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Abstract Science Gateways provide an easily accessible and powerful computing environment for researchers. These are built around a set of software tools that are frequently and heavily used by large number of researchers in specific domains. Science Gateways have been catering to a growing need of researchers for easy to use computational tools, however their usage model is typically single user-centric. As scientific research becomes ever more team oriented, the need driven by user-demand to support integrated collaborative capabilities in Science Gateways is natural progression. Ability to share data/results with others in an integrated manner is an important and frequently requested capability. In this article we will describe and discuss our work to provide a rich environment for data organization and data sharing by integrating the SeedMeLab (formerly SeedMe2) platform with two Science Gateways: CIPRES and GenApp. With this integration we also demonstrate SeedMeLab’s extensible features and how Science Gateways may incorporate and realize FAIR data principles in practice and transform into community data hubs.
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CORE: Comparable, Open, Reliable, Extensible Software: An Experience Report of Four Flux Balance Analysis Tools (Poster)
Computational tools have become increasingly important to the advancement of biological research. Despite the existence of data-sharing principles such as FAIR, there has been little attention paid to ensuring that computational tools provide a platform to compare experimental results and data across different studies. Tools often are lightly documented, non-extensible, and provide different levels of accuracy of their representation. This lack of standardization in biological research reduces the potential power for new insight and discovery and makes it hard for biologists to experiment, compare, and trust results between different studies. In this poster we present our experience using four tools that perform flux balance analysis, on a set of different metabolic models. We frame our work around a proposed principle, akin to FAIR, aimed at bioinformatics tools. We call this CORE (Comparable, Open, Reliable and Extensible), and find the biggest challenges to be with comparability between tools. We also find that while the tools are all open source, without a deep understanding of the code base, they have insufficient openness. We needed to reach out to developers to resolve many of our questions, and we were still left with unexpected behaviors. We present lessons learned as a path to future improvement using the CORE principles.
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- Award ID(s):
- 1909688
- PAR ID:
- 10464794
- Date Published:
- Journal Name:
- ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
