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%% BioMed_Central_Tex_Template_v1.06
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\title{CDK-Taverna: An open workflow environment for chemoinformatics}
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\author{Thomas Kuhn$^{1,2}$%
\email{Thomas Kuhn - thomas.kuhn@fh-gelsenkirchen.de}%
\and
Egon Willighangen$^1$%
\email{Egon Willighangen - egon.willighagen@gmail.com}
\and
Achim Zielesny\correspondingauthor$^2$%
\email{Achim Zielesny\correspondingauthor - achim.zielesny@fh-gelsenkirchen.de}
and
Christoph Steinbeck\correspondingauthor$^{1,3}$%
\email{Christoph Steinbeck\correspondingauthor - steinbeck@ebi.ac.uk}%
}
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\address{%
\iid(1)Cologne University Bioinformatics Center (CUBIC),%
Zuelpicher Str. 47, Koeln, Germany\\
\iid(2)University of Applied Sciences Gelsenkirchen, Germany\\
\iid(3)European Bioinformatics Institute (EBI), Cambridge, UK
}%
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\paragraph*{Background:} The recent release of large open access chemistry database in
the public domain generates a demand for flexible tools to process
them and discover new knowledge. To support Open Drug Discovery and
Open Notebook Science on top of these data resource, is is desirable
for the processing tools to be open-source and available for everyone.
\paragraph*{Results:} Here we describe a plugin for the open source workflow engine
Taverna based on the open source chemoinformatics library The Chemistry Development Kit
resulting in a open source workflow solution to attack chemoinformatics problems.
We have implemented more than 160 different workers to handle specific chemoinformtic tasks.
\paragraph*{Conclusions:} The combination of the two open-source projects
Taverna and the Chemistry Development Kit creates an open chemoinformatics workflow
solution.
\end{abstract}
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\section*{Background}
The recent release of large open Chemistry databases into the public domain
\cite{PubChem}\cite{IrwinJ2005}\cite{EBIDrugs2008} calls for flexible, open toolkits to process them.
These databases and tools will, for the first time, create opportunities for academia and third-world countries
to perform state-of-the-art open drug discovery and translational research - endeavors so far a domain of the
pharmaceutical industry. In order to enable this progress, the tools for processing these datasets must be free and open.
Within the Open Drug Discovery and Open
Notebook Science \cite{DeLano2005} scientists can participate in the research
and get unrestricted access to what has been learned \cite{GuhaR2006}.
This article describes the integration of various open source software packages to form an open
workflow engine for chemoinformatics and drug discovery.
These fields, like any other science, encompass sets of typical workflows.
These workflows can be grouped into different disciplines like bioinformatic,
chemoinformatic or statistical workflows. Areas calling for such
workflow support include
\begin{itemize}
\item Chemical data filtering, transformation and migration workflows
\item Chemical information retrieval related workflows (structures, reactions, object relational data etc.)
\item Data analysis workflows (statistics, clustering, soft computing/computational intelligence, QSAR/QSPR/pharmacophore oriented workflows)
\end{itemize}
The workflow paradigm allows scientists to flexibly create generic
workflows using different kind of data sources, filters and algorithms which fits the
ever-changing demand of current research.
In order to achieve this, library methods are encapsulated in Lego(TM)-like building blocks
which can be manipulated with a mouse or any such pointing device in a graphics environment.
These building blocks can be connected by piplines to enable data flow between them, which is why
"pipelining" is often used interchangably for "workflow".
Here, two open source tools were used to
create an open workflow environment for chemoinformatic solutions: Taverna
\cite{TomOinn2004}, a workflow environment with an extensible architecture, and the
Chemistry Development Kit (CDK)
\cite{Steinbeck2004a}\cite{Steinbeck2003a}, a chemoinformatics software library.
The resulting CDK-Taverna package is the first completely free workflow solution for chemoinformatics.
Existing proprietary or semi-proprietary implementations of the workflow or pipeline paradigm
in molecular informatics include Pipeline Pilot \cite{PipelinePilotWeb} from SciTegic, a subsidiary of
Accelrys or the InforSense platform from InforSense \cite{InforSenseWeb}. Both
are commercial well established but closed source products with a large variety
of different functionality. KNIME \cite{KNIMEWeb} is a modular data
exploration platform which uses a dual licensing model with the Aladdin free
public license. It is developed by the group of Michael Berthold at the
University of Konstanz, Germany. KNIME is based on the open-source Eclipse platform.
%ToDo Cite Wendy
\section*{Implementation}
The CDK-Taverna plug-in written in Java is published under the GNU Lesser
General Public License (LGPL).
%ToDo Cite the license and cite Maven
The plug-in uses like Taverna Maven 2 \cite{MavenWeb} as build system.
\subsection*{Taverna's extension points}
Taverna allows the execution of workflows linking together external
remote or local, private or public, third-party or home-grown, heterogeneous
open services, applications or databases. For the integration of these
different kind of resources Taverna provides various interfaces and
protocols for its extension. It allows for an easy integration
of webservices which use the WSDL \cite{WSDLWeb} or SOAP \cite{SOAPWeb}
protocol. The CDK-Taverna plug-in uses a local extension of Taverna. For the
local extension Taverna provides a list of different Service Provider Interfaces (SPI).
%TODO:At this position should be a table with a list of the SPI's
The CDK-Taverna project implements some of these extensions which lead to an
integration of the CDK functionality as so called Local Worker, running on the
same machine as the CDK taverna installation.
%TODO: Detailed description of the integration
All worker provided from the CDK-Taverna plug-in implement the
``CDKLocalWorker`` interface. This interface is used for the detection of each
worker, performed within the implementation of the
``CDKScavenger`` which itself implements the Taverna SPI
``org.embl.ebi.escience.scuflui.workbench.Scavenger``.
Adding user interfaces for some of the workers requires an extension of the
``AbstractCDKProcessorAction``,which itself implements the Taverna SPI
``org.embl.ebi.escience.scuflui.spi.ProcessorActionSPI``. The use of this SPI
allows the addition of, for example, file chooser dialogs for workers like file
reader or writer.
\subsection*{Plug-in installation}
The CDK-Taverna project uses the plug-in detection manager of Taverna
for its installation, which requires an XML file describing the plugin with information like the name ,
the version number, the target Taverna version number, the repository location and the Maven
like Java package description of the plug-in. After adding the plug-in
installation URL (http://www.cdk-taverna.de/plugin/) to the plug-in manager all
available plug-in versions will be graphically available to the user.
In order to install the CDK-Taverna plugin, the user selects the desired version and
all neccessary Java libraries are installed on the fly
from the given repository location.
\subsection*{Iteration over large datasets}
Chemoinformatics by definition deals with the discovery of chemical knowledge from large data collections.
Usually being to large to be loaded into memory as a whole, one needs to loop of these datasets to process them
piece by piece. Unfortunately the architecture of Taverna's current stable version do not support such loops.
CDK-Taverna therefore provides worker to build workflows which act like for or while loops
to process large datasets making use of Taverna's iteration and retry
mechanism.
%see section iterative qsar workflow
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Results and Discussion %%
%%
\section*{Results}
\subsection*{Current Status}
The CDK-Taverna plug-in provides approximately 160 different worker. The
allocation of the worker can be found in the following table.
\begin{table}[htbp]
\caption{The worker allocation of CDK-Taverna}
\begin{flushleft}
\begin{tabular}{|l|c|}
\hline
\textbf{Grouped worker} & \multicolumn{1}{l|}{\textbf{Number of worker}} \\ \hline
File I/O & 15 \\ \hline
Database I/O & 7 \\ \hline
Molecular descriptors & 42 \\ \hline
Atom descriptors & 27 \\ \hline
Bond descriptors & 6 \\ \hline
ART2A classificator and result analysis worker & 10 \\ \hline
SimpleKMean and EM clusterer (uses Weka) & 3 \\ \hline
SMILE tools & 2 \\ \hline
Inchi Parser & 2 \\ \hline
Miscellaneous & 50 \\ \hline
\end{tabular}
\end{flushleft}
\label{}
\end{table}
The miscellaneous worker are for example, the substructure filter, the
aromaticity detection, the atom typing or the reaction enumeration.
We will exemplify some of the components as part of example workflows described below.
\subsection*{Database I/O}
The CDK-Taverna project decided to use the PostgresSQL \cite{PostgreSQLWeb}
database with the open-source Pgchem::tigress \cite{PGChemWeb} extension. This
combination allows the storing and querying of molecules on the database using an implementation of the GIST index
of the PostgresSQL database.
\subsection*{Substructure Workflow}
A workflow to perform a substructure search can be done in different ways
depending on the type of input. In a first example the substructure workflow
performs a topological substructure search on a list of given molecules and a
given molecular substructure. (see figure~\ref{fig:substructureworkflow.ps})
The workflow inputs are a molecular substructure as SMILE
\cite{WeiningerDavid1988} and a list of structures stored in a file of the
format MDL SD \cite{DalbyArthur1992}. The structures which match the
substructure gets stored in MDL Mol \cite{DalbyArthur1992} files while the not matching structures will be
converted into the Chemical Markup Language (CML) \cite{Murray-RustP1999, KuhnS2007}.
This small workflow combines already 4 different molecular structure
representations and the use of a topological substructure filter.
Another substructure search workflow will do the substructure search directly
on a database. Therefore it uses functionality provided by the Pgchem extension
of the PostgresSQL database. This allows the use of SQL commands to perform a
substructure search e.g. ``SELECT id, molecule FROM molecules
WHERE molecule = (SELECT molecule from molecules WHERE id = 1)`` The
following workflow ~\ref{fig:database_substructure_search} will perform the
substructure search directly on the database. The molecules containing the
substructure will afterwards printed within a table and stored in a pdf file.
\begin{figure*}
\centering
\includegraphics[angle=0,clip=false,scale=.4]{pics/substructureworkflow.ps}\\
\caption{The workflow performs a topological substructure search on
molecules from an MDL SD file. The input of this workflow will be a SMILE
which represents the searchable substructure.}
\label{fig:substructureworkflow.ps}
\end{figure*}
\begin{figure*}
\centering
\includegraphics[angle=90,clip=false,scale=.4]{pics/database_substructure_search.ps}\\ \caption{The workflow performs a substructure search on the database.
The substructure is loaded from a MDL SD file. The output will be a pdf
file containing a tabular view of the molecules containing the
substructure}
\label{fig:database_substructure_search}
\end{figure*}
\subsubsection*{Descriptor Calculation Workflow}
This more complex example (see figure~\ref{fig:QSARWorkflow}) loads its
molecules from a PostgresSQL database. The perception of the atom types is the next step, after loading the molecules
from the database. In the following steps, the workflow gets for each molecule
its implicit hydrogens and goes through the detection of the Hueckel armaticity.
%Cite h�ckel aromaticity
The tagging of each molecule is needed in the process of the extraction
of the QSAR results from each molecule and the creation of a property vector.
The used QSAR worker provides the possibility to choose multiple QSAR
calculations which are provide from the CDK within an UI (see
figure~\ref{fig:QSARWorkerUI}).
\begin{figure*}
\centering
\includegraphics[angle=-90,clip=true,scale=.6]{pics/QSAR_Worker_UI_small.ps}\\
\caption{This user interface allows a selection of the available QSAR descriptors. The selected descriptors are calculated for each molecule during the execution of the workflow.}
\label{fig:QSARWorkerUI}
\end{figure*}
The result of this workflow will be a comma separated value (csv) text file
which contains the id of the molecule and the calculate property values.
\begin{figure*}
\centering
\includegraphics[angle=0,clip=false,scale=.3]{pics/QSAR_Workflow.ps}\\
\caption{The workflow calculates different QSAR properties for the given
molecules which gets loaded from a PostgresSQL database. The results of the
calculation will be stored in a csv file.}
\label{fig:QSARWorkflow}
\end{figure*}
\subsubsection*{Iterative QSAR Workflow}
The iterative QSAR workflow is more or less a hack which allows the user to
handle many thousands of molecules. This workflow (see
figure~\ref{fig:IterativeQSARWorkflow}) processes each molecule in the same
manner as the QSAR Calculation Workflow but it uses different database workers.
Instead of the one database worker ``Get Molecules From Database`` uses this
workflow three database worker. The ``Iterative Molecule From Database
Reader``, the ``Get Molecule From Database`` and the ``Has Next Molecule From
Database``. The first of the three worker is used to configure the database
connection and store it within an internal object registry. The second worker gets as input the id of the
database connection and loads the molecule from the database for the workflow.
But it loads only a subset of the original query using the SQL functions LIMIT
and OFFSET. The last database worker checks whether the loaded molecules are
the last of this query or if this query could load further molecules. If the
result of the query contains further molecules the output of this last
worker would be the text value ``true``. The last but the most essential worker
of the workflow is the ``Fail if true`` worker. This worker throws an
exception if it gets as input the value ``true``. This worker is set to be
critical for the nested workflow which means if it fails the whole nested
workflow fails. Taverna provides a retry mechanism for failing worker or nested
workflows. This mechanism is used to rerun the nested workflow as often as it
is configurated within the workflow or as long as the ``Fail if true`` worker
fails.
\begin{figure*}
\centering
\includegraphics[angle=0,clip=flase,scale=.3]{pics/Iterative_QSAR_Workflow.ps}\\ \caption{The workflow calculates different QSAR properties for the given molecules which gets loaded from a PostgresSQL database. The results of the calculation will be stored in a csv file.}
\label{fig:IterativeQSARWorkflow}
\end{figure*}
%%%%%%%%%%%%%%%%%%%%%%
\section*{Conclusions}
The open-source solution which is provided by the CDK-Taverna plug-in allows an
efficient and powerful linking between different resources without any need of
programming knowledge. It provides the possibility to process thousands of
molecules and is only limited through the available memory.
\section*{Availability and requirements}
\begin{itemize}
\item \textbf{Project name:} CDK-Taverna
\item \textbf{Project home page:} http://www.cdk-taverna.de
\item \textbf{Operating system(s):} Platform independent
\item \textbf{Programming language:} Java
\item \textbf{Other requirements:} e.g. Java 1.6.0 or higher
\item \textbf{License:} GNU Library or Lesser General Public License (LGPL)
\item \textbf{Any restrictions to use by non-academics:} non
\end{itemize}
%%%%%%%%%%%%%%%%%%
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\section*{Authors contributions}
Text for this section \ldots
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\section*{Acknowledgements}
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\section*{Tables}
\subsection*{Table 1 - List of available Service Provider Interfaces}
A packaged subset of extended classes or interface
of this table can be used to create a plug-in for Taverna which provides
additional functionality.
\par
\mbox{}
\par
\mbox{
\begin{tabular}{|c|}
\hline Interfaces\\ \hline
org.embl.ebi.escience.scuflworkers.java.LocalWorker \\ \hline
net.sf.taverna.perspectives.PerspectiveSPI \\ \hline
org.embl.ebi.escience.scuflui.spi.ProcessorActionSPI \\ \hline
org.embl.ebi.escience.scuflworkers.ProcessorInfoBean \\ \hline
org.embl.ebi.escience.scuflui.spi.RendererSPI \\ \hline
org.embl.ebi.escience.scuflui.spi.ResultMapSaveSPI \\ \hline
org.embl.ebi.escience.scuflui.workbench.scavenger.spi.ScavengerActionSPI \\ \hline
org.embl.ebi.escience.scuflworkers.ScavengerHelper \\ \hline
org.embl.ebi.escience.scuflui.workbench.Scavenger \\ \hline
org.embl.ebi.escience.scuflui.actions.ScuflModelActionSPI \\ \hline
org.embl.ebi.escience.scuflui.spi.UIComponentFactorySPI \\ \hline
\end{tabular}
}
\end{bmcformat}
\end{document}

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