BarleyBase--an expression profiling database for plant genomics.
ABSTRACT BarleyBase (BB) (www.barleybase.org) is an online database for plant microarrays with integrated tools for data visualization and statistical analysis. BB houses raw and normalized expression data from the two publicly available Affymetrix genome arrays, Barley1 and Arabidopsis ATH1 with plans to include the new Affymetrix 61K wheat, maize, soybean and rice arrays, as they become available. BB contains a broad set of query and display options at all data levels, ranging from experiments to individual hybridizations to probe sets down to individual probes. Users can perform cross-experiment queries on probe sets based on observed expression profiles and/or based on known biological information. Probe set queries are integrated with visualization and analysis tools such as the R statistical toolbox, data filters and a large variety of plot types. Controlled vocabularies for gene and plant ontologies, as well as interconnecting links to physical or genetic map and other genomic data in PlantGDB, Gramene and GrainGenes, allow users to perform EST alignments and gene function prediction using Barley1 exemplar sequences, thus, enhancing cross-species comparison.
- SourceAvailable from: Rod A Wing[show abstract] [hide abstract]
ABSTRACT: In recent years, access to complete genomic sequences, coupled with rapidly accumulating data related to RNA and protein expression patterns, has made it possible to determine comprehensively how genes contribute to complex phenotypes. However, for major crop plants, publicly available, standard platforms for parallel expression analysis have been limited. We report the conception and design of the new publicly available, 22K Barley1 GeneChip probe array, a model for plants without a fully sequenced genome. Array content was derived from worldwide contribution of 350,000 high-quality ESTs from 84 cDNA libraries, in addition to 1,145 barley (Hordeum vulgare) gene sequences from the National Center for Biotechnology Information nonredundant database. Conserved sequences expressed in seedlings of wheat (Triticum aestivum), oat (Avena strigosa), rice (Oryza sativa), sorghum (Sorghum bicolor), and maize (Zea mays) were identified that will be valuable in the design of arrays across grasses. To enhance the usability of the data, BarleyBase, a MIAME-compliant, MySQL relational database, serves as a public repository for raw and normalized expression data from the Barley1 GeneChip probe array. Interconnecting links with PlantGDB and Gramene allow BarleyBase users to perform gene predictions using the 21,439 non-redundant Barley1 exemplar sequences or cross-species comparison at the genome level, respectively. We expect that this first generation array will accelerate hypothesis generation and gene discovery in disease defense pathways, responses to abiotic stresses, development, and evolutionary diversity in monocot plants.Plant physiology 04/2004; 134(3):960-8. · 6.56 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.Nucleic Acids Research 02/2002; 30(1):207-10. · 8.28 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: The Stanford Microarray Database (SMD; http://genome-www.stanford.edu/microarray/) serves as a microarray research database for Stanford investigators and their collaborators. In addition, SMD functions as a resource for the entire scientific community, by making freely available all of its source code and providing full public access to data published by SMD users, along with many tools to explore and analyze those data. SMD currently provides public access to data from 3500 microarrays, including data from 85 publications, and this total is increasing rapidly. In this article, we describe some of SMD's newer tools for accessing public data, assessing data quality and for data analysis.Nucleic Acids Research 02/2003; 31(1):94-6. · 8.28 Impact Factor
BarleyBase—an expression profiling database
for plant genomics
Lishuang Shen1, Jian Gong1, Rico A. Caldo2, Dan Nettleton3, Dianne Cook1,3,
Roger P. Wise2,4and Julie A. Dickerson1,*
1Virtual Reality Applications Center,2Department of Plant Pathology, Center for Plant Responses to
Environmental Stresses,3Department of Statistics and4Corn Insects and Crop Genetics
Research, USDA-ARS, Iowa State University, Ames, IA 50011, USA
Received August 12, 2004; Revised and Accepted October 21, 2004
BarleyBase (BB) (www.barleybase.org) is an online
database for plant microarrays with integrated tools
for data visualization and statistical analysis. BB
houses raw and normalized expression data from
the two publicly available Affymetrix genome arrays,
Barley1 and Arabidopsis ATH1 with plans to include
the new Affymetrix 61K wheat, maize, soybean and
rice arrays, as they become available. BB contains a
broad set of query and display options at all data
levels, ranging from experiments to individual hybri-
dizations to probe sets down to individual probes.
Users can perform cross-experiment queries on
probe sets based on observed expression profiles
and/or based on known biological information.
Probe set queries are integrated with visualization
and analysis tools such as the R statistical toolbox,
vocabularies for gene and plant ontologies, as well
as interconnecting links to physical or genetic map
and other genomic data in PlantGDB, Gramene
and GrainGenes, allow users to perform EST align-
ments and gene function prediction using Barley1
exemplar sequences, thus, enhancing cross-species
BarleyBase (BB) is a USDA-funded public database for cereal
microarray data. BB was first developed to support the Affy-
metrix Barley1 GeneChip, and is being expanded to new plant
Chip is a new community-designed, Affymetrix probe array
(1), which pioneered the GeneChip design for plants without a
fully sequenced genome. Several new GeneChips for wheat,
soybean and maize will be released in 2005.
BB includes MIAME-compliant microarray experiment
annotations as well as Plant Ontology terms through Barley-
Express, its web-based submission tool (2). Links with other
sequence and crop databases give BB users the ability to
quickly discover all the known facts about any probe set or
exemplarsequenceonthechip andtocomparewith otherplant
species such as rice or wheat. Data queries are integrated
with analysis and visualization tools to allow users to explore
their experimental data. As of September, 2004, BB hosts
23 completed experiment submissions with a total of
There are many public databases that provide access to
microarray data. These include general repositories, such as
the Gene Expression Omnibus (GEO) (3), Stanford Micro-
array Database (4) and ArrayExpress (5) and species-specific
resources, such as TAIR (6) and NASCArrays (7). Reposi-
tories typically store data for download and later analysis. The
general repositories such as GEO and ArrayExpress are
intended to act as central data distribution hubs, not to replace
gene expression databases that are constructed to facilitate
particular analytic methods or comparisons. BB is designed
to meet the needs of plant biologists in their analysis of gene
expression data and to put the expression data in the context of
functional genomics by using controlled gene and plant ontol-
ogies to describe experimental conditions. Interconnecting
links to plant genomic resources such as PlantGDB (8), Gra-
mene (9) and GrainGenes (10) facilitate access to contig align-
ments, oligo probe information and a variety of BLAST tools
from the NCBI, PlantGDB, TIGR, TAIR or Rice genome
BB stores microarray gene expression data in a MIAME-
compliant and Plant Ontology enhanced format for plants,
*To whom correspondence should be addressed. Tel: +1 515 294 7705; Fax: +1 515 294 8432; Email: email@example.com
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access
version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press
only in part or as a derivative work this must be clearly indicated. For commercial re-use permissions, please contact firstname.lastname@example.org.
ª 2005, the authors
Nucleic Acids Research, Vol. 33, Database issue ª Oxford University Press 2005; all rights reserved
Nucleic Acids Research, 2005, Vol. 33, Database issue
and integrates the data with exploration and analysis tools
across experiments. BB stores the following types of informa-
tion: GeneChip and/or microarray structure data, experimental
and labeling protocols, raw and normalized gene expression
data and experiment and sample annotations such as summary
statistics from R and MAS5.0.
BB uses a hierarchical data model to organize and display
microarray gene expression data. The top-level data structure
is the experiment, which consists of a set of hybridizations
with a treatment structure designed to answer one or more
related biological questions. A factorial treatment structure
is used to describe BB experiments. Each treatment is asso-
factors. Each treatment has one or more samples as biological
replicates; each sample has one or more hybridizations as
To facilitate smooth data exchange across databases, plant
ontologies for growth stage and organism parts (11), and other
controlled vocabulariesare requiredinthe experiment descrip-
tion and sample annotation in BarleyExpress. BB follows
the MIAME standards (12) and the implementation used in
MIAMExpress (http://www.ebi.ac.uk/miamexpress). Barley-
Express adds plant-specific fields such as links to the Plant
Ontology terms on growth stages and tissue types are added in
the experiment submission process (2). The use of controlled
vocabularies allows cross-experiment comparisons based
upon common identifiers, facilitating interoperability between
existing plant databases to identify homologous genes.
Biological annotation for probe sets and exemplars includes
sequence description, BLAST hits from related sequence
databases or species, Gene Ontology, and pathway and
gene family information.
BB requires raw CEL data files for gene expression data for
which EXP and DAT files are recommended. BB processes all
submissions in a standardized way which ensures ease of
cross-experiment comparison. After the submitter uploads
the experiment data, the curator checks the data integrity
and computes the normalized expression measures, summary
statistics and graphs. Unique accession numbers are assigned
to each experiment for data access. Processed data, sequence
annotation and pre-computed analyses results are stored for
online access and analysis. Finally, BB generates MAGE-ML
and text files for batch download and data exchange. The
MAGE-ML files can be submitted to ArrayExpress or read
by many microarray data analysis programs.
BB uses open-source tools or tools free for academic insti-
tutions. The server uses RedHat Linux version 9. The website
dynamic web pages, and a MySQL 4.0 relational database
as the back end. The data pre-processing uses R (13), Biocon-
ductor and Perl. R is an open platform for statistical computa-
tion and Bioconductor is a project written in R for microarray
data analysis. Robust Multichip Average (RMA) (14) in the
affy package of Bioconductor and Affymetrix MAS 5.0 (15)
are used to compute normalized expression measures from the
raw expression values.
Data access policy
BB has secure and flexible account and data access manage-
ment, which allows data owners to protect their data before
publication and yet enables dispersed collaboration. The sub-
mitter can specify the accessibility to data of an experiment as
‘public’, ‘private’ or ‘group accessible’. Public access allows
any users to access data; private allows data to be viewed only
by the data owner; and group access allows group members to
access the data. Registered users can create groups and add
selected users to the groups to grant access to data from desig-
nated experiments. Reviewers can anonymously access data-
sets referenced by a manuscript to verify the conclusions using
reviewer’s login ID. All users are strongly encouraged to make
their data public as soon as possible.
DATA ANALYSIS AND VISUALIZATION
Microarray gene expression datasets are large and multivariate
in nature and require flexible approaches for analysis. Instruc-
tive data visualization and presentation of the data are indis-
pensable for users to efficiently mine the data and derive
meaningful biological interpretations. The visualization
pages are provided at different levels based on the query
hierarchy. Visualizing the expression data will aid users in
choosing suitable parameters for gene filtering and analysis.
The analysis and visualization tools can be accessed by using a
traditional pipeline of experiment analysis or searching for a
gene(s) of interest using sequence comparisons then finding
genes that behave similarly. The Supplementary Material
guides a user through some of the analysis tools available
at BB (http://www.barleybase.org/quicktour.php).
Data visualization for experiments
Experiment queries are typically the starting point for data
retrieval and analysis flow. Based on the information captured
for experiment design in BarleyExpress, the Experiment
Query allows users to search and browse the experiments,
protocols and array designs.
Quality checking and understanding the experimental data
are essential before conducting gene-centric analysis. Users
can navigate the expression values by hybridizations and
experimental factor, and check sample annotation. The sum-
mary statistics and visualizations allow users to quickly assess
experiment quality. Box plots and histograms of raw Perfect
Match (PM) intensities and normalized expression values are
used to check the distribution of the expression data and the
quality across hybridizations in an experiment. Histograms of
the PM values detect signal saturation, and help to quickly
catch problems such as incorrect scanner parameters. Side-by-
side boxplots of the normalized expression data are used to
assess normalization results. These boxplots are ideally almost
identical as shown in Figure 1.
At the hybridization level, pseudo-color images of PM
intensities are used for visual detection of spatial abnormal-
ities. Scatter plots and MVA plots show reproducibility and
variability among and/or between hybridizations or treat-
ments. These comparative scatter plots can range across
experiments, with x- and y-axes using hybridizations or treat-
ment means from different experiments sharing similarity in
experimental material or factors. In the MVA plots, the M is
the log ratio between two hybridizations and A is average of
the logged signal intensities. MVA plots can be regarded as a
Nucleic Acids Research, 2005, Vol. 33, Database issueD615
45?clockwise rotation of scatter plots for easier viewing of
Gene-centric expression data analysis tools
Following the initial experiment and hybridization explora-
tion, users can further filter data and create gene lists. Creating
gene lists is the first step in most gene-centric analysis for
microarray experiments. Saved gene lists can be fed to
advanced microarray data analysis and visualization methods.
BB provides a full range of gene filters by expression profiles
and by biological criteria. Gene-centric expression profiles for
single genes or gene lists are displayed as profile-plots (line
graphs) and heatmaps. Interactive profile-plots allow the user
to gain insight into the way treatments affect expression. An
‘Expression view’ (heatmap) explores genes with similar
expression profiles that may represent co-regulated genes.
Expression profile filters are mainly used to identify differ-
entially expressed genes (probe sets). The filters usually oper-
ate on a single experiment, but users may do cross-experiment
query for hypothesis generation. The filter can be a single filter
or a composite filter that is a combination of several filters
linked with various Boolean operators. Filters are based on
absolute value range, relative and absolute variation, fold
change, MAS5.0 Presence/Absence call or other variation
measures. Statistical test filters include most standard two-
sample and multiple-sample statistical methods for identifying
differentially expressed genes with multiple test corrections.
Co-regulated genes can also be identified. For cross-
experiment filtering, hybridizations from several experiments
are compared with each other. This functions like a virtual
experiment in silico using hybridizations from different
Biologically based filters use annotation keywords and
sequence similarity to group genes into a gene list. For the
ATH1 GeneChip, gene family and KEGG pathway filters are
available to find probe sets corresponding to enzymes from
interesting metabolic or regulatory pathways or a given gene
Users may import their own list of gene or probe set names.
Files of free text containing the gene names can be used
directly without tedious editing. Users may export gene
lists as tab-delimited text files for names, annotation or expres-
sion values. Gene lists can be compared in various combina-
tions: union of two gene lists, intersection of both gene lists
and unique genes in either gene lists. This is useful for com-
bining the results of different filters, such as biological and
methods. Analysis results are automatically saved, including
information about the methods, parameters and gene list
Many of the standard supervised and unsupervised pattern
recognition methods are implemented for online analysis.
Methods include hierarchical and k-means clustering, princi-
pal component analysis (PCA), self-organizing maps (SOMs)
and Sammon’s non-linear mapping. For each of the methods,
data can be transformed or scaled using logarithm-transforma-
tion, mean or median centering, and scaling based on the
standard deviation of the probe set in an experiment. The
pattern recognition results are visualized using expression
profile line graphs, dendrograms and heatmaps for the entire
gene list or for each subcluster. Each method also has its
specialized visual presentation, such as clustering plots for
partitions in k-means or partition grids for SOMs.
Gene function and GeneChip annotation
GeneChips may be searched for a sequence of interest by
performing a BLAST search against a particular GeneChip
(16). This search gives a list of exemplars and probe sets
on a particular GeneChip that match that sequence. This
page also allows users to access gene expression data from
Log2(PM) in BB4
1. BB4_H12. BB4_H23. BB4_H34. BB4_H45. BB4_H56. BB4_H67. BB4_H78. BB4_H89. BB4_H9
10. BB4_H10 11. BB4_H1112. BB4_H12 13. BB4_H1314. BB4_H1415. BB4_H15 16. BB4_H1617. BB4_H1718. BB4_H1819. BB4_H1920. BB4_H2021. BB4_H2122. BB4_H22 23. BB4_H23 24. BB4_H2425. BB4_H25 26. BB4_H2627. BB4_H27 28. BB4_H28 29. BB4_H2930. BB4_H3031. BB4_H3132. BB4_H3233. BB4_H3334. BB4_H3435. BB4_H3536. BB4_H36
same treatment factors. These data summarize the logarithm of the raw probe set perfect match (PM) expression values in experiment BB4 before normalization.
D616Nucleic Acids Research, 2005, Vol. 33, Database issue
BB. This type of search is particularly important for organisms
which have not been fully sequenced. Figure 2 shows the
results of finding a particular exemplar and its accompanying
annotation that links to plant genomic resources such as
PlantGDB, Gramene and GrainGenes. Contig alignments
(1)] and oligo probe information from the Barley1 and Arabi-
dopsis GeneChips can be displayed. The sequences can be
blasted against the NCBI, PlantGDB, TAIR or Rice genome
databases for additional annotation information.
The annotation page also links to expression data related to
the probe exemplar as shown in Figure 2. The user can look at
how this probe set is expressed in different experiments or
search for genes that behave similarly in certain experiments.
Probe sets with similar expression profiles as the selected
exemplar can be identified using correlation tests. These
genes may be used to create gene lists for further analysis
on a particular experiment or groups of experiments. This
type of analysis is critical in identifying co-regulated genes
that may be involved in similar biochemical pathways. The
results are displayed using heatmaps or profile plots. For more
detail, the raw probe pair PM and MM data can also be dis-
played to further investigate GeneChip response to a particular
hybridization as shown in Figure 2. Barplots with standard
deviation are plotted by hybridizations or by probe pair num-
bers, allowing comparison of intensities across hybridizations
for same probe, or across probe pairs for same hybridization.
As our data and understanding of the GeneChips accumulate,
we plan to exclude probe pairs that are known to be ineffective
from the analysis available to BB users.
Comparative genomic analysis
BarleyBase supports comparative genomics capabilities by
interconnecting links with established plant databases.
Barley1 exemplars are aligned to the sequenced model
plant rice genome browser in Gramene, and to other cereal
genomes for annotation information integration. Barley1
exemplars can also be queried for Triticeae map positions
in GrainGenes. Integrated links with PlantGDB facilitates
detailed gene prediction and contig view of the exemplars.
ATH1 exemplars, function and pathway information are
supported through links with TAIR. A series of BLAST
matches for any sequences on plant GeneChips with links to
GenBank and other major databases.
Choosing an experiment from different GeneChip platform
will automatically initiate cross-platform gene list creation,
where the best BLAST hits are used as match from other
platforms. Cross-platform gene list creation enhances com-
parative gene expression analysis to fully utilize microarray
data from different plant species.
Adherence to MIAME standards and controlled plant
organization of the volumes of data from a typical microar-
ray-based investigation. BarleyBase captures and stores all
applicable MIAME-compliant information and enforces
plant ontology and controlled vocabulary for experiments.
efficient presentation and
blocks at the bottom show the experimental factors laid out in a factorial experimental design. The raw probe intensity data PM (red) and MM (blue) levels for
exemplar Barley1_11969 in hybridizations 17 and 18 in experiment BB4 show the different responses across the probe pairs.
Nucleic Acids Research, 2005, Vol. 33, Database issueD617
BB explicitly captures factorial experiment design informa-
tion, enhancing the flow of experiment submission, data
analysis and data presentation. It makes data accessible at
each data level, from the experiment level to the individual
probe level. The online pipeline integrates a broad set of gene
query and display options with a full set of analysis and visua-
lization tools. Cross-experiment gene filtering and cross-
platform matching provide great flexibility in hypothesis
planned for the near future. First, BB will expand to support
the Affymetrix high-density GeneChips for maize, rice, soy-
bean and wheat that will be available soon, and will evolve
into PLEXdb, a comprehensive Plant Expression Data Base.
Second, data from spotted cDNA and long oligo microarray
platforms will also be added using open-source tools for inte-
grating cDNA microarray data processing and management,
such as the TM4 suite from TIGR (17) and BASE (18). Third,
plant ontologies for other species beyond barley will be
enhanced. These changes will begin to pave the way toward
comparative expression data analysis. Gene Ontology and
pathway information need to be adapted to BB for exemplar
annotation, which will allow functional gene expression
analysis with insight on how specific genes are involved in
biological processes. Fourth, expression analysis and visuali-
zation tool development will add new methods for gene
identification and pattern recognition, and enhance BB’s
web-based interactive visualization capabilities. Overlaying
expression data with Gene Ontology, gene network and path-
way analysis will be added to aid biological interpretation.
Cross-experiment, cross-platform and cross-species data
analysis and comparison capabilities will be enhanced for
BarleyBase is hosted at the Iowa State University Virtual
Reality Applications Center. Barley1 exemplar sequences
Nottingham Arabidopsis Stock Centre’s microarray database
(NASCArrays) shares ATH1 data. The BarleyBase project is
funded by the USDA National Research Initiative (NRI) grant
no. 02-35300-12619 and USDA-CSREES North American
Barley Genome Project.
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