Group Title: BMC Bioinformatics
Title: Phenotypic and genotypic data integration and exploration through a web-service architecture
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Title: Phenotypic and genotypic data integration and exploration through a web-service architecture
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Language: English
Creator: Nuzzo, Angelo
Riva, Alberto
Bellazzi, Riccardo
Publisher: BMC Bioinformatics
Publication Date: 2009
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Abstract: BACKGROUND:Linking genotypic and phenotypic information is one of the greatest challenges of current genetics research. The definition of an Information Technology infrastructure to support this kind of studies, and in particular studies aimed at the analysis of complex traits, which require the definition of multifaceted phenotypes and the integration genotypic information to discover the most prevalent diseases, is a paradigmatic goal of Biomedical Informatics. This paper describes the use of Information Technology methods and tools to develop a system for the management, inspection and integration of phenotypic and genotypic data.RESULTS:We present the design and architecture of the Phenotype Miner, a software system able to flexibly manage phenotypic information, and its extended functionalities to retrieve genotype information from external repositories and to relate it to phenotypic data. For this purpose we developed a module to allow customized data upload by the user and a SOAP-based communications layer to retrieve data from existing biomedical knowledge management tools. In this paper we also demonstrate the system functionality by an example application of the system in which we analyze two related genomic datasets.CONCLUSION:In this paper we show how a comprehensive, integrated and automated workbench for genotype and phenotype integration can facilitate and improve the hypothesis generation process underlying modern genetic studies.
General Note: Periodical Abbreviation:BMC Bioinformatics
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General Note: M3: 10.1186/1471-2105-10-S12-S5
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BMC Bioinformatics Bioled Central


Research I

Phenotypic and genotypic data integration and exploration
through a web-service architecture
Angelo Nuzzo', Alberto Riva2 and Riccardo Bellazzi*1


Address: 'Department of Computer Engineering and Systems Science, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy and 2Department of
Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, USA
E-mail: Angelo Nuzzo angelo.nuzzo@unipv.it; Alberto Riva ariva@ufl.edu; Riccardo Bellazzi* riccardo.bellazzi@unipv.it
* Corresponding author



from Bioinformatics Methods for Biomedical Complex Systems Applications (NETTAB2008)
Varenna, Italy 19-21 May 2008

Published: 15 October 2009
BMC Bioinformatics 2009, 10 O(Suppl 12):S5 doi: 10.1186/1471-2105-10-SI 2-S5


This article is available from: http://www.biomedcentral.com/1471-2105/10/S12/S5
2009 Nuzzo et al; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Abstract
Background: Linking genotypic and phenotypic information is one of the greatest challenges of
current genetics research. The definition of an Information Technology infrastructure to support
this kind of studies, and in particular studies aimed at the analysis of complex traits, which require
the definition of multifaceted phenotypes and the integration genotypic information to discover the
most prevalent diseases, is a paradigmatic goal of Biomedical Informatics. This paper describes the
use of Information Technology methods and tools to develop a system for the management,
inspection and integration of phenotypic and genotypic data.
Results: We present the design and architecture of the Phenotype Miner, a software system able
to flexibly manage phenotypic information, and its extended functionalities to retrieve genotype
information from external repositories and to relate it to phenotypic data. For this purpose we
developed a module to allow customized data upload by the user and a SOAP-based
communications layer to retrieve data from existing biomedical knowledge management tools. In
this paper we also demonstrate the system functionality by an example application of the system in
which we analyze two related genomic datasets.
Conclusion: In this paper we show how a comprehensive, integrated and automated workbench
for genotype and phenotype integration can facilitate and improve the hypothesis generation
process underlying modern genetic studies.



Background hypothesis-free research that has been made possible by
One of the most challenging goals of current biomedical these technological advances opens unprecedented new
research is to link the genotypic and phenotypic opportunities for studying biological systems on a large
information generated by high-throughput experimental scale, at a low cost, and with a holistic perspective that
technologies [1]. The shift from hypothesis based to promises to expand our understanding of biological



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Dpen Access I







BMC Bioinformatics 2009, 1O(Suppl 12):S5


processes and of their connections with clinically
relevant outcomes. The price to pay for this paradigmatic
shift is that researchers will increasingly need to handle
very large volumes of heterogeneous data, both gener-
ated by their own experiments and retrieved from
publicly available repositories of genomic knowledge.
Integration, exploration, manipulation and interpreta-
tion of such data therefore need to become as automated
as possible. The "traditional" data inspection and
analysis methods are quickly becoming inadequate in a
scenario in which an investigator can sample hundreds
of thousands of variables in parallel: not only ad-hoc
analysis methods need to be developed in order to
address problems related with the statistical significance
of the analysis results, but all phases of the scientific
discovery process (hypothesis generation and testing,
background knowledge gathering, experiment design,
interpretation of results, generation of new knowledge)
will have to be adapted to this new reality. In an era in
which an entire new genome can be sequenced and
annotated in a matter of days, it will become essential to
be able to automatically link new observations and
findings to pre-existing knowledge. Finally, new data
storage and retrieval systems will need to be adopted in
order to handle the unprecedented volumes of data and
information being generated in an efficient and produc-
tive way. These tools, however, should be easily
accessible to the broad research community, facilitating
the discovery process by providing high usability and
effective automation. Information Technology will there-
fore play an increasingly crucial role in modern
biomedical and translational research [2], by developing
the methods and tools that will allow researchers to
bridge the gap between biomedical research and clinical
applications. The ability to effectively address the
challenges outlined above will have a direct, dramatic
impact on the speed, accuracy and effectiveness of the
scientific progress in all areas of the life sciences.

We therefore propose the application of data warehouse
concepts to facilitate the investigation of biomedical
data by researchers lacking technical expertise and
database skills [3]. Our system, called Phenotype
Miner, provides a simple and effective way to organize,
represent and navigate phenotypic data along multiple
dimensions, and to select subsets of subjects based on
one or more phenotypes of interest. Our current aim is
to turn the system into a general tool for hypothesis
generation, experiment design, automated annotation
and biomedical data management, by relating pheno-
typic data to genomic knowledge. In this paper we
describe the overall architecture of the system, which
exploits a distributed architecture based on Web Services
to integrate the Phenotype miner with two additional
modules that support automated hypothesis generation


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process as an integral part of modern translational
research. The first one is the Data Uploader, a tool to
parse user-provided data sets of phenotypic data and to
store them in the Phenotype Miner's database. The Data
Uploader modular architecture makes it possible to
easily customize both the parsing method (to support
different data formats and representations) and the way
in which data are stored in the database (to adapt them
to the most appropriate structure for the specific analysis
being performed). The second module allows the system
to access external resources providing background
genomic information and knowledge through a standard
Web Services protocol. This tool can be used, for
example, to automatically link the phenotypes under
study with available genotype data on the basis of pre-
existing knowledge about the relationship between the
phenotypes and genomic markers.

In the following we present a detailed description of the
system's architecture and usage, we illustrate the most
important technological and methodological solutions
we adopted for its implementation, and we present an
application example. Using two sample datasets, the first
one containing a dozen heterogeneous clinical measure-
ments on about one hundred individuals and the second
one containing their genotypes obtained with a SNP
microarray, we demonstrate the system's usability and
we highlight the ways in which it can be used to answer
complex questions about genotype-phenotype correla-
tions.


System description
The Phenotype Miner, the core module of the system,
was developed as an application of data warehousing to
the domain of genetic studies. These studies rely on the
integration and manipulation of large amounts of
heterogeneous data, including genotypic, phenotypic
and genealogical information, and therefore pose sig-
nificant challenges related with structuring the data and
querying them effectively. In particular, we showed how
the definition and interpretation of phenotypic data can
be improved through a multidimensional analysis
approach [3].


Data Warehouse techniques: the OLAP engine for
data exploration
While a normalized structure (i.e, one based on the
Entity-Relation model) may be preferred for a correct
management of the database in terms of data integrity
and reliability, the adoption of query-oriented models
that reflect the logical structure of the data elements
greatly facilitates the task of filtering the data on the
basis of the desired combination of phenotypes and
patient features. Thus, we used a logical database design


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technique to support end-user queries in a data ware-
house called "dimensional modelling", and we adopted
a data structure called "star-join schema", that has
become a standard for data warehouse applications.
Unlike the Entity-Relation model, a dimensional model
is very asymmetric. There is one large dominant table in
the center of the schema, called fact table. It is the only
table in the schema which is connected to the other
tables with multiple joins. Such other tables, called
dimensional tables, only require a single join to be
referenced by the fact table [4].

Typically a clinical database can be modeled by a star
schema in which each record in the fact table represents a
combination of a clinical measure and its values on a
specific date for a specific patient. Therefore, the
dimensions are individuals, measurement time and
measurement values: all of them can be further specified
using a snowflake model, that is, a model in which a
given dimension has relationships to other attributes of
the same dimension (used to re-normalize complex
dimensions in order to eliminate redundancy) [5].

Multidimensional analysis is implemented by software
tools called OLAP (Online Analytical Processing)
engines. Unlike Online Transaction Processing (OLTP),
where typical operations read and modify individual and
small numbers of records, OLAP engines deal with large
quantities of data in real time, and operations are
generally read-only. The term "online" implies that even
though huge quantities of data are involved typically
many millions of records, occupying several gigabytes -
the system must respond to queries fast enough to allow
an interactive exploration of the data.

Moreover, formalization of the phenotype definition is
needed to implement automated query generation. The
definition and use of a formal phenotype definition
allowed us to implement an automatic query generation
tool, suitable for users who may not possess the
necessary technical skills in query languages and
database manipulation.

As a results, we developed the Phenotype Miner
system, which included three main components: i)
the Phenotype Editor, for the automated definition of
phenotype queries, ii) a customized version of the
Mondrian [6] OLAP engine for dynamic data inspec-
tion, iii) the PedLauncher [7] plug-in, used to map
phenotype information onto the population pedigree
when needed [8]. A detailed schema of the data
structure and components interaction is shown and
described in Figure 1, while the Web interface is
presented in Figure 2.


Figure I
Phenotype Miner components schemas. An overview
of the Phenotype Miner components and their interaction.
The "star-schemas" section shows the data modelling
schemas to perform a multidimensional analysis.


B101 Lib's Phenotype Miner


Phenotype 1
Editor I


MaurOm
Valuo

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:*All Genders *AII Al ribuleas %;*A Indlys -.1 -I ?1- 9_



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Figure 2
Main Web page. The main page of the Web interface of
the system. All modules are available to the user from this
main panel, and in particular the table in the OLAP engine
section can be dynamically expanded by simply clicking on
rows, columns and cells to examine the data according to the
desired stratification strategies.


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The Data Uploader
Phenotypic datasets are, in general, highly complex and
deeply structured: the definition of the value types and
ranges of the individual data elements, of the relation-
ships that exist among them, and of the role they play is
a function of the study being performed, and the
structure of the database that will hold them should
reflect the specific purpose for which they are going to be
used. Moreover, phenotypic data comes in a very wide
variety of formats, encodings and representations. It is
therefore impossible to develop a single, universal
method to import phenotypic data into a general-
purpose tool such as the Phenotype Miner. Instead, we
developed a modular component, the Data Uploader,
which acts as the interface between the user-provided
data source and the database. As described below, the
Data Uploader can easily be reconfigured to adapt to
changes in the database structure or to accept different
input data formats. In this way, the Phenotype Miner can
be used as an off-the shelf data mining tool, which is
independent of the original data source used.


The SOAP interface
The final component of the system is a module to
communicate with external resources through a suitable
Web Services interface. According to the W3C, a Web
Service is defined as "a software system designed to
support interoperable machine to machine interaction
over a network" [9]. Web Services are Web-based
communication protocols that allow a program to access
one or more services available on a remote system over
the network. The protocols specify how to define, locate,
implement, and invoke services. In the case of SOAP
[10], the most widely used protocol in the Web Services
context, messages are encoded as XML documents and
are transmitted between client and server using the
HTTP/HTTPS protocols.

We developed a SOAP client interface by which the
Phenotype Miner can access external repositories of
genomic knowledge that support the Web Services
protocols. In particular, we implemented methods to
interact with Genephony, an online tool for genomic
dataset annotation [11]. In the current prototype, we
exploit the functionalities provided by Genephony to
automatically find SNPs related to a Mendelian pheno-
type by searching the OMIM database [12].

Thanks to the addition of the two above-described
modules, the Phenotype Miner becomes a complete,
general and flexible tool to integrate phenotypic and
genomic information. In the most common usage
scenario, the user imports patient phenotypic data into
the database using a suitable Data Uploader and uses the


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Phenotype Miner components (Phenotype Editor, OLAP
Engine, PedLauncher) to perform the desired selection
and filtering operations on the dataset. The user may
then use the available external knowledge bases to
integrate the phenotypic data with genomic information
in order to help in their interpretation or correlate them
with available data at the genomic level. Finally, the
resulting dataset can be browsed or exported for
subsequent analysis.

Methods
The system was entirely developed using the Java
programming language and related technologies and
products. It is implemented as a servlet-based applica-
tion, hosted by an Apache Tomcat Web server, and relies
on a MySQL local relational database as the data
warehouse. User interaction with the system takes place
through a Web-based interface, and communication
with external tools and resources also takes place over
the Web. An overview of the system's architecture and of
its components is given in Figure 3.

The phenotype editor and the OLAP engine
Final users can create phenotype definitions using a
graphical wizard developed in JAVA programming
language. The tool runs as a Java Web Start application
in order to exploit both the development facilities
provided by stand-alone GUI APIs and to keep it
available as a Web tool (instead of downloading and
running it as a separate application). It interacts with two
sections of the non-normalized database, i.e. the star
schema of the clinical data and the phenotypes defini-
tion tables. The conditions (attributes and their values)


---- l\ ----
PM ~ ~ ',se e .r:r']


Figure 3
System components overview. An overview of the
system components. The column on the left highlights the
logical sections of the project.


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BMC Bioinformatics 2009, 1O(Suppl 12):S5


may be defined by combo boxes, which provide lists of
attributes according to the measures table of the star
schema, suggesting the admissible ranges of values for
each attribute. Once the rules are defined, the corre-
sponding SQL string is created by merging conditions by
AND operators, in order to: i) store the rules in the
phenotype section tables, and ii) select the subgroup of
individuals satisfying the conditions and storing the
individuals-phenotype relationship. In the same way, it
is possible to use another graphical panel to select
previously defined phenotypes to be merged together by
OR operators to create a new phenotype, which is then
saved in the phenotypes section of the database.

When the OLAP engine starts, it reads an XML file
containing the data definition. The first page of the
system shows a set of check boxes containing the fields
of the underlying tables, so that the user may choose the
variable to be investigated (the phenotypes are among
them). Once the features have been chosen, the engine
loads information related to the individuals having that
phenotype. Then a visual inspection of the measurement
values can be performed expanding or collapsing cells of
the resulting table, so that the analysis can be executed at
different levels of detail.


The Data Uploader
The Data Uploader module allows the user to upload
study data from text files directly into the system's
database. It is composed of different modules (called
"datasheet parsers" in Figure 3), each of which is
designed to parse a different type of file. The Uploader
engine parses the supplied file using the appropriate
datasheet parser, and inserts the resulting data into the
database according to the star-schema structure. User
intervention is only needed to describe the logical
connections between the fields in the source files and
the data elements represented in the database. We
assume that the data have already been pre-processed
to ensure their quality, as data cleaning and filtering are
beyond the scope of this paper.

The final result is that the data are automatically stored
in the data warehouse according to the star-join structure
exploited by the Phenotype Miner.

It is important to note that data import is not necessarily
limited to parsing local files. As the use of Web Services
becomes more widespread, it is conceivable that a
growing number of repositories of phenotypic data
(e.g. clinical information systems) will make their data
available to authorized software agents using standard
interoperability protocols such as SOAP. In this case, it


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will be sufficient to implement a SOAP-based Data
Uploader to give our system the ability to automatically
acquire data from remote primary sources.


The SOAP interface
The SOAP Interface is a module that provides the
Phenotype Miner with the ability to access genomic
knowledge stored within a remote resource. While there
are several different types of messaging patterns available
in SOAP, the most common is the Remote Procedure
Call (RPC) pattern, in which one network node (the
client) sends a request message to another node (the
server) invoking the execution of a specified function on
a specified set of arguments. The server executes the
function, and automatically sends a response message
back to the client with the results of the computation.

The current prototype of our system includes a SOAP
interface to communicate with Genephony, an online
tool for the creation and annotation of large genomic
datasets. Using the services provided by Genephony, the
Phenotype Miner can perform complex data annotation
tasks in an efficient and straightforward way. As an
example, we used the SOAP interface to retrieve the set of
SNPs that are potentially related with a Mendelian
phenotype of interest. This is accomplished through the
following sequence of steps:

1. The client requests the creation of a session, in which
all subsequent processing will take place;

2. The client queries Genephony for all OMIM entries
containing the term (or terms) identifying the phenotype
of interest; Genephony stores the resulting set in the
current session;

3. The client asks for all genomic regions referenced in
the OMIM entries (ie, regions known to be associated
with the phenotype), and then for all SNPs belonging to
these regions;

4. Finally, the client retrieves the resulting list of SNP
identifiers.

The actual processing takes place on the Genephony
server, with several advantages: there is no need for a
local annotation database or for methods to access
multiple external resources, the communication between
the client and the server can be optimized (since only the
final result set is transmitted to the client, and not all the
intermediate steps), and the computational load on the
client application is minimized. It should be noted that
although OMIM only provides information about




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Mendelian diseases, the process just described could be
applied equally well to other sources of knowledge
about genotype-phenotype correlations (eg GAD, the
Genetic Association Database [13]).

Results
As an example scenario of the system usage, here we
consider a case-control genome-wide association study.
Association studies aim to find statistically significant
differences in the distribution of a set of markers
between a group of individuals showing a trait of
interest (the cases) and a group of unrelated individuals
who do not exhibit the trait (the controls) [14,15].
Among the several different kinds of association studies,
genome-wide association studies (GWAS) rely on a set of
genetic markers covering the whole genome [16]. This
strategy is motivated when there is little or no a priori
information about the location of the genetic cause of
the phenotype being studied. Although the power of a
genome-wide association study is usually low, such
studies are useful to pinpoint areas of the genome that
may contain candidate genes, and to guide a subsequent,
more targeted analysis step. As the costs of genotyping
decreases and the number of known, well characterized
genetic markers in the human genome increases, the
genome-wide approach represents an increasingly cost-
effective way of generating testable research hypotheses.

Genome-wide association studies typically rely on two
kinds of datasets, one collecting clinical (i.e. phenotypic)
measurements, and the other one storing the individuals'
genotypic markers values. In our case we assume the
genotype dataset to be the result of large-scale SNP
genotyping, while the clinical dataset may include any
kind of measurement or observation. Starting from these
two sources of information, the user's objective is to
identify a set of individuals sharing a phenotype of
interest, to identify a set of SNPs known to be related to
the phenotype, and finally to extract from the genotype
database the allelic values of the SNPs for the previously
identified individuals. These steps will be reflected in the
following sequence of operations in the system:

* upload the subjects' phenotype information
(i.e. clinical measurements);

* define the phenotypes of interest;

* find individuals showing the defined phenotype;

* find the set of genomic markers known to be related to
the phenotype;

* finally, retrieve and download genotypes for those
individuals and markers only.


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The starting point is the file containing clinical
measurements (in this example, an Excel spreadsheet).
The user can upload the data into the database through
the Data Uploader Web interface (Figure 4). The tool
shows the contents of the datasheet in several re-
organized menus, through which the user may specify
additional information, such as which columns to parse,
and the appropriate data type for each clinical measure-
ment and variable. The data are then automatically
inserted into the data mart on which the Phenotype
Miner relies.

Once the data are in the system database, the user may
start defining the phenotype to investigate using the
Phenotype Editor. Let us suppose that the user is
interested in studying individuals affected by diabetes-
induced hyperglycemia. This condition can be defined as
a set of logical statements on clinical measurements and
other variables: for example, the diabetes phenotype may
be defined as: "glycemia > 125 mg/dl AND gender =
both" (Figure 5). Once a phenotype has been defined in
this way, the system automatically generates and
executes a query to identify individuals that meet the
specified definition (Figure 6).

Internally, phenotypes are represented as filters, one
of the functionalities provided by the OLAP engine
(Figure 7). When the user selects one of them, the
dynamic navigation table in the Web interface main page
is automatically loaded with all the data regarding the
individuals showing the selected phenotype. The user
can then stratify the data at different levels of detail,
simply by expanding or collapsing the table rows and
columns (Figure 8).

The next step is to retrieve a set of markers that are
known to be related to the phenotypes. Through the
automatic SOAP request to Genephony described above,
the user can retrieve all the SNPs (if any) related to the
OMIM entry associated with the phenotype under study.
In this example, the dataset produced by Genephony in
response to the query contains about 48,000 SNPs. Of
course not all SNPs will be actually needed for the
purposes of the study, since many SNPs will be
redundant with each other because of their proximity,
and, more importantly, not all of them will be
represented on the genotyping microarray used in the
analysis.

Therefore, a further processing step consists in filtering
the resulting set of SNPs according to the specific
genotyping platform used in the study, and to other
desired properties (e.g., validated SNPs only). This step
too can be performed through a SOAP request to a
genomic annotation service such as Genephony.


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Figure 4
Data Uploader. Some of the interactive menus of the Data Uploader Web interface. After uploading one or more files,
the user may specify which measurements to store in the database, and the correspondences between fields in different files,
if needed.


Finally, the system needs to retrieve the allelic values of
the selected individuals only for SNPs that are known to
be related to the phenotype and present in the user's
database. Since the genotyping results may in general be
stored in a remote database, we have used the SOAP
protocol again to allow the Phenotype Miner to retrieve
them. The remote database provides a SOAP service that


takes as input a list of subject identifiers and a list of SNP
identifiers, and returns the encoded genotypes for the
specified subset of individuals and SNPs. The resulting
data table is proposed to the user in a common tabular
format, in which the selected individuals are listed in the
rows, and the SNPs genotype values are in the columns.
The table may be downloaded as a text file for further


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Figure 5
Phenotype generation panel. The Phenoype Editor panel
for phenotype generation. Menus are automatically
generated from the variables stored in the database. The rule
that defines the phenotype is composed combining multiple
conditions with the logical AND operator. A similar panel is
also available to combine phenotypes together using the
logical OR operator.


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PhemiyWe's DescflpaI
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Pheomi O's RA
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eow -BOTH


Figure 6
Phenotype Editor main panel. The main panel of the
Phenotype Editor, showing phenotype definitions
represented as a logical tree model. The top left frame
contains the list of phenotypes defined so far, the top right
frame shows the logical definition of the selected phenotype,
and the bottom frame displays the same definition in textual
form.



analysis through external genetic analysis software tools
(Figure 9).

So far, we have shown how to retrieve specific genotypic
information that is related to a phenotype of interest.
Using the same sequence of operations it is possible to
investigate whether genetic differences arise when
including other covariates in the phenotype definition.
For example, we could investigate if the SNP patterns
related to the previous definition of diabetes would be
different in individuals who also have an obesity
problem, by including conditions on BMI or cholesterol
values in the new phenotype definition (Figure 10).

Since the phenotype definition is different, the system
will not only retrieve a different set of patients, but also a
(possibly) different set of genetic markers. After


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SColumns I|x
SV V Measures
* V A Exams
IRows
v V Gender
* aV AttnbUtes
B V A Individual
7 Filter
B M Ptre.ot.',p %Phenotype Description=diaberes;


Measures
Value
I Exams
Gander Attributes I Individual BMI Cholesterol


+Ail Cenders *All Attributess *All Indivs 28.113 214,9

Figure 7
The OLAP engine Web interface. A detail of the OLAP
engine section of the Phenotype Miner. The first button of
the action bar allows the selection of the fields to be
visualized in the dynamic table. The phenotypes defined by
using the Phenotype Editor are listed as filters.






Exosums
O.nO.. ..ntT.. [ I 3M! C, JIs.lo. .
I ^*ABI.. t[.r.l|A n b .... .. .. H a.f Ir S l I: .? -
i ____ nail atrfbuses -Asll Irde.t i.1 '...* i .'.)


A Patleptidi Ph 1pOt 9 Gi<&dpri **niPirilmB *EArarnmEW r ia fCmegmory val

.Aj dj,0o..rm i .: I,. ,... i. ] .,, ,
le: ... ... ...... I |






Figure 8
Dill-down example. An example of a drill-down of the
table, showing detail data on the patient's phenotypes and on
their clinical variables.



performing association analysis in the two cases, it
would therefore be possible to compare the resulting sets
of associated markers directly, for example to determine
if some of them are associated with both phenotypes, or
to identify markers that are associated with only one of
the two conditions.


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,04







http://www.biomedcentral.com/1471-2105/10/S12/S5


Palrumeter,


fPnd SW.t from


G*ne ID.




fnd Sot e v


PfrWft04 4


ChPo so pheriOpype v


Reponos w r*tated to Oh phenotvpe


Choose ptefioetype


S NPs.
C* e : -r10OI4.0';rs1 Ol07&yrs10002l)
(Chdt Afn f* | A~ [ tJoh pC*4cctrcP "0 No lr aj eo& s w AArn f Reo'pos-e ream)


Respon'be:
Gp soRvo: cyeated bq 'host(dov o.
Stwbrg set 4 75 GP-OMNIM ob)c.s cretiE.
*e of 6c r GP-C1Nt &dend.
Sqtl o 4 CP-TAMSCtIPT deirved.
S* eo 141 GP-.SPw d4rved
Sot (6 P -SWNP dnved Artr fateeing (vYbdatd-f),

FINAL RESULT: 86 SWts maped an gsres rote4 to 46*bstes'.

Seq c*tet4 f


* Sep ID Chre emew. PosItoem
I rs3Sf4"70 chrlO 41997#32
I rs)s80597S chrlO 54191842
I rw3576811 I6 chrO 54196206
1 rsl406743 ctrlO ,54199S67
3 rs3$fl 4?4 chri 55406209
1 rs3SS26045 CNrI 54199265
I Fs35 7$54) 3Ar10 S4100591
I rs lS 3lZ)3 chrf 0 419B670
3 s3501480SO ctrlO !4399464
3 rs 400)i6 chrl0 5 419766
Z rs346S008 chrl0 54198752
1 rsM4 1091 chr1O 54199136


Irzdiv "urlOOl2O'




10.2/4,1/1 0.0
11.2/4.1'1 2/2


S. /4. 1/1. 2/4
16 ./4, 1-1, 4 /
17. 22, 1/1, 2 /4

,2./4.1/1 24/4
2O, &v4. [t 4!4


Figure 9
'Genetic Inspection' section. A summary of the Genetic Inspection section, by which SOAP-based requests are sent to
remove servers, using Web Services protocols, in order to retrieve both genomic information and genotype values. The final
result is a downloadable text file, which is formatted to be read by the most common genetic analysis software tools.


Thanks to the ability to access external resources for
genomic annotation, the system could then provide the
user with detailed information about the genomic
context of the markers thus identified (e.g. proximity


to genes, biological pathways the affected genes belong
to, etc). The system here described can therefore be used
to generate hypothesis about candidate genetic factors
potentially involved in disease development, through


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Acth*a


rslO0078V'


BMC Bioinformatics 2009, 10O(Suppl 12):S5








BMC Bioinformatics 2009, 1O(Suppl 12):S5


D Ph-mwe mbm
settig 1001S
phenotypes hk
hmilma~g_30
C01
dtab M&oh ibfhI
diabelesI
hkjh~bnu .
IlypeflbnSiOn
kmngem,


. C


obesity
T~ OR high-brra
AN 8-*hDMI 30 O
9 R c 0l
1 -AND Cholestefoi ,2000


Pienotype's Rule
Condlioln Fromn high _brni
BMI, 30 0
Gender BO1H
Condnwiom Fron col
Ciholeslefol > 200.0
Gender BO H


Figure 10
A more complex phenotype definition. A more
complex phenotype definition, which exploits the logical tree
model of the Phenotype Editor.


dynamic and powerful data exploration and annotation
features.



Conclusion
In this paper we presented a web-based software system
aimed at addressing the problem of managing and
exploring heterogeneous phenotypic and genotypic
information in an automated, integrated and easy to
use application. After describing the main system, called
Phenotype Miner, which allows a dynamical inspection
of clinical data, we described in more detail a set of
desirable system's functionalities that we developed in
order to: i) allow the user to upload experimental data,
and ii) retrieve genomic information from existing
biological knowledge bases and integrate it with user-
supplied data. The typical application scenario of our
system is in the context of studies aimed at investigating
genetic factors potentially underlying phenotypes of
interest.

We integrated different methodologies to develop the
various software components of our system. We used a
logical formalization for phenotype definition, a power-
ful graphical tool to define phenotypes, a data ware-
house approach for dynamic, multi-dimensional data
investigation, and a "Web Services" architecture to access
external sources of genomic data and knowledge. The
modular nature of the system makes it suitable for the
development of new applications that integrate clinical
data with already available external services and infor-
mation resources. Future developments will concern the
inclusion of new sources of information, such as
investigation results from the literature, and a post-


http://www.biomedcentral.com/1 471-2105/10/S12/S5




processing phase to provide specific output data formats
for genetic analysis software, as well as including some
preliminary statistical analysis.

A prototype version of the system is freely available at
the URL http://bioinfo.unipv.it/PhenotypeMiner. A pre-
populated example database is provided for demonstra-
tion purposes.


Competing interests
The authors declare that they have no competing
interests.


Authors' contributions
A.N. designed and implemented the Phenotype Miner
system described in this paper. A.R. provided assistance
with the development of the SOAP interface. R.B.
contributed to the overall design of this project and
supervised the Phenotype Miner project. All three
authors participated in writing the present paper and
approved it.


Acknowledgements
This work was supported in part by FIRB project "ITALBIONET Rete
Italiana di Bioinformatica", funded by the Italian Ministry of University and
Research, and by NIH grant ROI HL87681-01, "Genome-Wide Associa-
tion Studies in Sickle Cell Anemia and in Centenarians."

This article has been published as part of BMC Bioinformatics Volume 10
Supplement 12, 2009: Bioinformatics Methods for Biomedical Complex
System Applications. The full contents of the supplement are available
online at http://www.biomedcentral.com/1471-2105/10?issue=S 12.

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