# Genevestigator V3: A Reference Expression Database for   the Meta-Analysis of Transcriptomes

Authors: Tomas Hruz; Oliver Laule; Gabor Szabo; Frans Wessendorp; Stefan Bleuler; Lukas Oertle; Peter Widmayer; Wilhelm Gruissem; Philip Zimmermann
Journal: Advances in Bioinformatics
 (2008)

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## Abstract

The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene
expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling
ability of the system architecture, multiorganism data storage and analysis capability, and availability of
computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting
from new algorithmic and software development using a client/server architecture, large-scale manual
curation and quality control of microarray data for several organisms, and curation of pathway data for mouse
and Arabidopsis. In addition to improved querying features, Genevestigator V3 provides new tools to analyze
the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into
expression modules, and to model expression responses in the context of metabolic and regulatory networks.
Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery
research and hypothesis validation.


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## Body

## 1. Background

Systems biology explores interactions between
components of a biological system. Frequently, the termsystems biology is used in the context of mathematical modeling of cellular networks in
unicellular organisms or in specific cell types of multicellular organisms. A
less prevalent but fundamental approach is the modeling of networks at the
organism level by including quantitative information about where, when, and why
specific components are active. Dissecting the organism network (in contrast to
the cellular network) of multicellular organisms helps to understand mechanisms
that drive development, the functions of system components, and regulatory
networks that trigger responses to environmental cues or to genetic
perturbations.To build such networks at multicellular scale, both
expression data and contextual information have to be analysed simultaneously.
Although gene expression data and statistical tools are now widely available
[1–4], the simultaneous analysis
of hundreds or thousands of gene expression arrays in their biological context
remains a major challenge for biologists. Additionally, the contextual
information (sample metadata) of published experiments is not readily available
in a systematic form, but frequently in plain text descriptions, published
articles, or in author's personal notes. Collecting and annotating sample
metadata using accepted ontologies and combining them with the large amount of
published expression data opens new perspectives in
the analysis of transcriptomes across many cell types, tissues, organs, and
conditions. Additionally, it allows to create large reference expression
databases representing a virtual mirror of life's biological processes.In [5, 6], we presented a novel approach to assemble public
expression data for mouse and Arabidopsis into context-related profiles
(metaprofiles) and provided easy online access to this database. The earlier
versions of Genevestigator, however, offered only a limited number of analysis
tools and the original architecture did not allow it to scale with the rapidly
increasing number of users and gene expression information from multiple
organisms. We therefore developed Genevestigator V3, which is based on a new
framework of the original Genevestigator concept. In particular, we
(i)
curated and quality controlled more than 20,000
Affymetrix expression microarrays from human, mouse, rat, Arabidopsis, and
barley,(ii)
manually curated pathway reaction networks for mouse
and Arabidopsis (see Table1),(iii)
developed an architecture to distribute the analysis
task between the client and the server such that computationally intensive
algorithms can run on the client computer,(iv)
designed a database model which provides high
scalability and organism-independent modeling,(v)
performed major improvements in existing analysis
tools,(vi)
implemented a more efficient and flexible workflow
including workspace storage and figure export, and(vii)
developed a set of new analysis tools for biomarker
identification, clustering, biclustering, and pathway analysis.Table 1
Currently available data. Overview of the number of quality controlled and annotated arrays, partitioned into types of biological contexts for which expression data is available in the database (status: March 2008). For mouse and Arabidopsis, the number of curated pathway reactions is indicated.
HumanMouseRatArabidopsisBarleySize of arrays10 k20 k47 k12 k40 k8 k31 k22 k22 kNumber of Affymetrix arrays1109378627823071196721468583110706Anatomy–  total of nodes in
anatomy tree97174185150133108634323–  central nervous system33465139162920–  cell culture / primary cells1339291921109–  other anatomy parts518910492966934Development–  number of stages
available998121267106Stimulus (external perturbations)–  total number of
treatments81295243122811075516839–  chemical treatments13973243344525403–  diseases, biotic
stresses2361341212401824–  other external
perturbations*451371756735583011012Mutation (internal perturbations)–  number of mutant models
or cell lines14251171005216717Reaction networks– Number of reaction nodes———945———1199—*includes: surgery, stress, hormone, growth factors and cytokines, exercise, caloric restriction, fasting, peptides, several tumor types, light irradiation, and many more external perturbations.


## 2. Construction

The large number of simultaneous users, the increasing
load of server side data processing, and the need to implement advanced
analysis tools required the choice of a technology that shares the load between
server and client machines. In fact, some analyses such as the clustering of
large data matrices require considerable CPU time, frequently overloading a
single machine if multiple clustering jobs are sent to it simultaneously. At
the same time, we looked for a technology that minimizes hardware and software
adaptations on the user side.A careful analysis of possible technologies revealed
that a combination of Java and relational database (RDB) technologies would
represent the optimal combination to handle the complex problem of providing a
large centralized database and complex, highly interactive analysis tools to
users in the biology community. We therefore verified the penetration of Java
(and its various versions) in the Genevestigator user base [7], to be sure that a
sufficiently powerful version of Java is installed on most user computers.Genevestigator V3 comprises four main components: (1) a
database, (2) an application server, (3) a Java client application, and (4) a
website for user support and management. More specifically, Genevestigator V3
is a multitier Java/Java/Mysql system in which a thick Java client communicates
with a farm of server machines running a Tomcat application server which, in
turn, communicates with a distributed system of database instances to retrieve
the queried data (see Figure1). Except for a number of specific cases, most of
the scientific computing is done on the client side of the application, which
provides a highly interactive and integrated view of the analyzed data. This is
enabled by Java 2 Swing technology and a custom communication protocol between
the Java client and the server farm.Figure 1
System architecture of Genevestigator. The white spaces show the possible boundaries between computers. The
application server modules are running in a Tomcat web container and
communicate with the database through the JDBC protocol. A thick Java applet
reads data from an application server and a cache subsystem through a custom
RPC protocol tunneled over http/https. A load-balancing subsystem ensures that
the clients can communicate with a scalable cluster of application servers.
Moreover, the application servers can be transparently configured to work with
a database cluster of an arbitrary size.The Genevestigator V3 client application is a signed
applet loaded and running within the Internet browser. New versions of the
client are distributed on the fly from the server once they are available.The task of the application server is to cache and
quickly provide the compressed data to the client computer, where they could be
processed with visualization and clustering algorithms. The application server
provides also the data-mining functionality with presampling algorithms to
quickly retrieve the data according to biologically meaningful ranking functions.The database system behind the application server was
carefully designed to accommodate multiorganism data and to provide the
possibility to switch quickly between different organisms. Another challenge
was posed by the experiment classification database subsystem, which stores the
measurement data according to ontologies representing the sample experimental
context, in particular the anatomy part, developmental stage, stimulus applied,
and genetic background. In a further database subsystem, metabolic and
regulatory pathways are mapped to multiple organisms and microarray data.


## 3. Content and Tools

In order to build the Genevestigator reference
expression database, we collected hundreds of Affymetrix experiments from the
public domain, controlled the quality of the data using several Bioconductor
packages (see [6]),
and manually annotated them according to systematic ontologies. As of March
2008, a compendium of more than 20,000 Affymetrix arrays from human, mouse,
rat, Arabidopsis, and barley has been made available for meta-analysis through
Genevestigator. This gives users the opportunity to look at how genes are
expressed in a very wide variety of contexts. For example, the human dataset
currently comprises expression monitoring across more than 330 different
conditions, including 89 chemicals, 64 different diseases, 20 growth factors,
17 infection causes, and 36 different tumor types (see Table 1 in Supplementary Material available online at doi:10.1155/2008/420747). An overview of the
number of microarrays, pathways, and biological contexts for which expression
data is available is shown in Table 1. In Genevestigator, the biological
context is embedded into a time-space-response architecture and is given by the
terms “Anatomy” (space), “Development” (time), and “Stimulus”
or “Mutation” (external or internal perturbations).An overview of the Genevestigator V3 data- and
workflow is shown in Figure2. Thanks to its novel software architecture and
the complete redesign of workflows using usability criteria, Genevestigator V3
allows to work easily with several organisms and multiple lists of genes at the
same time. Four toolsets are available to analyze these transcriptomes: (1)
Meta-Profile analysis, (2) Biomarker Search, (3) Clustering Analysis, and (4)
Pathway Projector. Each toolset comprises several tools that focus on a specific
type of analysis. Besides significantly improving the Meta-Profile analysis
tools, the Biomarker Search, Clustering Analysis, and Pathway Projector
toolsets largely comprise novel tools that were not available in previous
Genevestigator releases. More details about these toolsets are provided on the
Genevestigator website (https://www.genevestigator.ethz.ch/). In brief;Data flow and analysis cycle. Schematic overview of
Genevestigator V3. (a) Data flow through the Genevestigator system. Microarray
experiments are performed by the research community and either stored in public
repositories or sent directly to Genevestigator. Data and annotations are
curated and quality controlled by Genevestigator curators and stored in the Genevestigator
system (see Figure 1). (b) Expression analysis cycle. The user starts at the
Genevestigator website, from where the client application is launched. Four
toolsets allow different types of analysis. Results obtained are used either to
confirm an existing hypothesis or to create new
ones that can be validated in the laboratory. Figures from Genevestigator can
be exported in a variety of formats. The website
provides user support and a community exchange platform.
(a)(b)TheMeta-Profile analysis toolset includes
several tools that provide information about when, where, and in response to what genes of interest are
expressed. Specifically, they visualize gene expression across experiments or
biological contexts such as anatomy, development, stimulus, and mutation.TheBiomarker Search toolset allows users to
easily identify genes that are specifically expressed in a set of biological
states (anatomy or development) or specifically up- or downregulated in
response to a set of perturbations (stimulus or mutation). An explanation on
how to use these tools is provided on the website.The clustering tools in Genevestigator V3 group genes
that show similarity in their summarized expression levels (i.e., in their
metaprofiles). These tools can also be used to cluster array-level profiles.
Two types of methods are currently provided in theClustering Analysis toolset: (1)
hierarchical clustering, and (2) biclustering. Both methods have been shown to
reveal biologically significant associations between biological components or
gene networks [8, 9].ThePathway Projector toolset allows the visualization of expression data on a pathway
or network level. Several conceptual features distinguish it from most
currently available pathway analysis tools. First, all pathways were verified
manually from the literature and modeled into a single reaction network, in
which each reaction is represented only once, even if it is shared between
several pathways. The user can find locations within the network using
classically defined pathway terms. Nevertheless, network analysis focuses on
identifying expression modules within the global reaction network, irrespective
of classical pathway boundaries. For example, the user can start with a single
reaction or pathway and create subnetworks by extending them with neighbor reactions
or pathways according to their expression. A second distinguishing feature is
the projection of expression metaprofile data onto these networks. The user can
create virtually any type of comparison selected from the database and project
it onto the network, for instance, compare one state of development with
another, a tissue against another tissue, or a treatment against its control
set.


## 4. Utility and Discussion

In contrast to the previous versions, Genevestigator
V3 integrates all the different analysis tools. For example, marker genes
identified using the Biomarker Search tool can be further analyzed by switching
to the Meta-Profile or Clustering analysis toolsets. Another major improvement
is that all organisms are integrated into the same user client interface,
allowing the user to study genes from several organisms in parallel and in the
same session.Besides these analysis tools, several novel usability
features have been added to the client, such as saving workspaces, exporting
figures in Web or print resolution, saving pathway views, or exporting lists of
genes. Users can submit their workspaces or pathway views to be placed on the
community page of the Website. This allows other users to view the
corresponding results without having to upload arrays or genes again, or to
redesign network structures. Additionally, a role-based access control system
allows confidential data to be visualized in the context of all other
experiments in the database, without being seen by nonauthorized users. Upon request,
confidential data from laboratories are quality-controlled and entered into the
database by Genevestigator curators to ensure comparability and systematic
annotation.It is generally accepted that the integration of
multiple data types will be essential to model biological systems.
Transcriptomics data from single experiments provide an informative but
selective view of biological processes. There is an increasing evidence,
however, that mining microarray data simultaneously from many different experiments
and organisms make this type of data highly informative and valuable for
functional genomics and systems biology applications. The Genevestigator V3
discovery tool and reference database therefore covers a broad range of academic and commercial
research interests. Until now, most researchers have used Genevestigator in
molecular biology research for discovery or experimental validation. More
recent tools in Genevestigator V3 now allow researchers to develop applications
in plant and animal genomics, toxicogenomics, biomarker discovery, crop
biotechnology, disease and clinical research, as well as pathway
prioritization. Its multiorganism capabilities allow researchers to perform
cross-species studies to confirm orthologs and facilitate gene function
discovery.Genevestigator tools are made available in three
forms: OPEN-ACCESS, CLASSIC, and ADVANCED. For academic users, the first two
are freely available, while access to ADVANCED is made available for a moderate
subscription fee (in the range of reagent kits that are frequently used in the
lab) to support our costs of curation and development. The tools and features
available for each form are described on the Genevestigator website.


## 5. Conclusion

In summary, Genevestigator V3 provides a large and
continuously growing reference database of systematically annotated and
high-quality microarray data from several organisms. Powerful but user-friendly
tools for the meta-analysis of transcriptomes allow to visualize gene
expression through a large library of biological contexts, facilitating the
modeling of gene regulatory networks and the identification of specific
markers. The tool is accessible athttp://www.genevestigator.ethz.ch.



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