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Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator

BACKGROUND: Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the proli...

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Detalles Bibliográficos
Autores principales: Garcia Castro, Alexander, Thoraval, Samuel, Garcia, Leyla J, Ragan, Mark A
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1090554/
https://www.ncbi.nlm.nih.gov/pubmed/15813976
http://dx.doi.org/10.1186/1471-2105-6-87
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author Garcia Castro, Alexander
Thoraval, Samuel
Garcia, Leyla J
Ragan, Mark A
author_facet Garcia Castro, Alexander
Thoraval, Samuel
Garcia, Leyla J
Ragan, Mark A
author_sort Garcia Castro, Alexander
collection PubMed
description BACKGROUND: Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the proliferation of GUIs (Graphic User Interfaces) in bioinformatics, only some of them provide workflow capabilities; surprisingly, no meta-analysis of workflow operators and components in bioinformatics has been reported. RESULTS: We present a set of syntactic components and algebraic operators capable of representing analytical workflows in bioinformatics. Iteration, recursion, the use of conditional statements, and management of suspend/resume tasks have traditionally been implemented on an ad hoc basis and hard-coded; by having these operators properly defined it is possible to use and parameterize them as generic re-usable components. To illustrate how these operations can be orchestrated, we present GPIPE, a prototype graphic pipeline generator for PISE that allows the definition of a pipeline, parameterization of its component methods, and storage of metadata in XML formats. This implementation goes beyond the macro capacities currently in PISE. As the entire analysis protocol is defined in XML, a complete bioinformatic experiment (linked sets of methods, parameters and results) can be reproduced or shared among users. Availability: (interactive), (download). CONCLUSION: From our meta-analysis we have identified syntactic structures and algebraic operators common to many workflows in bioinformatics. The workflow components and algebraic operators can be assimilated into re-usable software components. GPIPE, a prototype implementation of this framework, provides a GUI builder to facilitate the generation of workflows and integration of heterogeneous analytical tools.
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spelling pubmed-10905542005-05-07 Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator Garcia Castro, Alexander Thoraval, Samuel Garcia, Leyla J Ragan, Mark A BMC Bioinformatics Methodology Article BACKGROUND: Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the proliferation of GUIs (Graphic User Interfaces) in bioinformatics, only some of them provide workflow capabilities; surprisingly, no meta-analysis of workflow operators and components in bioinformatics has been reported. RESULTS: We present a set of syntactic components and algebraic operators capable of representing analytical workflows in bioinformatics. Iteration, recursion, the use of conditional statements, and management of suspend/resume tasks have traditionally been implemented on an ad hoc basis and hard-coded; by having these operators properly defined it is possible to use and parameterize them as generic re-usable components. To illustrate how these operations can be orchestrated, we present GPIPE, a prototype graphic pipeline generator for PISE that allows the definition of a pipeline, parameterization of its component methods, and storage of metadata in XML formats. This implementation goes beyond the macro capacities currently in PISE. As the entire analysis protocol is defined in XML, a complete bioinformatic experiment (linked sets of methods, parameters and results) can be reproduced or shared among users. Availability: (interactive), (download). CONCLUSION: From our meta-analysis we have identified syntactic structures and algebraic operators common to many workflows in bioinformatics. The workflow components and algebraic operators can be assimilated into re-usable software components. GPIPE, a prototype implementation of this framework, provides a GUI builder to facilitate the generation of workflows and integration of heterogeneous analytical tools. BioMed Central 2005-04-07 /pmc/articles/PMC1090554/ /pubmed/15813976 http://dx.doi.org/10.1186/1471-2105-6-87 Text en Copyright © 2005 Castro et al; licensee BioMed Central Ltd.
spellingShingle Methodology Article
Garcia Castro, Alexander
Thoraval, Samuel
Garcia, Leyla J
Ragan, Mark A
Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator
title Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator
title_full Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator
title_fullStr Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator
title_full_unstemmed Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator
title_short Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator
title_sort workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1090554/
https://www.ncbi.nlm.nih.gov/pubmed/15813976
http://dx.doi.org/10.1186/1471-2105-6-87
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