CDK-Taverna: an open workflow environment for cheminformatics
© Kuhn et al; licensee BioMed Central Ltd. 2010
Received: 7 September 2009
Accepted: 29 March 2010
Published: 29 March 2010
Small molecules are of increasing interest for bioinformatics in areas such as metabolomics and drug discovery. The recent release of large open access chemistry databases generates a demand for flexible tools to process them and discover new knowledge. To freely support open science based on these data resources, it is desirable for the processing tools to be open source and available for everyone.
Here we describe a novel combination of the workflow engine Taverna and the cheminformatics library Chemistry Development Kit (CDK) resulting in a open source workflow solution for cheminformatics. We have implemented more than 160 different workers to handle specific cheminformatics tasks. We describe the applications of CDK-Taverna in various usage scenarios.
The combination of the workflow engine Taverna and the Chemistry Development Kit provides the first open source cheminformatics workflow solution for the biosciences. With the Taverna-community working towards a more powerful workflow engine and a more user-friendly user interface, CDK-Taverna has the potential to become a free alternative to existing proprietary workflow tools.
Small molecules are of increasing interest for bioinformatics in areas such as metabolomics and drug discovery. The recent release of large open chemistry databases into the public domain [1–4] 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. Commonly used in this context are workflow engines for cheminformatics, where numerous recurring tasks can be automated, including tasks for
chemical data filtering, transformation, curation and migration workflows
chemical documentation and information retrieval related workflows (structures, reactions, pharmacophores, object relational data etc.)
data analysis workflows (statistics and clustering/machine learning for QSAR, diversity analysis etc.)
The workflow paradigm allows scientists to flexibly create generic workflows using different kinds of data sources, filters and algorithms, which can later be adapted to changing needs. In order to achieve this, library methods are encapsulated in Lego™-like building blocks which can be manipulated with a mouse or any pointing device in a graphical environment, relieving the scientist from the need to learn a programming language. Building blocks are connected by data pipelines to enable data flow between them, which is why pipelining is often used interchangeably for workflow. Workflows are increasingly used in cheminformatics research [5, 6].
Existing proprietary or semi-proprietary implementations of the workflow or pipeline paradigm in molecular informatics include Pipeline Pilot  from SciTegic, a subsidiary of Accelrys or the InforSense platform from InforSense . Both are commercially well established but closed source products with a large variety of different functionality. KNIME  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. An overview of workflow systems in life sciences was recently given by Tiwari et al..
In 2005 we started to integrate our open source cheminformatics library, the Chemistry Development Kit (CDK) [11, 12] with Taverna [13–15], a workflow environment with an extensible architecture, to produce CDK-Taverna, the first completely free  workflow solution for cheminformatics, which we present here.
It makes additional use of other open source components such as Bioclipse  for visualization of workflow results, and Pgchem::tigress  as an interface to the database back-end for storage of large data sets.
The here introduced CDK-Taverna plugin takes advantage of the plug-in detection manager of Taverna for its installation. This manager requires a plug-in description XML file containing a plug-in name, a version number, a target Taverna version number, a repository location and a Maven-like Java package description, all provided by the plug-in's installation website: http://www.cdk-taverna.de/plugin/.
After adding this URL, the manager presents all available plug-in versions graphically to the user. In order to install the CDK-Taverna plug-in the user selects the desired version after which all necessary Java libraries are installed on-the-fly from the given installation website.
The CDK-Taverna plug-in is written in Java is published under the GNU Lesser General Public License (LGPL). Version 0.5.1.1 uses CDK revision 12084. Like Taverna itself the CDK-Taverna plug-in uses Maven 2  as a build system.
To integrate the CDK functionality, the plug-in makes use of the extension points provided by Taverna allowing dynamic discovery of the provided functionality. The following sections describe what extension points are used, and how molecular data is represented when flowing through the workflow.
Taverna's extension points
Taverna allows the execution of workflows linking together heterogeneous open services, applications or databases (remote or local, private or public, third-party or home-grown) . For the integration of these different resource types Taverna provides various interfaces and protocols for its extension. For example, it allows for easy access to webservices through WSDL  and SOAP .
List of available Service Provider Interfaces that can be used to create plug-ins for Taverna to provide additional functionality.
All workers in CDK-Taverna implement the CDKLocalWorker interface. It is used for the detection of workers by the CDKScavenger class which itself implements the Taverna SPI org.embl.ebi.escience.scuflui.workbench.Scavenger interface. Adding user interfaces for some of the workers requires an extension of the AbstractCDKProcessorAction which again 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.
The anatomy of a CDK-Taverna worker
To create a CDK-Taverna worker the Java class of this worker has to implement the CDKLocalWorker Interface. This interface defines that every worker has to define the following methods:
public Map <String, DataThing> execute(Map String, DataThing inputMap)
public String inputNames();
public String inputTypes();
public String outputNames();
public String outputTypes();
The method inputNames and outputNames return the names of the ports of each worker whereas the inputTypes and outputTypes methods return the names for the Java object types with its package declaration e.g. java/java.util.List for a List. Within CDK-Taverna chemical structures are passed around using the Java object java/org.openscience.cdk.applications.taverna.CMLChemfile
The worker allocation of CDK-Taverna by function.
Workers by function
Number of workers
SDFParser, CML Reader & Writer
SMILES Parser, SMILES Writer
InChI Parser, InChI Generator
Insert Molecules Into Database, Read Molecules From Database
AtomCount & LargestChain
AtomHybridization & BondsToAtom
K-Means, ART 2-A Classification
Substructure Search, Reaction Enumeration
Iteration over large data sets
Cheminformatics by definition deals with the discovery of chemical knowledge from large data collections. Because these data sources are usually too large to be loaded into memory as a whole, it is needed to loop over all data entries to process them one by one. Unfortunately, the architecture of Taverna 1.7 does not support such loops. CDK-Taverna, therefore, provides workers which act like FOR or WHILE loops, making use of Taverna's iteration-and-retry mechanism to allow workflows to process large data sets.
For database support the CDK-Taverna project uses the PostgreSQL  relational database management system (RDBMS) with the open source Pgchem::tigress  extension. This combination allows storage and fast retrieval of up to a million molecules without running into memory limitations. The Pgchem::tigress extension uses an implementation of the Generalized Search Tree (GiST)  of the PostgreSQL database. CDK-Taverna can use a local installation of the PostgreSQL Database with the Pgchem::tigress extension or can connect to a remote instance.
Scenario 1: Substructure Search
Scenario 2: Descriptor Calculation
Scenario 3: Iterative Descriptor Calculation
Scenario 4: Validation of CDK Atom Types
Scenario 5: Reaction Enumeration
Markush structures are chemical drawings which represent a series of molecules by indicating locations where differences occur. These locations are marked as Heterocyclic, Alkyl, or identified by an R group, enumerating a series of possible groups, such as Methyl, Isopropyl, and Pentyl. Markush structures are commonly used in patents for describing whole compound classes and are named after Eugene A. Markush who described these kind of structures firstly in his US patent in the 1920s.
In the process of reaction enumeration, Markush structures are used to design generic reactions. These reactions are usable for the enumeration of large chemical spaces, which includes the generation of chemical target libraries. The results of the enumeration have important applications in patent formulation and in High Throughput Screening (HTS). HTS experiments screen large amounts of small molecules, called molecule libraries, against one or more assays for testing for biological activity. A couple of years ago, the libraries used for a single HTS experiment consisted of up to 100.000 molecules. Nowadays, more targeted libraries of a reduced size of up to 1.000 molecules are used, but still commonly defined using Markush structures.
Scenario 6: Clustering Workflows
linear scaling of the input vector to values between 0 and 1,
a switch between deterministic random and random random for the selection of the vectors to process,
the definition of the convergence criteria of the clustering process,
the required similarity for the convergence criteria,
the maximum clustering time, and
a limit for the number of clustering steps and a range for the vigilance parameter that guides the ART 2-A algorithm.
With CDK-Taverna we have presented the first free and open cheminformatics workflow solution for the biosciences. It allows to link and process data from various sources in visually accessible workflow diagrams without any deeper programming experience. Processing of hundreds of thousands of molecules has been demonstrated and the upper boundary is only limited by the amount of available memory. The currently implemented workers allow the processing of chemical data in various formats, provides the possibility to calculate chemical properties and allows cluster analysis of molecular descriptor vectors. The use of the PostgreSQL database with the Pgchem::tigres cheminformatics cartridge provides access to chemical databases with up to a million molecules.
Availability and requirements
Project name: CDK-Taverna
Project home page: http://www.cdk-taverna.de
Operating system(s): Platform independent
Programming language: Java
Other requirements: Java 1.6.0 or higher http://java.sun.com/, Taverna 1.7.2 http://sourceforge.net/projects/taverna/files/taverna/1.7.2/
License: GNU Library or Lesser General Public License (LGPL)
Any restrictions to use by non-academics: none
The authors express their gratitude to the Taverna team as well as the CDK community for creating these great open tools, and like to thank Ernst-Georg Schmid for his support concerning the Pgchem::tigress functionality.
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