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1.  Tavaxy: Integrating Taverna and Galaxy workflows with cloud computing support 
BMC Bioinformatics  2012;13:77.
Over the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts.
In this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure.
Tavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis.
The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at
PMCID: PMC3583125  PMID: 22559942
2.  The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud 
Nucleic Acids Research  2013;41(Web Server issue):W557-W561.
The Taverna workflow tool suite ( is designed to combine distributed Web Services and/or local tools into complex analysis pipelines. These pipelines can be executed on local desktop machines or through larger infrastructure (such as supercomputers, Grids or cloud environments), using the Taverna Server. In bioinformatics, Taverna workflows are typically used in the areas of high-throughput omics analyses (for example, proteomics or transcriptomics), or for evidence gathering methods involving text mining or data mining. Through Taverna, scientists have access to several thousand different tools and resources that are freely available from a large range of life science institutions. Once constructed, the workflows are reusable, executable bioinformatics protocols that can be shared, reused and repurposed. A repository of public workflows is available at This article provides an update to the Taverna tool suite, highlighting new features and developments in the workbench and the Taverna Server.
PMCID: PMC3692062  PMID: 23640334
3.  U-Compare: share and compare text mining tools with UIMA 
Bioinformatics  2009;25(15):1997-1998.
Summary: Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the world's largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources.
PMCID: PMC2712335  PMID: 19414535
4.  A web services choreography scenario for interoperating bioinformatics applications 
BMC Bioinformatics  2004;5:25.
Very often genome-wide data analysis requires the interoperation of multiple databases and analytic tools. A large number of genome databases and bioinformatics applications are available through the web, but it is difficult to automate interoperation because: 1) the platforms on which the applications run are heterogeneous, 2) their web interface is not machine-friendly, 3) they use a non-standard format for data input and output, 4) they do not exploit standards to define application interface and message exchange, and 5) existing protocols for remote messaging are often not firewall-friendly. To overcome these issues, web services have emerged as a standard XML-based model for message exchange between heterogeneous applications. Web services engines have been developed to manage the configuration and execution of a web services workflow.
To demonstrate the benefit of using web services over traditional web interfaces, we compare the two implementations of HAPI, a gene expression analysis utility developed by the University of California San Diego (UCSD) that allows visual characterization of groups or clusters of genes based on the biomedical literature. This utility takes a set of microarray spot IDs as input and outputs a hierarchy of MeSH Keywords that correlates to the input and is grouped by Medical Subject Heading (MeSH) category. While the HTML output is easy for humans to visualize, it is difficult for computer applications to interpret semantically. To facilitate the capability of machine processing, we have created a workflow of three web services that replicates the HAPI functionality. These web services use document-style messages, which means that messages are encoded in an XML-based format. We compared three approaches to the implementation of an XML-based workflow: a hard coded Java application, Collaxa BPEL Server and Taverna Workbench. The Java program functions as a web services engine and interoperates with these web services using a web services choreography language (BPEL4WS).
While it is relatively straightforward to implement and publish web services, the use of web services choreography engines is still in its infancy. However, industry-wide support and push for web services standards is quickly increasing the chance of success in using web services to unify heterogeneous bioinformatics applications. Due to the immaturity of currently available web services engines, it is still most practical to implement a simple, ad-hoc XML-based workflow by hard coding the workflow as a Java application. For advanced web service users the Collaxa BPEL engine facilitates a configuration and management environment that can fully handle XML-based workflow.
PMCID: PMC394315  PMID: 15113410
5.  Deploying and sharing U-Compare workflows as web services 
U-Compare is a text mining platform that allows the construction, evaluation and comparison of text mining workflows. U-Compare contains a large library of components that are tuned to the biomedical domain. Users can rapidly develop biomedical text mining workflows by mixing and matching U-Compare’s components. Workflows developed using U-Compare can be exported and sent to other users who, in turn, can import and re-use them. However, the resulting workflows are standalone applications, i.e., software tools that run and are accessible only via a local machine, and that can only be run with the U-Compare platform.
We address the above issues by extending U-Compare to convert standalone workflows into web services automatically, via a two-click process. The resulting web services can be registered on a central server and made publicly available. Alternatively, users can make web services available on their own servers, after installing the web application framework, which is part of the extension to U-Compare. We have performed a user-oriented evaluation of the proposed extension, by asking users who have tested the enhanced functionality of U-Compare to complete questionnaires that assess its functionality, reliability, usability, efficiency and maintainability. The results obtained reveal that the new functionality is well received by users.
The web services produced by U-Compare are built on top of open standards, i.e., REST and SOAP protocols, and therefore, they are decoupled from the underlying platform. Exported workflows can be integrated with any application that supports these open standards. We demonstrate how the newly extended U-Compare enhances the cross-platform interoperability of workflows, by seamlessly importing a number of text mining workflow web services exported from U-Compare into Taverna, i.e., a generic scientific workflow construction platform.
PMCID: PMC3658995  PMID: 23419017
UIMA; U-Compare; Web service; Annotation; Workflow; Text mining; Components
6.  Biowep: a workflow enactment portal for bioinformatics applications 
BMC Bioinformatics  2007;8(Suppl 1):S19.
The huge amount of biological information, its distribution over the Internet and the heterogeneity of available software tools makes the adoption of new data integration and analysis network tools a necessity in bioinformatics. ICT standards and tools, like Web Services and Workflow Management Systems (WMS), can support the creation and deployment of such systems. Many Web Services are already available and some WMS have been proposed. They assume that researchers know which bioinformatics resources can be reached through a programmatic interface and that they are skilled in programming and building workflows. Therefore, they are not viable to the majority of unskilled researchers. A portal enabling these to take profit from new technologies is still missing.
We designed biowep, a web based client application that allows for the selection and execution of a set of predefined workflows. The system is available on-line. Biowep architecture includes a Workflow Manager, a User Interface and a Workflow Executor. The task of the Workflow Manager is the creation and annotation of workflows. These can be created by using either the Taverna Workbench or BioWMS. Enactment of workflows is carried out by FreeFluo for Taverna workflows and by BioAgent/Hermes, a mobile agent-based middleware, for BioWMS ones. Main workflows' processing steps are annotated on the basis of their input and output, elaboration type and application domain by using a classification of bioinformatics data and tasks. The interface supports users authentication and profiling. Workflows can be selected on the basis of users' profiles and can be searched through their annotations. Results can be saved.
We developed a web system that support the selection and execution of predefined workflows, thus simplifying access for all researchers. The implementation of Web Services allowing specialized software to interact with an exhaustive set of biomedical databases and analysis software and the creation of effective workflows can significantly improve automation of in-silico analysis. Biowep is available for interested researchers as a reference portal. They are invited to submit their workflows to the workflow repository. Biowep is further being developed in the sphere of the Laboratory of Interdisciplinary Technologies in Bioinformatics – LITBIO.
PMCID: PMC1885848  PMID: 17430563
7.  BioMoby extensions to the Taverna workflow management and enactment software 
BMC Bioinformatics  2006;7:523.
As biology becomes an increasingly computational science, it is critical that we develop software tools that support not only bioinformaticians, but also bench biologists in their exploration of the vast and complex data-sets that continue to build from international genomic, proteomic, and systems-biology projects. The BioMoby interoperability system was created with the goal of facilitating the movement of data from one Web-based resource to another to fulfill the requirements of non-expert bioinformaticians. In parallel with the development of BioMoby, the European myGrid project was designing Taverna, a bioinformatics workflow design and enactment tool. Here we describe the marriage of these two projects in the form of a Taverna plug-in that provides access to many of BioMoby's features through the Taverna interface.
The exposed BioMoby functionality aids in the design of "sensible" BioMoby workflows, aids in pipelining BioMoby and non-BioMoby-based resources, and ensures that end-users need only a minimal understanding of both BioMoby, and the Taverna interface itself. Users are guided through the construction of syntactically and semantically correct workflows through plug-in calls to the Moby Central registry. Moby Central provides a menu of only those BioMoby services capable of operating on the data-type(s) that exist at any given position in the workflow. Moreover, the plug-in automatically and correctly connects a selected service into the workflow such that users are not required to understand the nature of the inputs or outputs for any service, leaving them to focus on the biological meaning of the workflow they are constructing, rather than the technical details of how the services will interoperate.
With the availability of the BioMoby plug-in to Taverna, we believe that BioMoby-based Web Services are now significantly more useful and accessible to bench scientists than are more traditional Web Services.
PMCID: PMC1693925  PMID: 17137515
8.  BIFI: a Taverna plugin for a simplified and user-friendly workflow platform 
BMC Research Notes  2014;7(1):740.
Heterogeneity in the features, input-output behaviour and user interface for available bioinformatics tools and services is still a bottleneck for both expert and non-expert users. Advancement in providing common interfaces over such tools and services are gaining interest among researchers. However, the lack of (meta-) information about input-output data and parameter prevents to provide automated and standardized solutions, which can assist users in setting the appropriate parameters. These limitations must be resolved especially in the workflow-based solution in order to ease the integration of software.
We report a Taverna Workbench plugin: the XworX BIFI (Beautiful Interfaces for Inputs) implemented as a solution for the aforementioned issues. BIFI provides a Graphical User Interface (GUI) definition language used to layout the user interface and to define parameter options for Taverna workflows. BIFI is also able to submit GUI Definition Files (GDF) directly or discover appropriate instances from a configured repository. In the absence of a GDF, BIFI generates a default interface.
The Taverna Workbench is an open source software providing the ability to combine various services within a workflow. Nevertheless, users can supply input data to the workflow via a simple user interface providing only a text area to enter the input in text form. The workflow may contain meta-information in human readable form such as description text for the port and an example value. However, not all workflow ports are documented so well or have all the required information.
BIFI uses custom user interface components for ports which give users feedback on the parameter data type or structure to be used for service execution and enables client-side data validations. Moreover, BIFI offers user interfaces that allow users to interactively construct workflow views and share them with the community, thus significantly increasing usability of heterogeneous, distributed service consumption.
Electronic supplementary material
The online version of this article (doi:10.1186/1756-0500-7-740) contains supplementary material, which is available to authorized users.
PMCID: PMC4228112  PMID: 25332013
9.  Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization 
PLoS ONE  2013;8(4):e55814.
Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access ( Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from, under the Creative Commons – Attribution – Share Alike (CC BY-SA) license.
PMCID: PMC3629104  PMID: 23613707
10.  Structuring research methods and data with the research object model: genomics workflows as a case study 
One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows.
We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”.
Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.
The Research Object is available at
The Wf4Ever Research Object Model is available at
Electronic supplementary material
The online version of this article (doi:10.1186/2041-1480-5-41) contains supplementary material, which is available to authorized users.
PMCID: PMC4177597  PMID: 25276335
Semantic web models; Scientific workflows; Digital libraries; Genome wide association study
11.  Disambiguating the species of biomedical named entities using natural language parsers 
Bioinformatics  2010;26(5):661-667.
Motivation: Text mining technologies have been shown to reduce the laborious work involved in organizing the vast amount of information hidden in the literature. One challenge in text mining is linking ambiguous word forms to unambiguous biological concepts. This article reports on a comprehensive study on resolving the ambiguity in mentions of biomedical named entities with respect to model organisms and presents an array of approaches, with focus on methods utilizing natural language parsers.
Results: We build a corpus for organism disambiguation where every occurrence of protein/gene entity is manually tagged with a species ID, and evaluate a number of methods on it. Promising results are obtained by training a machine learning model on syntactic parse trees, which is then used to decide whether an entity belongs to the model organism denoted by a neighbouring species-indicating word (e.g. yeast). The parser-based approaches are also compared with a supervised classification method and results indicate that the former are a more favorable choice when domain portability is of concern. The best overall performance is obtained by combining the strengths of syntactic features and supervised classification.
Availability: The corpus and demo are available at, and the software is freely available as U-Compare components (Kano et al., 2009): NaCTeM Species Word Detector and NaCTeM Species Disambiguator. U-Compare is available at
PMCID: PMC2828111  PMID: 20053840
12.  tmBioC: improving interoperability of text-mining tools with BioC 
The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to heterogeneity and variety in data formats. In response, BioC is a recent proposal that offers a minimalistic approach to tool interoperability by stipulating minimal changes to existing tools and applications. BioC is a family of XML formats that define how to present text documents and annotations, and also provides easy-to-use functions to read/write documents in the BioC format. In this study, we introduce our text-mining toolkit, which is designed to perform several challenging and significant tasks in the biomedical domain, and repackage the toolkit into BioC to enhance its interoperability.
Our toolkit consists of six state-of-the-art tools for named-entity recognition, normalization and annotation (PubTator) of genes (GenNorm), diseases (DNorm), mutations (tmVar), species (SR4GN) and chemicals (tmChem). Although developed within the same group, each tool is designed to process input articles and output annotations in a different format. We modify these tools and enable them to read/write data in the proposed BioC format. We find that, using the BioC family of formats and functions, only minimal changes were required to build the newer versions of the tools. The resulting BioC wrapped toolkit, which we have named tmBioC, consists of our tools in BioC, an annotated full-text corpus in BioC, and a format detection and conversion tool.
Furthermore, through participation in the 2013 BioCreative IV Interoperability Track, we empirically demonstrate that the tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. The tmBioC toolkit is publicly available at
Database URL:
PMCID: PMC4110697  PMID: 25062914
13.  Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II 
Manual curation of data from the biomedical literature is a rate-limiting factor for many expert curated databases. Despite the continuing advances in biomedical text mining and the pressing needs of biocurators for better tools, few existing text-mining tools have been successfully integrated into production literature curation systems such as those used by the expert curated databases. To close this gap and better understand all aspects of literature curation, we invited submissions of written descriptions of curation workflows from expert curated databases for the BioCreative 2012 Workshop Track II. We received seven qualified contributions, primarily from model organism databases. Based on these descriptions, we identified commonalities and differences across the workflows, the common ontologies and controlled vocabularies used and the current and desired uses of text mining for biocuration. Compared to a survey done in 2009, our 2012 results show that many more databases are now using text mining in parts of their curation workflows. In addition, the workshop participants identified text-mining aids for finding gene names and symbols (gene indexing), prioritization of documents for curation (document triage) and ontology concept assignment as those most desired by the biocurators.
Database URL:
PMCID: PMC3500522  PMID: 23160416
14.  Distilling structure in Taverna scientific workflows: a refactoring approach 
BMC Bioinformatics  2014;15(Suppl 1):S12.
Scientific workflows management systems are increasingly used to specify and manage bioinformatics experiments. Their programming model appeals to bioinformaticians, who can use them to easily specify complex data processing pipelines. Such a model is underpinned by a graph structure, where nodes represent bioinformatics tasks and links represent the dataflow. The complexity of such graph structures is increasing over time, with possible impacts on scientific workflows reuse. In this work, we propose effective methods for workflow design, with a focus on the Taverna model. We argue that one of the contributing factors for the difficulties in reuse is the presence of "anti-patterns", a term broadly used in program design, to indicate the use of idiomatic forms that lead to over-complicated design. The main contribution of this work is a method for automatically detecting such anti-patterns, and replacing them with different patterns which result in a reduction in the workflow's overall structural complexity. Rewriting workflows in this way will be beneficial both in terms of user experience (easier design and maintenance), and in terms of operational efficiency (easier to manage, and sometimes to exploit the latent parallelism amongst the tasks).
We have conducted a thorough study of the workflows structures available in Taverna, with the aim of finding out workflow fragments whose structure could be made simpler without altering the workflow semantics. We provide four contributions. Firstly, we identify a set of anti-patterns that contribute to the structural workflow complexity. Secondly, we design a series of refactoring transformations to replace each anti-pattern by a new semantically-equivalent pattern with less redundancy and simplified structure. Thirdly, we introduce a distilling algorithm that takes in a workflow and produces a distilled semantically-equivalent workflow. Lastly, we provide an implementation of our refactoring approach that we evaluate on both the public Taverna workflows and on a private collection of workflows from the BioVel project.
We have designed and implemented an approach to improving workflow structure by way of rewriting preserving workflow semantics. Future work includes considering our refactoring approach during the phase of workflow design and proposing guidelines for designing distilled workflows.
PMCID: PMC4016501  PMID: 24564760
15.  CaGrid Workflow Toolkit: A taverna based workflow tool for cancer grid 
BMC Bioinformatics  2010;11:542.
In biological and medical domain, the use of web services made the data and computation functionality accessible in a unified manner, which helped automate the data pipeline that was previously performed manually. Workflow technology is widely used in the orchestration of multiple services to facilitate in-silico research. Cancer Biomedical Informatics Grid (caBIG) is an information network enabling the sharing of cancer research related resources and caGrid is its underlying service-based computation infrastructure. CaBIG requires that services are composed and orchestrated in a given sequence to realize data pipelines, which are often called scientific workflows.
CaGrid selected Taverna as its workflow execution system of choice due to its integration with web service technology and support for a wide range of web services, plug-in architecture to cater for easy integration of third party extensions, etc. The caGrid Workflow Toolkit (or the toolkit for short), an extension to the Taverna workflow system, is designed and implemented to ease building and running caGrid workflows. It provides users with support for various phases in using workflows: service discovery, composition and orchestration, data access, and secure service invocation, which have been identified by the caGrid community as challenging in a multi-institutional and cross-discipline domain.
By extending the Taverna Workbench, caGrid Workflow Toolkit provided a comprehensive solution to compose and coordinate services in caGrid, which would otherwise remain isolated and disconnected from each other. Using it users can access more than 140 services and are offered with a rich set of features including discovery of data and analytical services, query and transfer of data, security protections for service invocations, state management in service interactions, and sharing of workflows, experiences and best practices. The proposed solution is general enough to be applicable and reusable within other service-computing infrastructures that leverage similar technology stack.
PMCID: PMC3098092  PMID: 21044328
16.  New developments on the cheminformatics open workflow environment CDK-Taverna 
The computational processing and analysis of small molecules is at heart of cheminformatics and structural bioinformatics and their application in e.g. metabolomics or drug discovery. Pipelining or workflow tools allow for the Lego™-like, graphical assembly of I/O modules and algorithms into a complex workflow which can be easily deployed, modified and tested without the hassle of implementing it into a monolithic application. The CDK-Taverna project aims at building a free open-source cheminformatics pipelining solution through combination of different open-source projects such as Taverna, the Chemistry Development Kit (CDK) or the Waikato Environment for Knowledge Analysis (WEKA). A first integrated version 1.0 of CDK-Taverna was recently released to the public.
The CDK-Taverna project was migrated to the most up-to-date versions of its foundational software libraries with a complete re-engineering of its worker's architecture (version 2.0). 64-bit computing and multi-core usage by paralleled threads are now supported to allow for fast in-memory processing and analysis of large sets of molecules. Earlier deficiencies like workarounds for iterative data reading are removed. The combinatorial chemistry related reaction enumeration features are considerably enhanced. Additional functionality for calculating a natural product likeness score for small molecules is implemented to identify possible drug candidates. Finally the data analysis capabilities are extended with new workers that provide access to the open-source WEKA library for clustering and machine learning as well as training and test set partitioning. The new features are outlined with usage scenarios.
CDK-Taverna 2.0 as an open-source cheminformatics workflow solution matured to become a freely available and increasingly powerful tool for the biosciences. The combination of the new CDK-Taverna worker family with the already available workflows developed by a lively Taverna community and published on enables molecular scientists to quickly calculate, process and analyse molecular data as typically found in e.g. today's systems biology scenarios.
PMCID: PMC3292505  PMID: 22166170
17.  Extracting semantically enriched events from biomedical literature 
BMC Bioinformatics  2012;13:108.
Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them.
Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task.
We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.
PMCID: PMC3464657  PMID: 22621266
18.  Finding biomarkers in non-model species: literature mining of transcription factors involved in bovine embryo development 
BioData Mining  2012;5:12.
Since processes in well-known model organisms have specific features different from those in Bos taurus, the organism under study, a good way to describe gene regulation in ruminant embryos would be a species-specific consideration of closely related species to cattle, sheep and pig. However, as highlighted by a recent report, gene dictionaries in pig are smaller than in cattle, bringing a risk to reduce the gene resources to be mined (and so for sheep dictionaries). Bioinformatics approaches that allow an integration of available information on gene function in model organisms, taking into account their specificity, are thus needed. Besides these closely related and biologically relevant species, there is indeed much more knowledge of (i) trophoblast proliferation and differentiation or (ii) embryogenesis in human and mouse species, which provides opportunities for reconstructing proliferation and/or differentiation processes in other mammalian embryos, including ruminants. The necessary knowledge can be obtained partly from (i) stem cell or cancer research to supply useful information on molecular agents or molecular interactions at work in cell proliferation and (ii) mouse embryogenesis to supply useful information on embryo differentiation. However, the total number of publications for all these topics and species is great and their manual processing would be tedious and time consuming. This is why we used text mining for automated text analysis and automated knowledge extraction. To evaluate the quality of this “mining”, we took advantage of studies that reported gene expression profiles during the elongation of bovine embryos and defined a list of transcription factors (or TF, n = 64) that we used as biological “gold standard”. When successful, the “mining” approach would identify them all, as well as novel ones.
To gain knowledge on molecular-genetic regulations in a non model organism, we offer an approach based on literature-mining and score arrangement of data from model organisms. This approach was applied to identify novel transcription factors during bovine blastocyst elongation, a process that is not observed in rodents and primates. As a result, searching through human and mouse corpuses, we identified numerous bovine homologs, among which 11 to 14% of transcription factors including the gold standard TF as well as novel TF potentially important to gene regulation in ruminant embryo development. The scripts of the workflow are written in Perl and available on demand. They require data input coming from all various databases for any kind of biological issue once the data has been prepared according to keywords for the studied topic and species; we can provide data sample to illustrate the use and functionality of the workflow.
To do so, we created a workflow that allowed the pipeline processing of literature data and biological data, extracted from Web of Science (WoS) or PubMed but also from Gene Expression Omnibus (GEO), Gene Ontology (GO), Uniprot, HomoloGene, TcoF-DB and TFe (TF encyclopedia). First, the human and mouse homologs of the bovine proteins were selected, filtered by text corpora and arranged by score functions. The score functions were based on the gene name frequencies in corpora. Then, transcription factors were identified using TcoF-DB and double-checked using TFe to characterise TF groups and families. Thus, among a search space of 18,670 bovine homologs, 489 were identified as transcription factors. Among them, 243 were absent from the high-throughput data available at the time of the study. They thus stand so far for putative TF acting during bovine embryo elongation, but might be retrieved from a recent RNA sequencing dataset (Mamo et al. , 2012). Beyond the 246 TF that appeared expressed in bovine elongating tissues, we restricted our interpretation to those occurring within a list of 50 top-ranked genes. Among the transcription factors identified therein, half belonged to the gold standard (ASCL2, c-FOS, ETS2, GATA3, HAND1) and half did not (ESR1, HES1, ID2, NANOG, PHB2, TP53, STAT3).
A workflow providing search for transcription factors acting in bovine elongation was developed. The model assumed that proteins sharing the same protein domains in closely related species had the same protein functionalities, even if they were differently regulated among species or involved in somewhat different pathways. Under this assumption, we merged the information on different mammalian species from different databases (literature and biology) and proposed 489 TF as potential participants of embryo proliferation and differentiation, with (i) a recall of 95% with regard to a biological gold standard defined in 2011 and (ii) an extension of more than 3 times the gold standard of TF detected so far in elongating tissues. The working capacity of the workflow was supported by the manual expertise of the biologists on the results. The workflow can serve as a new kind of bioinformatics tool to work on fused data sources and can thus be useful in studies of a wide range of biological processes.
PMCID: PMC3563503  PMID: 22931563
19.  Web services-based text-mining demonstrates broad impacts for interoperability and process simplification 
The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation.
Database URL:
PMCID: PMC4207221  PMID: 24919658
20.  CDK-Taverna: an open workflow environment for cheminformatics 
BMC Bioinformatics  2010;11:159.
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.
PMCID: PMC2862046  PMID: 20346188
21.  Text-mining-assisted biocuration workflows in Argo 
Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced.
Database URL:
PMCID: PMC4103424  PMID: 25037308
22.  Mining semantic networks of bioinformatics e-resources from the literature 
Journal of Biomedical Semantics  2011;2(Suppl 1):S4.
There have been a number of recent efforts (e.g. BioCatalogue, BioMoby) to systematically catalogue bioinformatics tools, services and datasets. These efforts rely on manual curation, making it difficult to cope with the huge influx of various electronic resources that have been provided by the bioinformatics community. We present a text mining approach that utilises the literature to automatically extract descriptions and semantically profile bioinformatics resources to make them available for resource discovery and exploration through semantic networks that contain related resources.
The method identifies the mentions of resources in the literature and assigns a set of co-occurring terminological entities (descriptors) to represent them. We have processed 2,691 full-text bioinformatics articles and extracted profiles of 12,452 resources containing associated descriptors with binary and tf*idf weights. Since such representations are typically sparse (on average 13.77 features per resource), we used lexical kernel metrics to identify semantically related resources via descriptor smoothing. Resources are then clustered or linked into semantic networks, providing the users (bioinformaticians, curators and service/tool crawlers) with a possibility to explore algorithms, tools, services and datasets based on their relatedness. Manual exploration of links between a set of 18 well-known bioinformatics resources suggests that the method was able to identify and group semantically related entities.
The results have shown that the method can reconstruct interesting functional links between resources (e.g. linking data types and algorithms), in particular when tf*idf-like weights are used for profiling. This demonstrates the potential of combining literature mining and simple lexical kernel methods to model relatedness between resource descriptors in particular when there are few features, thus potentially improving the resource description, discovery and exploration process. The resource profiles are available at
PMCID: PMC3105496  PMID: 21388573
23.  NGS-Trex: Next Generation Sequencing Transcriptome profile explorer 
BMC Bioinformatics  2013;14(Suppl 7):S10.
Next-Generation Sequencing (NGS) technology has exceptionally increased the ability to sequence DNA in a massively parallel and cost-effective manner. Nevertheless, NGS data analysis requires bioinformatics skills and computational resources well beyond the possibilities of many "wet biology" laboratories. Moreover, most of projects only require few sequencing cycles and standard tools or workflows to carry out suitable analyses for the identification and annotation of genes, transcripts and splice variants found in the biological samples under investigation. These projects can take benefits from the availability of easy to use systems to automatically analyse sequences and to mine data without the preventive need of strong bioinformatics background and hardware infrastructure.
To address this issue we developed an automatic system targeted to the analysis of NGS data obtained from large-scale transcriptome studies. This system, we named NGS-Trex (NGS Transcriptome profile explorer) is available through a simple web interface and allows the user to upload raw sequences and easily obtain an accurate characterization of the transcriptome profile after the setting of few parameters required to tune the analysis procedure. The system is also able to assess differential expression at both gene and transcript level (i.e. splicing isoforms) by comparing the expression profile of different samples.
By using simple query forms the user can obtain list of genes, transcripts, splice sites ranked and filtered according to several criteria. Data can be viewed as tables, text files or through a simple genome browser which helps the visual inspection of the data.
NGS-Trex is a simple tool for RNA-Seq data analysis mainly targeted to "wet biology" researchers with limited bioinformatics skills. It offers simple data mining tools to explore transcriptome profiles of samples investigated taking advantage of NGS technologies.
PMCID: PMC3633008  PMID: 23815181
24.  Overview of the BioCreative III Workshop 
BMC Bioinformatics  2011;12(Suppl 8):S1.
The overall goal of the BioCreative Workshops is to promote the development of text mining and text processing tools which are useful to the communities of researchers and database curators in the biological sciences. To this end BioCreative I was held in 2004, BioCreative II in 2007, and BioCreative II.5 in 2009. Each of these workshops involved humanly annotated test data for several basic tasks in text mining applied to the biomedical literature. Participants in the workshops were invited to compete in the tasks by constructing software systems to perform the tasks automatically and were given scores based on their performance. The results of these workshops have benefited the community in several ways. They have 1) provided evidence for the most effective methods currently available to solve specific problems; 2) revealed the current state of the art for performance on those problems; 3) and provided gold standard data and results on that data by which future advances can be gauged. This special issue contains overview papers for the three tasks of BioCreative III.
The BioCreative III Workshop was held in September of 2010 and continued the tradition of a challenge evaluation on several tasks judged basic to effective text mining in biology, including a gene normalization (GN) task and two protein-protein interaction (PPI) tasks. In total the Workshop involved the work of twenty-three teams. Thirteen teams participated in the GN task which required the assignment of EntrezGene IDs to all named genes in full text papers without any species information being provided to a system. Ten teams participated in the PPI article classification task (ACT) requiring a system to classify and rank a PubMed® record as belonging to an article either having or not having “PPI relevant” information. Eight teams participated in the PPI interaction method task (IMT) where systems were given full text documents and were required to extract the experimental methods used to establish PPIs and a text segment supporting each such method. Gold standard data was compiled for each of these tasks and participants competed in developing systems to perform the tasks automatically.
BioCreative III also introduced a new interactive task (IAT), run as a demonstration task. The goal was to develop an interactive system to facilitate a user’s annotation of the unique database identifiers for all the genes appearing in an article. This task included ranking genes by importance (based preferably on the amount of described experimental information regarding genes). There was also an optional task to assist the user in finding the most relevant articles about a given gene. For BioCreative III, a user advisory group (UAG) was assembled and played an important role 1) in producing some of the gold standard annotations for the GN task, 2) in critiquing IAT systems, and 3) in providing guidance for a future more rigorous evaluation of IAT systems. Six teams participated in the IAT demonstration task and received feedback on their systems from the UAG group. Besides innovations in the GN and PPI tasks making them more realistic and practical and the introduction of the IAT task, discussions were begun on community data standards to promote interoperability and on user requirements and evaluation metrics to address utility and usability of systems.
In this paper we give a brief history of the BioCreative Workshops and how they relate to other text mining competitions in biology. This is followed by a synopsis of the three tasks GN, PPI, and IAT in BioCreative III with figures for best participant performance on the GN and PPI tasks. These results are discussed and compared with results from previous BioCreative Workshops and we conclude that the best performing systems for GN, PPI-ACT and PPI-IMT in realistic settings are not sufficient for fully automatic use. This provides evidence for the importance of interactive systems and we present our vision of how best to construct an interactive system for a GN or PPI like task in the remainder of the paper.
PMCID: PMC3269932  PMID: 22151647
25.  The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration 
In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications.
This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds.
The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.
PMCID: PMC4036106
Molecular compounds; Self organizing map; Clustering; Visualization; Taverna

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