to collect
sufficient samples to run an experiment that will generate biologically
generalizable results. Section 3
highlights tools for sample size and power
analysis for microarray studies. Image analysis tools (Section 4) are used to quantitate
the amount of fluorescence for a spot or set of spots. Microarray experiments
generate copious amounts of data. The storage and distribution of the data are accomplished
by the tools described in Section 5.
Databases of gene annotations are provided in Section 6. Sections 7 and 8
describe statistical analysis and annotation tools. The two grouped together
for the same tools often provide both functions. In fact, many of the database
tools will also provide analytical and annotation functions as well. Finally,
in Section 9
we describe web sites for disseminating microarray data and
analyses.


## 2. Probe Design Software

Plant scientists conduct their research on a wide variety of plant taxa.
Arrays have been developed for a
number of plant species including Arabidopsis, Maize, Populus, Rice, Barley,
Grape, Citrus, Cotton, Medicago, Soybean, Sugar Cane, Tomato, and Wheat. While arrays can be used on closely related
species, it is often better to design a new array for the species of interest.
Several tools have been designed to help design probes for spotting or
deposition on arrays, based upon genomic sequence data. The critical stage is
to have high-quality sequence data. The more complete the genome is, the easier it will be
to design probes that will not cross hybridize, be subject to SNPs, and query
the gene accurately. Table1 lists a number of tools for probe design; many of them
are free, but a number specific to a single array manufacturer.Table 1
Probe design software packages.
Tool and websiteCost and functions of the toolArray Designerhttp://www.premierbiosoft.com/dnamicroarray/index.htmlDesign primers and probes for oligo and cDNA expression microarrays. It can also design probes for SNP detection,
single exon, whole gene, tiling, and resequencing arrays.  The software is not free.ArrayScribehttp://www.nimblegen.com/products/software/arrayscribe.htmlFree, but limited to designing
NimbleGen Arrays. The tool can design probes, specify mismatches at specific
sequence positions, automatically generate mismatches, generate multiple
probes for a gene, and design the placement of spots on an array.eArrayhttp://earray.chem.agilent.com/earray/login.doFree, but limited to designing
Agilent arrays. Can design probes for expression, CGH, and ChiP for any species with
genomic sequence.Primer3Plushttp://www.bioinformatics.nl/cgi-in/primer3plus/primer3plus.cgiFree software that can design probes for expression detection on
arrays, amplification/cloning, and sequencing/resequencing.Sarani Oligo Designhttp://www.strandls.com/oligodesign.htmlProbe design for expression analysis. The software is not free.Visual OMPhttp://www.dnasoftware.com/Products/VisualOMPDesign software for RNA,
DNA, single or multiple probe design, microarrays, TaqMan assays, genotyping,
single and multiplex PCR, secondary structure simulation, sequencing, genotyping.


## 3. Power Analysis and Samplesize Calculations

One of the keys to a successful
microarray study is to collect enough data (arrays) in order to derive biologically generalizable results. The key to this is the statistical
power of a study. Power is the probability of being able to detect a
significant difference between experiment groups when one really exists.  There are several factors involved in power,
but the main one under the control of an investigator is the sample size. A
study with too few samples may not detect real differences, while too many
samples will waste resources. Power analysis allows the selection of the optimal
sample size. While sample sizes for microarrays can be planned with traditional
statistical power calculation tools such as
PS (http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize),
the unique features of arrays such as the large number of tests and the large
number of genes that are different between groups have lead to the development
of several methods and tools for calculating power and sample size analysis.


### 3.1. The Power Atlas

The Power Atlas is a web-based
resource to assist investigators in the planning and design of microarray and expression-based experiments. This software currently
aims at estimating the power and sample size for a two group comparison based
upon pilot data. The methods underlying the
web site are reported in Gadbury et al.
[1]  and the software is described in further detail
at Page et al. [2].  The tool may be used in two manners: one may
either upload one’s own pilot data or select a pilot dataset from over 1 000
public data sets. Output includes graphs of power for a variety of significance
and false discovery
rates; see http://www.poweratlas.org/ [2].


### 3.2. Significance Analysis of Microarrays (SAM)

SAM is a free flexible Excel Addin that includes a number of useful
functions for the analysis of microarray data. Tools include statistical analysis
for discrete, quantitative, and time series data, adjustments for multiple
testing, gene set enrichment analysis, sample size assessment, estimates of False
Discovery rate (FDR) andq-value,  as well as per gene power analysis; see http://www-stat.stanford.edu/~tibs/SAM/
[3].


## 3.1. The Power Atlas

The Power Atlas is a web-based
resource to assist investigators in the planning and design of microarray and expression-based experiments. This software currently
aims at estimating the power and sample size for a two group comparison based
upon pilot data. The methods underlying the
web site are reported in Gadbury et al.
[1]  and the software is described in further detail
at Page et al. [2].  The tool may be used in two manners: one may
either upload one’s own pilot data or select a pilot dataset from over 1 000
public data sets. Output includes graphs of power for a variety of significance
and false discovery
rates; see http://www.poweratlas.org/ [2].


## 3.2. Significance Analysis of Microarrays (SAM)

SAM is a free flexible Excel Addin that includes a number of useful
functions for the analysis of microarray data. Tools include statistical analysis
for discrete, quantitative, and time series data, adjustments for multiple
testing, gene set enrichment analysis, sample size assessment, estimates of False
Discovery rate (FDR) andq-value,  as well as per gene power analysis; see http://www-stat.stanford.edu/~tibs/SAM/
[3].


## 4. Image Analysis Software

The purpose of image analysis software
is to generate a quantified expression score from the scanned microarray images.
Some of the tools are specific to particular array types, and thus are not
appropriate for all array types. There are a number of tools that are available
in this area, many of which are expensive. We present here tools that are still
being actively supported and developed. Additional tools are listed in Table2.Table 2
Other useful image analysis software packages.
Tool nameWeb siteAble Image Analyserhttp://able.mulabs.com/ArrayVisionhttp://www.imagingresearch.com/products/ARV.aspIcononClusthttp://www.clondiag.com/frame.php?page=/products/sw/iconoclust/index.phpImaGenehttp://www.biodiscovery.com/index/imageneKoadarrayhttp://www.koada.com/koadarray/Microvigenehttp://www.vigenetech.com/MicroVigene.htmScanAlyzehttp://rana.lbl.gov/EisenSoftware.htmSpothttp://www.hca-vision.com/productspot.html


### 4.1. Affy

This is a packagein Bioconductorforprocessing Affymetrix arrays. A
wide variety of image processing, normalization, and quality control procedures are available. As a note, there are a
variety of other image processing tools in Bioconductor including PDNN and
DCHip that should be considered for use as well; seehttp://www.bioconductor.org/packages/2.1/bioc/html/affy.html [4].


### 4.2. Affyprobe Miner

Affyprobe miner is used to redefine
chip definition files (CDFs) for Affymetrix chips to take into account the more
recent genomic sequence information on SNP, alternative splicing, changes in
the gene model, exon structure, and other such genomic difference. Precomputed
CDFs for several chips are available for download; seehttp://gauss.dbb.georgetown.edu/liblab/affyprobeminer/
[5].


### 4.3. Beadarray

This is a function in Bioconductor for reading preprocessed Illumina Bead summary data as well as reconstructing bead-level data using raw TIFF
images. Methods for quality control and low-level analysis are also provided;
seehttp://www.bioconductor.org/packages/2.1/bioc/html/beadarray.html
[6].


### 4.4. Genechip Operating Software (GCOS)

Affymetrix GCOS automates the control
of GeneChip Fluidics Stations and Scanners. In addition, GCOS acquires data,
manages sample and experimental information, and performs
gene expression data analysis. GCOS can quantitate images using MAS 5 and PLIER;
seehttp://www.affymetrix.com/products/software/specific/gcos.affx.


### 4.5. Gene Pix Pro 6.0

This software
has a number of useful features including imaging, spot finding, quality
control, analysis tools, visualizations, and automation capabilities. GenePix
can display and process up to four single wavelengths, thus four-channel
imaging can be used. This tool can be integrated with a web-accessible
database. GenePix is in some ways the default industrial standard microarray
image analysis software because of its early development of couple of output
file formats, *.gpr and *.gps that are used by many other applications; seehttp://www.moleculardevices.com/.


### 4.6. Nimblescan

This is a
NimbleGen product designed for the extraction of feature intensity raw values,
linkage of the raw intensity values with the corresponding probe parameters,
and generation of analysis reports for expression, ChIP-chip and resequencing
arrays, and methylation analysis for NimbleGen Arrays; seehttp://www.nimblegen.com/products/software/nimblescan.html.


### 4.7. TM4/Spotfinder

Spotfinder is
part of the larger freely available microarray analysis suite TM4.  Spotfinder is designed for the rapid,
reproducible, and computer-aided analysis of microarray images, and the quantification
of gene expression. Spotfinder can read paired 16-bit or 8-bit TIFF image files
generated by most microarray scanners. Automatic, semiautomatic and manual grid
construction and adjustments can be made. Two segmentation methods are
available. Reusable grid geometry files and
automatic grid adjustment allow user to analyze large quantities of images in a
consistent and efficient manner. Quality control views allow the user to assess
systematic biases in the data; seehttp://www.tm4.org/spotfinder.html
[7, 8].


## 4.1. Affy

This is a packagein Bioconductorforprocessing Affymetrix arrays. A
wide variety of image processing, normalization, and quality control procedures are available. As a note, there are a
variety of other image processing tools in Bioconductor including PDNN and
DCHip that should be considered for use as well; seehttp://www.bioconductor.org/packages/2.1/bioc/html/affy.html [4].


## 4.2. Affyprobe Miner

Affyprobe miner is used to redefine
chip definition files (CDFs) for Affymetrix chips to take into account the more
recent genomic sequence information on SNP, alternative splicing, changes in
the gene model, exon structure, and other such genomic difference. Precomputed
CDFs for several chips are available for download; seehttp://gauss.dbb.georgetown.edu/liblab/affyprobeminer/
[5].


## 4.3. Beadarray

This is a function in Bioconductor for reading preprocessed Illumina Bead summary data as well as reconstructing bead-level data using raw TIFF
images. Methods for quality control and low-level analysis are also provided;
seehttp://www.bioconductor.org/packages/2.1/bioc/html/beadarray.html
[6].


## 4.4. Genechip Operating Software (GCOS)

Affymetrix GCOS automates the control
of GeneChip Fluidics Stations and Scanners. In addition, GCOS acquires data,
manages sample and experimental information, and performs
gene expression data analysis. GCOS can quantitate images using MAS 5 and PLIER;
seehttp://www.affymetrix.com/products/software/specific/gcos.affx.


## 4.5. Gene Pix Pro 6.0

This software
has a number of useful features including imaging, spot finding, quality
control, analysis tools, visualizations, and automation capabilities. GenePix
can display and process up to four single wavelengths, thus four-channel
imaging can be used. This tool can be integrated with a web-accessible
database. GenePix is in some ways the default industrial standard microarray
image analysis software because of its early development of couple of output
file formats, *.gpr and *.gps that are used by many other applications; seehttp://www.moleculardevices.com/.


## 4.6. Nimblescan

This is a
NimbleGen product designed for the extraction of feature intensity raw values,
linkage of the raw intensity values with the corresponding probe parameters,
and generation of analysis reports for expression, ChIP-chip and resequencing
arrays, and methylation analysis for NimbleGen Arrays; seehttp://www.nimblegen.com/products/software/nimblescan.html.


## 4.7. TM4/Spotfinder

Spotfinder is
part of the larger freely available microarray analysis suite TM4.  Spotfinder is designed for the rapid,
reproducible, and computer-aided analysis of microarray images, and the quantification
of gene expression. Spotfinder can read paired 16-bit or 8-bit TIFF image files
generated by most microarray scanners. Automatic, semiautomatic and manual grid
construction and adjustments can be made. Two segmentation methods are
available. Reusable grid geometry files and
automatic grid adjustment allow user to analyze large quantities of images in a
consistent and efficient manner. Quality control views allow the user to assess
systematic biases in the data; seehttp://www.tm4.org/spotfinder.html
[7, 8].


## 5. Database Tools

Microarray experiments generate a huge
amount of data. The handling, storing, sharing, and distribution of the data
can be quite complex. As a result a variety of database tools have been
developed for assisting in this aspect
of microarray studies. Some of the tools listed below are more than just stand-alone
database tools and may include extensive analysis and visualization functionality
as well. There are a number of database tools with highly different utility and
platform requirements. Table3 outlines the tools and websites.Table 3
Database tools.
Tool nameWeb siteAcuityhttp://www.moleculardevices.com/pages/software/gnacuity.htmlArray Results Manager ARMhttp://www.biodiscovery.com/index/armArraytrackhttp://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/ [35, 36]BASE  2http://base.thep.lu.se/caArrayhttp://caarray.nci.nih.gov/Expressionisthttp://www.genedata.com/products/expressionist/index_eng.htmlGene
Array Analyzer Software GAAShttp://www.medinfopoli.polimi.it/GAAS/GeneDirectorhttp://www.biodiscovery.com/index/genedirectorGeneSpring Workgrouphttp://www.chem.agilent.com/scripts/pds.asp?lpage=34668GeneTraffichttp://www.iobion.com/products/products_GENETRAFFIC.htmlGenowizhttp://www.ocimumbio.com/Longhorn Array Database LAD [37]http://www.longhornarraydatabase.org/MaxdLoad2http://www.bioinf.man.ac.uk/microarray/maxd/index.htmlPARTISAN arrayLIMShttp://www.clondiag.com/Rosetta Resolver Systemhttp://www.rosettabio.com/products/resolver/default.htmStanford Microarray  Database
SMDhttp://smd-www.stanford.edu//download/ [38]


## 6. Databases of Functional Information

The amount of information about the functions of genes is
beyond what any one person can know. Consequently, it is useful to pull in
information on what others have discovered about genes in order to fully and
correctly interpret an expression study. The following tools are databases of
various types on information such as published papers, gene sequences,
pathways, and ontologies that might be useful for an
investigator who is interpreting an expression study.


### 6.1. Agbase

AgBase
is a curated, open-source, web-accessible resource for functional analysis of
agricultural plant and animal gene products. Agbase contains databases of Poplar and Pine gene ontology terms and
annotations as well as several animals, microbes, and parasites; seehttp://www.agbase.msstate.edu)
[9, 10].


### 6.2. Agricola

Agricola is the catalog and index to the collections of the
National Agricultural Library. The database covers materials in all formats and
periods, dating back to the 15th century.
The records include all aspects of agriculture and related disciplines;
seehttp://agricola.nal.usda.gov/.


### 6.3. Eukaryotic Gene Orthologues (EGO)

EGO is generated by the pair-wise comparison
between the tentative consensus (TC) sequences from
individual organisms. The reciprocal pairs of the best match are clustered into
individual groups and multiple sequence alignments are displayed for each
group. EGO is very useful for connecting homologous genes across species; seehttp://compbio.dfci.harvard.edu/tgi/ego/
[11].


### 6.4. Ensembl

Ensembl is a joint project between European Bioinformatics Institute
and the Wellcome
Trust
Sanger Institute
to develop a software system which produces and maintains automatic annotation
on selected eukaryotic genomes. Initially developed for vertebrates, Ensembl
has been adapted for use by several plant groups including legume, Gramene, and
Arabidopsis; seehttp://www.ensembl.org/index.html [12].


### 6.5. Entrez Gene

Entrez Gene is an NCBI's database for gene-specific in- formation. Entrez
Gene focuses on the genomes that have been completely sequenced, have an active research community to contribute
gene-specific information, or that are scheduled for intense sequence
analysis.  Records are assigned unique,
stable and tracked integers as identifiers. The content (nomenclature, map
location, gene products and their attributes, markers, phenotypes, and links to
citations, sequences, variation details, maps, expression, protein homologs,
protein domains and external databases) is updated regularly. There is
currently at least some gene information on 113 plant species; seehttp://www.ncbi.nlm.nih.gov/sites/entrez?db=gene.


### 6.6. Gene Index

The goal of The Gene Index Project is
to use the available EST and gene sequences, along with the reference genomes,
to provide an inventory of likely genes and variants. Genes are linked to annotation regarding their functions. Currently GI
databases have been constructed for 34 plant species; (http://compbio.dfci.harvard.edu/tgi/plant.html)  [13, 14].


### 6.7. Gene Ontology

The objective of GO is to provide
controlled vocabularies for the description of the molecular function,
biological process, and cellular component of gene products. These terms are to
be used as attributes of gene products by various collaborating
databases such as Gramene and TAIR; seehttp://www.geneontology.org/ [15].


### 6.8. Gramene

Gramene is a curated, open-source, data resource
for ge- nome analysis in the grasses. The information stored in the database is
derived from public sources and includes genomes, EST sequencing, protein
structure and function analysis, genetic and physical mapping, interpretation
of bi- ochemical pathways, Gene Ontologies, gene and QTL localization and
descriptions of phenotypic characters and
mutations. Extensive information is provided forOryza,
Zea,
Triticum,
Hordeum,
Avena,
Setaria,
Pennisetum,
Secale,
Sorghum,
Zizania, and Brachypodium; see  http://www.gramene.org/.


### 6.9. Kyoto Encyclopedia of Genes and Genomes (KEGG)

KEGG is a database of biological
systems, consisting of genes and proteins
(KEGG GENES), endogenous and exogenous substances (KEGG LIGAND), pathways (KEGG
PATHWAY), and hierarchies and relationships of biological objects (KEGG BRITE).
This database is very rich in data with information across hundreds of species
including many plants; seehttp://www.genome.jp/kegg/ [16–18].


### 6.10. Plant Associated Microbe Geneontology (PAMGO)

PAMGO is a database of the results of
a multiinstitutional collaborative effort, aimed at developing new GO terms and
relationships for gene products implicated in plant-pathogen interactions.  GO terms are currently being developed for the
following species:Erwiniachrysanthemi, Pseudomonas syringae
pv tomato and Agrobacteriumtumefaciens, the fungus Magnaporthe grisea,
the oomycetesPhytophthora sojaeandPhytophthora
ramorum,and the nematode Meloidogyne hapla;
seehttp://pamgo.vbi.vt.edu/.


### 6.11. SWISS-PROT

SWISS-PROT is a curated protein
sequence database which provides high level of annotations such as the
description of the function of a protein, its domains structure,
post-translational modifications, variants, and so forth, along with good integration with other databases; seehttp://www.expasy.ch/sprot/.


### 6.12. TAIR

The Arabidopsis
Information Resource (TAIR) maintains a database of
genetic and molecular
biology data forArabidopsis
thaliana. Data available from TAIR includes the complete genome sequence along with gene structure, gene product
information, metabolism, gene expression, DNA and seed stocks, genome maps,
genetic and physical markers, and publications; see http://www.arabidopsis.org/.


## 6.1. Agbase

AgBase
is a curated, open-source, web-accessible resource for functional analysis of
agricultural plant and animal gene products. Agbase contains databases of Poplar and Pine gene ontology terms and
annotations as well as several animals, microbes, and parasites; seehttp://www.agbase.msstate.edu)
[9, 10].


## 6.2. Agricola

Agricola is the catalog and index to the collections of the
National Agricultural Library. The database covers materials in all formats and
periods, dating back to the 15th century.
The records include all aspects of agriculture and related disciplines;
seehttp://agricola.nal.usda.gov/.


## 6.3. Eukaryotic Gene Orthologues (EGO)

EGO is generated by the pair-wise comparison
between the tentative consensus (TC) sequences from
individual organisms. The reciprocal pairs of the best match are clustered into
individual groups and multiple sequence alignments are displayed for each
group. EGO is very useful for connecting homologous genes across species; seehttp://compbio.dfci.harvard.edu/tgi/ego/
[11].


## 6.4. Ensembl

Ensembl is a joint project between European Bioinformatics Institute
and the Wellcome
Trust
Sanger Institute
to develop a software system which produces and maintains automatic annotation
on selected eukaryotic genomes. Initially developed for vertebrates, Ensembl
has been adapted for use by several plant groups including legume, Gramene, and
Arabidopsis; seehttp://www.ensembl.org/index.html [12].


## 6.5. Entrez Gene

Entrez Gene is an NCBI's database for gene-specific in- formation. Entrez
Gene focuses on the genomes that have been completely sequenced, have an active research community to contribute
gene-specific information, or that are scheduled for intense sequence
analysis.  Records are assigned unique,
stable and tracked integers as identifiers. The content (nomenclature, map
location, gene products and their attributes, markers, phenotypes, and links to
citations, sequences, variation details, maps, expression, protein homologs,
protein domains and external databases) is updated regularly. There is
currently at least some gene information on 113 plant species; seehttp://www.ncbi.nlm.nih.gov/sites/entrez?db=gene.


## 6.6. Gene Index

The goal of The Gene Index Project is
to use the available EST and gene sequences, along with the reference genomes,
to provide an inventory of likely genes and variants. Genes are linked to annotation regarding their functions. Currently GI
databases have been constructed for 34 plant species; (http://compbio.dfci.harvard.edu/tgi/plant.html)  [13, 14].


## 6.7. Gene Ontology

The objective of GO is to provide
controlled vocabularies for the description of the molecular function,
biological process, and cellular component of gene products. These terms are to
be used as attributes of gene products by various collaborating
databases such as Gramene and TAIR; seehttp://www.geneontology.org/ [15].


## 6.8. Gramene

Gramene is a curated, open-source, data resource
for ge- nome analysis in the grasses. The information stored in the database is
derived from public sources and includes genomes, EST sequencing, protein
structure and function analysis, genetic and physical mapping, interpretation
of bi- ochemical pathways, Gene Ontologies, gene and QTL localization and
descriptions of phenotypic characters and
mutations. Extensive information is provided forOryza,
Zea,
Triticum,
Hordeum,
Avena,
Setaria,
Pennisetum,
Secale,
Sorghum,
Zizania, and Brachypodium; see  http://www.gramene.org/.


## 6.9. Kyoto Encyclopedia of Genes and Genomes (KEGG)

KEGG is a database of biological
systems, consisting of genes and proteins
(KEGG GENES), endogenous and exogenous substances (KEGG LIGAND), pathways (KEGG
PATHWAY), and hierarchies and relationships of biological objects (KEGG BRITE).
This database is very rich in data with information across hundreds of species
including many plants; seehttp://www.genome.jp/kegg/ [16–18].


## 6.10. Plant Associated Microbe Geneontology (PAMGO)

PAMGO is a database of the results of
a multiinstitutional collaborative effort, aimed at developing new GO terms and
relationships for gene products implicated in plant-pathogen interactions.  GO terms are currently being developed for the
following species:Erwiniachrysanthemi, Pseudomonas syringae
pv tomato and Agrobacteriumtumefaciens, the fungus Magnaporthe grisea,
the oomycetesPhytophthora sojaeandPhytophthora
ramorum,and the nematode Meloidogyne hapla;
seehttp://pamgo.vbi.vt.edu/.


## 6.11. SWISS-PROT

SWISS-PROT is a curated protein
sequence database which provides high level of annotations such as the
description of the function of a protein, its domains structure,
post-translational modifications, variants, and so forth, along with good integration with other databases; seehttp://www.expasy.ch/sprot/.


## 6.12. TAIR

The Arabidopsis
Information Resource (TAIR) maintains a database of
genetic and molecular
biology data forArabidopsis
thaliana. Data available from TAIR includes the complete genome sequence along with gene structure, gene product
information, metabolism, gene expression, DNA and seed stocks, genome maps,
genetic and physical markers, and publications; see http://www.arabidopsis.org/.


## 7. Annotation Tools

The databases described in Section6 can provide data in a variety of forms, which makes merging the annotations
with the expression difficult. To deal with this heterogeneity a number of
tools have been developed to increase the
ease of annotating genes in expression studies.


### 7.1. CiteXplore

CiteXplore combines literature search
with text mining tools for biology. Search results are cross referenced to
European Bioinformatics Institute applications based on publication
identifiers.  Links to full text versions
are provided where available; seehttp://www.ebi.ac.uk/citexplore/.


### 7.2. Database for Annotation, Visualization, and Integrated Discovery
(DAVID)

DAVID provides a huge set of functional annotation tools for
investigators to understand biological meaning behind a large list of genes.
The key is the DAVID Knowledgebase which
provides a comprehensive, high-quality collection of gene annotation resource,
the flexibility to cross-reference gene identifiers, and heterogeneous
annotations from almost all databases. The DAVID tools are able to identify enriched biological themes,
particularly GO terms, cluster redundant annotation terms, visualize genes
on Baccarat and KEGG pathway maps, display related many-genes-to-many-terms on
2D view, search for other functionally related genes not in the list, list
interacting proteins, highlight protein functional domains and
motifs, redirect to related literatures, and convert gene identifiers from
one type to another; seehttp://david.abcc.ncifcrf.gov/ [19].


### 7.3. MatchMiner

MatchMiner translates
between several gene identifier types for the same list of hundreds or
thousands of genes. The gene identifier types supported by the tool includes
GenBank accession numbers, IMAGE clone IDs, common gene names, HUGO names, gene
symbols, UniGene clusters, FISH-mapped BAC clones, Affymetrix identifiers, and
chromosome locations. MatchMiner can also find the intersection of two lists of
genes specified by different identifiers; seehttp://discover.nci.nih.gov/matchminer/index.jsp  [20].


### 7.4. Medminer

MedMiner searches and organizes the
biomedical literature on genes, gene-gene relationships, and gene-drug
relationships. It uses GeneCards, PubMed, and syntactic analysis,
truncated-keyword filtering of relational and user-controlled sculpting of
Boolean queries to generate key sentences from pertinent abstracts. Abstracts selected can be automatically
entered into EndNote; seehttp://discover.nci.nih.gov/textmining/main.jsp
[21].


## 7.1. CiteXplore

CiteXplore combines literature search
with text mining tools for biology. Search results are cross referenced to
European Bioinformatics Institute applications based on publication
identifiers.  Links to full text versions
are provided where available; seehttp://www.ebi.ac.uk/citexplore/.


## 7.2. Database for Annotation, Visualization, and Integrated Discovery
(DAVID)

DAVID provides a huge set of functional annotation tools for
investigators to understand biological meaning behind a large list of genes.
The key is the DAVID Knowledgebase which
provides a comprehensive, high-quality collection of gene annotation resource,
the flexibility to cross-reference gene identifiers, and heterogeneous
annotations from almost all databases. The DAVID tools are able to identify enriched biological themes,
particularly GO terms, cluster redundant annotation terms, visualize genes
on Baccarat and KEGG pathway maps, display related many-genes-to-many-terms on
2D view, search for other functionally related genes not in the list, list
interacting proteins, highlight protein functional domains and
motifs, redirect to related literatures, and convert gene identifiers from
one type to another; seehttp://david.abcc.ncifcrf.gov/ [19].


## 7.3. MatchMiner

MatchMiner translates
between several gene identifier types for the same list of hundreds or
thousands of genes. The gene identifier types supported by the tool includes
GenBank accession numbers, IMAGE clone IDs, common gene names, HUGO names, gene
symbols, UniGene clusters, FISH-mapped BAC clones, Affymetrix identifiers, and
chromosome locations. MatchMiner can also find the intersection of two lists of
genes specified by different identifiers; seehttp://discover.nci.nih.gov/matchminer/index.jsp  [20].


## 7.4. Medminer

MedMiner searches and organizes the
biomedical literature on genes, gene-gene relationships, and gene-drug
relationships. It uses GeneCards, PubMed, and syntactic analysis,
truncated-keyword filtering of relational and user-controlled sculpting of
Boolean queries to generate key sentences from pertinent abstracts. Abstracts selected can be automatically
entered into EndNote; seehttp://discover.nci.nih.gov/textmining/main.jsp
[21].


## 8. Data Analysis Software

There is an incredible
breadth of tools in this area with many tools providing very slick interfaces
and very useful functions; however, you really do not need any of these tools.  Most statistical packages such as SAS, SPSS,
JMP, and R can be used to analyze microarray data and will do most of the
functions the following
tools will do, for there are few statistical methods that are 100% unique to
expression studies.  Nonetheless many of
the following tools are much easier to use and often have better visualization
functions than the pure statistical programs.  Typically the tools have been designed for
ease of use, often too easy. Regardless of the tool you use, strive to
understand the function and analyses provided and the assumption that are made
when you choose to use them for analysis. For example, in cluster analysis you
need to make a choice of link and weight functions and the clusters that result
will be quite different based on methods which are chosen. There are similar
issues to learn and understand for all statistical methods and most
visualization methods. Additional tools are listed in Table4.Table 4
Other useful statistical analysis and data-mining tools.
Tool nameWeb siteAmiada (analyzing
microarray data)http://dambe.bio.uottawa.ca/amiada.asp
[39]ArrayAssist
Enterprisehttp://www.stratagene.com/caGEDAhttp://bioinformatics.upmc.edu/GE2/GEDA.htmlClusterhttp://rana.lbl.gov/EisenSoftware.htmdChiphttp://www.dchip.org/GeneMaths XThttp://www.applied-maths.com/genemaths/genemaths.htmINCLUSivehttp://homes.esat.kuleuven.be/~dna/Biol/Software.htmlJ-Express
Prohttp://www.molmine.com/software.htmMAExplorerhttp://maexplorer.sourceforge.net/NIA
Array analysishttp://lgsun.grc.nia.nih.gov/ANOVA/Onto-Toolshttp://vortex.cs.wayne.edu/projects.htmProbe Profilerhttp://www.corimbia.com/Pages/ProductOverview.htmTableViewhttp://ccgb.umn.edu/software/java/apps/TableView/Venn Mapperhttp://www.gatcplatform.nl//vennmapper/index.php


### 8.1. Bioconductor

Bioconductor
is a multicenter effort to develop tools in the R programming environment for
analyzing genomic data, especially microarray data. There are a large number of
different packages available to conduct many types of analyses; currently there
are over 115 microarray applications. Tools are still in very active
development, and are all freely available. Some of the most relevant tools are
affy, maanova, genefilter, limma, mulltest, annotate, geneplotter, marray to
name a few. A couple of the packages are described elsewhere in this document,
but for more details of specific tools see the Bioconductor web site; seehttp://www.bioconductor.org/  [22].


### 8.2. Biometric Research Branch (BRB) Arrays Tools

BRB ArrayTools is a free integrated
package for the visualization and statistical analysis of DNA microarray gene
expression data. It functions as an Excel Addin. It was developed by
professional statisticians experienced in the analysis of microarray data. It
is probably the best tool available for discriminate analysis and has a variety
of other statistical and cluster methods included; seehttp://linus.nci.nih.gov/BRB-ArrayTools.html.


### 8.3. Expression Profiler

Expression Profiler is a set of tools for cluster
analysis, pattern discovery, pattern visualization, study and search for gene ontology categories. The tool also generates sequence
logos, extracts regulatory sequences, studies protein interactions, and links
analysis results to external tools and databases; seehttp://ep.ebi.ac.uk/.


### 8.4. Genepattern

GenePattern puts sophisticated
computational methods into the hands of the biomedical research community. A
simple application interface gives a broad audience access to a growing
repository of analytic tools for genomic data, while an API supports
computational biologists. GenePattern is a powerful analysis workflow tool
developed to support multidisciplinary genomic research programs and designed
to encourage rapid integration of new techniques; seehttp://www.broad.mit.edu/cancer/software/genepattern/index.html
[23].


### 8.5. GeneXpress

GeneXPress is a visualization and
analysis tool for gene expression data, integrating clustering, gene
annotation, and sequence information. GeneXPress allows the user to load
clustering results and automatically analyze them for significance of
functional groups through correlation with functional annotations (e.g., Gene
Ontology) and for enrichment of motif binding sites (e.g., TRANSFAC motifs); seehttp://genexpress.stanford.edu/.


### 8.6. GEPAS (Gene Expression Pattern Analysis Suite)

GEPAS is an integrated web-based tool for the analysis of gene
expression data. GEPAS includes tools for normalization, many clustering
methods, supervised analysis, differential analysis, differential gene
expression, predictors, array CGH and functional annotation; seehttp://gepas.bioinfo.cipf.es/ [24, 25].


### 8.7. High-Dimensional Biology Statistics (HDBStat!)

HDBStat is a free java application that allows for the
normalization, transformation, and statistical analysis of expression data.
HDBStat also has a number of unique quality control
procedures included.  HDBStat has
implemented reproducible research design to allow for analysis to be readily
repeated; (http://www.ssg.uab.edu/hdbstat/)  [26].


### 8.8. JMP Genomics

JMP genomics leverages many statistical tools in JMP, a statistical
analysis package, as a result it has over 100 di- fferent analytical procedures that can be run. It also includes extensive
visualization tools. Scripts can be written for the development of standard
analytical procedures; seehttp://www.jmp.com/software/genomics/.


### 8.9. Onto-Tools

Onto-Tools are a series of freely
available tools for the analysis of microarray data.  Tools are available for array design (onto-design), gene class testing (onto-express),
comparing the content of arrays (onto-compare), mapping gene information across
databases (onto-translate), annotation (onto-miner), and pathway analysis
(pathway-express); seehttp://www.vortex.cs.wayne.edu [27].


### 8.10. Partek Genomic Suite

Partek Genomics Suite can be used for gene expression analysis, exon expression analysis, chromosomal copy number analysis,
and promoter tiling array analysis,
and analysis of SNP arrays. Partek includes a large number of statistical, visualization, and annotation
tools that can be tied together using workflow tools for rapid repetition of
analysis and for reproducible research; seehttp://www.partek.com/software/.


### 8.11. R/Maanova

Maanova stands for MicroArray ANalysis Of VAriance. It provides a
complete work flow for microarray data analysis including data-quality checks
and visualization, data trans- formation, ANOVA model fitting for both fixed and mixed effects models, statistical tests
including permutation tests, confidence interval with bootstrapping, and
cluster analysis. R/maanova is available in Bioconductor/R; refer tohttp://www.jax.org/staff/churchill/labsite/software/ Rmaanova/index.html
[28].


### 8.12. SAM (Significant Analysis of Microarrays)

SAM can be used on any type of array
data: oligo or cDNA arrays, SNP arrays, protein arrays, and so forth. Both parametric
and nonparametric tests are available for correlating expression data to
clinical parameters including treatment, diagnosis categories, survival time, paired data,
quantitative (e.g., tumor volume), and one-class. SAM can also implement
imputation methods for missing data via nearest neighbor algorithm; seehttp://www-stat.stanford.edu/~tibs/SAM/.


### 8.13. TM4

The TM4 suite of tools consists of four major applications, Microarray Data Manager (MADAM), TIGR_Spotfinder, Microarray Data Analysis System (MIDAS), and Multiexperiment Viewer (MeV), as well as a
MySQL database, all of which are freely
available. Although these software tools were developed for spotted two-color
arrays, many of the components can be easily adapted to work with single-color
formats such as filter arrays and GeneChips; seehttp://www.tm4.org/index.html.


## 8.1. Bioconductor

Bioconductor
is a multicenter effort to develop tools in the R programming environment for
analyzing genomic data, especially microarray data. There are a large number of
different packages available to conduct many types of analyses; currently there
are over 115 microarray applications. Tools are still in very active
development, and are all freely available. Some of the most relevant tools are
affy, maanova, genefilter, limma, mulltest, annotate, geneplotter, marray to
name a few. A couple of the packages are described elsewhere in this document,
but for more details of specific tools see the Bioconductor web site; seehttp://www.bioconductor.org/  [22].


## 8.2. Biometric Research Branch (BRB) Arrays Tools

BRB ArrayTools is a free integrated
package for the visualization and statistical analysis of DNA microarray gene
expression data. It functions as an Excel Addin. It was developed by
professional statisticians experienced in the analysis of microarray data. It
is probably the best tool available for discriminate analysis and has a variety
of other statistical and cluster methods included; seehttp://linus.nci.nih.gov/BRB-ArrayTools.html.


## 8.3. Expression Profiler

Expression Profiler is a set of tools for cluster
analysis, pattern discovery, pattern visualization, study and search for gene ontology categories. The tool also generates sequence
logos, extracts regulatory sequences, studies protein interactions, and links
analysis results to external tools and databases; seehttp://ep.ebi.ac.uk/.


## 8.4. Genepattern

GenePattern puts sophisticated
computational methods into the hands of the biomedical research community. A
simple application interface gives a broad audience access to a growing
repository of analytic tools for genomic data, while an API supports
computational biologists. GenePattern is a powerful analysis workflow tool
developed to support multidisciplinary genomic research programs and designed
to encourage rapid integration of new techniques; seehttp://www.broad.mit.edu/cancer/software/genepattern/index.html
[23].


## 8.5. GeneXpress

GeneXPress is a visualization and
analysis tool for gene expression data, integrating clustering, gene
annotation, and sequence information. GeneXPress allows the user to load
clustering results and automatically analyze them for significance of
functional groups through correlation with functional annotations (e.g., Gene
Ontology) and for enrichment of motif binding sites (e.g., TRANSFAC motifs); seehttp://genexpress.stanford.edu/.


## 8.6. GEPAS (Gene Expression Pattern Analysis Suite)

GEPAS is an integrated web-based tool for the analysis of gene
expression data. GEPAS includes tools for normalization, many clustering
methods, supervised analysis, differential analysis, differential gene
expression, predictors, array CGH and functional annotation; seehttp://gepas.bioinfo.cipf.es/ [24, 25].


## 8.7. High-Dimensional Biology Statistics (HDBStat!)

HDBStat is a free java application that allows for the
normalization, transformation, and statistical analysis of expression data.
HDBStat also has a number of unique quality control
procedures included.  HDBStat has
implemented reproducible research design to allow for analysis to be readily
repeated; (http://www.ssg.uab.edu/hdbstat/)  [26].


## 8.8. JMP Genomics

JMP genomics leverages many statistical tools in JMP, a statistical
analysis package, as a result it has over 100 di- fferent analytical procedures that can be run. It also includes extensive
visualization tools. Scripts can be written for the development of standard
analytical procedures; seehttp://www.jmp.com/software/genomics/.


## 8.9. Onto-Tools

Onto-Tools are a series of freely
available tools for the analysis of microarray data.  Tools are available for array design (onto-design), gene class testing (onto-express),
comparing the content of arrays (onto-compare), mapping gene information across
databases (onto-translate), annotation (onto-miner), and pathway analysis
(pathway-express); seehttp://www.vortex.cs.wayne.edu [27].


## 8.10. Partek Genomic Suite

Partek Genomics Suite can be used for gene expression analysis, exon expression analysis, chromosomal copy number analysis,
and promoter tiling array analysis,
and analysis of SNP arrays. Partek includes a large number of statistical, visualization, and annotation
tools that can be tied together using workflow tools for rapid repetition of
analysis and for reproducible research; seehttp://www.partek.com/software/.


## 8.11. R/Maanova

Maanova stands for MicroArray ANalysis Of VAriance. It provides a
complete work flow for microarray data analysis including data-quality checks
and visualization, data trans- formation, ANOVA model fitting for both fixed and mixed effects models, statistical tests
including permutation tests, confidence interval with bootstrapping, and
cluster analysis. R/maanova is available in Bioconductor/R; refer tohttp://www.jax.org/staff/churchill/labsite/software/ Rmaanova/index.html
[28].


## 8.12. SAM (Significant Analysis of Microarrays)

SAM can be used on any type of array
data: oligo or cDNA arrays, SNP arrays, protein arrays, and so forth. Both parametric
and nonparametric tests are available for correlating expression data to
clinical parameters including treatment, diagnosis categories, survival time, paired data,
quantitative (e.g., tumor volume), and one-class. SAM can also implement
imputation methods for missing data via nearest neighbor algorithm; seehttp://www-stat.stanford.edu/~tibs/SAM/.


## 8.13. TM4

The TM4 suite of tools consists of four major applications, Microarray Data Manager (MADAM), TIGR_Spotfinder, Microarray Data Analysis System (MIDAS), and Multiexperiment Viewer (MeV), as well as a
MySQL database, all of which are freely
available. Although these software tools were developed for spotted two-color
arrays, many of the components can be easily adapted to work with single-color
formats such as filter arrays and GeneChips; seehttp://www.tm4.org/index.html.


## 9. Dissemination

Early in the use of microarray in
research, it became common practice for many journals to require investigators
to submit expression data for publication in a public database. This sharing of
data has allowed the mining of these rich resources that many investigators
have used to help their research. A number of the public databases exist that contain
and accept plant data.


### 9.1. ArrayExpress

ArrayExpress is a public repository
for microarray data, which is aimed at storingMIAME-compliantdatain
accordance with MGED recommendations.  This database is a bit less biomedical in
focus than GEO with a good representation of plant expression data; seehttp://www.ebi.ac.uk/arrayexpress
[29, 30].


### 9.2. GEO

Gene Expression
Omnibus
is a gene expression/molecular abundance repository supporting MIAME
compliant data submissions, and a curated, online resource for gene expression data browsing, query and
retrieval. This is supported by the US National Library of Medicine, but
contains a good amount of plant expression data; seehttp://www.ncbi.nlm.nih.gov/projects/geo/ [31, 32].


### 9.3. NASC (Nottingham Arabidopsisstock Center) Arrays

NASC
runs a database of its own arrays as well as other data that has been deposited
in the database. The database primarily contains Arabidopsis array data; seehttp://affymetrix.arabidopsis.info/
[33].


### 9.4. Plant Expression Database (PlexDB)

PLEXdb is a unified public resource for
gene expression for plants and plant pathogens. PLEXdb serves as a portal to integrate gene expression
profile data sets with structural genomics and phenotypic data. Data from seven
species is contained in the database; seehttp://www.plexdb.org/index.php
[34].


## 9.1. ArrayExpress

ArrayExpress is a public repository
for microarray data, which is aimed at storingMIAME-compliantdatain
accordance with MGED recommendations.  This database is a bit less biomedical in
focus than GEO with a good representation of plant expression data; seehttp://www.ebi.ac.uk/arrayexpress
[29, 30].


## 9.2. GEO

Gene Expression
Omnibus
is a gene expression/molecular abundance repository supporting MIAME
compliant data submissions, and a curated, online resource for gene expression data browsing, query and
retrieval. This is supported by the US National Library of Medicine, but
contains a good amount of plant expression data; seehttp://www.ncbi.nlm.nih.gov/projects/geo/ [31, 32].


## 9.3. NASC (Nottingham Arabidopsisstock Center) Arrays

NASC
runs a database of its own arrays as well as other data that has been deposited
in the database. The database primarily contains Arabidopsis array data; seehttp://affymetrix.arabidopsis.info/
[33].


## 9.4. Plant Expression Database (PlexDB)

PLEXdb is a unified public resource for
gene expression for plants and plant pathogens. PLEXdb serves as a portal to integrate gene expression
profile data sets with structural genomics and phenotypic data. Data from seven
species is contained in the database; seehttp://www.plexdb.org/index.php
[34].


## 10. Conclusions

We hope this listing of tools, which only dip the surface of the
possible tools, will assist you in conducting, analyzing, and interpreting
expression studies. We suggest exploring several tools in an area and
understanding the principles of the methods implemented before settling on one
or a few to use regularly. By exploring several tools you will understand the potential of the various tools, how easy (or difficult)
they are to use, and determine what you really want and need for your
microarray analysis.



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*Source: 147563-2008-05-29.xml*