BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments
Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing techniques and can benefit from specialized...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2018-08-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/5551.pdf |
| _version_ | 1797422141397794816 |
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| author | Maria Luiza Mondelli Thiago Magalhães Guilherme Loss Michael Wilde Ian Foster Marta Mattoso Daniel Katz Helio Barbosa Ana Tereza R. de Vasconcelos Kary Ocaña Luiz M.R. Gadelha Jr |
| author_facet | Maria Luiza Mondelli Thiago Magalhães Guilherme Loss Michael Wilde Ian Foster Marta Mattoso Daniel Katz Helio Barbosa Ana Tereza R. de Vasconcelos Kary Ocaña Luiz M.R. Gadelha Jr |
| author_sort | Maria Luiza Mondelli |
| collection | DOAJ |
| description | Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application. Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information. We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow. We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database. Some of these queries are available as a pre-built feature of the BioWorkbench web application. Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time. We also show how the application of machine learning techniques can enrich the analysis process. |
| first_indexed | 2024-03-09T07:27:55Z |
| format | Article |
| id | doaj.art-1913b3e06e6446db80d4e6a62bb80769 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:27:55Z |
| publishDate | 2018-08-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-1913b3e06e6446db80d4e6a62bb807692023-12-03T06:47:37ZengPeerJ Inc.PeerJ2167-83592018-08-016e555110.7717/peerj.5551BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experimentsMaria Luiza Mondelli0Thiago Magalhães1Guilherme Loss2Michael Wilde3Ian Foster4Marta Mattoso5Daniel Katz6Helio Barbosa7Ana Tereza R. de Vasconcelos8Kary Ocaña9Luiz M.R. Gadelha Jr10National Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, BrazilNational Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, BrazilNational Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, BrazilComputation Institute, Argonne National Laboratory/University of Chicago, Chicago, IL, USAComputation Institute, Argonne National Laboratory/University of Chicago, Chicago, IL, USAComputer and Systems Engineering Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, BrazilNational Center for Supercomputing Applications, University of Illinois, Urbana, IL, USANational Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, BrazilNational Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, BrazilNational Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, BrazilNational Laboratory for Scientific Computing, Petrópolis, Rio de Janeiro, BrazilAdvances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, they require high-performance computing techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems and databases. In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments. This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application. Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information. We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow. We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database. Some of these queries are available as a pre-built feature of the BioWorkbench web application. Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time. We also show how the application of machine learning techniques can enrich the analysis process.https://peerj.com/articles/5551.pdfBioinformaticsScientific workflowsProvenanceProfilingData analytics |
| spellingShingle | Maria Luiza Mondelli Thiago Magalhães Guilherme Loss Michael Wilde Ian Foster Marta Mattoso Daniel Katz Helio Barbosa Ana Tereza R. de Vasconcelos Kary Ocaña Luiz M.R. Gadelha Jr BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments PeerJ Bioinformatics Scientific workflows Provenance Profiling Data analytics |
| title | BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments |
| title_full | BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments |
| title_fullStr | BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments |
| title_full_unstemmed | BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments |
| title_short | BioWorkbench: a high-performance framework for managing and analyzing bioinformatics experiments |
| title_sort | bioworkbench a high performance framework for managing and analyzing bioinformatics experiments |
| topic | Bioinformatics Scientific workflows Provenance Profiling Data analytics |
| url | https://peerj.com/articles/5551.pdf |
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