Galaxy-SynBioCAD: Synthetic Biology Design Automation tools in Galaxy workflows
Galaxy-SynBioCAD: Synthetic Biology Design Automation tools in Galaxy workflows
复制标题
Galaxy-SynBioCAD:Galaxy 工作流程中的合成生物学设计自动化工具
DOI:
10.1101/2020.06.14.145730
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发表时间:
2020
期刊:
影响因子:
--
通讯作者:
Du Lac M
中科院分区:
文献类型:
--
作者:
Du Lac M
Many computer-aided design tools are available for synthetic biology and metabolic engineering. Yet, these tools can be difficult to apprehend, sometimes requiring a level of expertise that limits their use by a wider community. Furthermore, some of the tools, although complementary, rely on different input and output formats and cannot communicate with one another. Scientific workflows address these shortcomings while offering a novel design strategy. Among the workflow systems available, Galaxy is a web-based platform for performing findable and accessible data analyses for all scientists regardless of their informatics expertise, along with interoperable and reproducible computations regardless of the particular platform that is being used.Here, we introduce the Galaxy-SynBioCAD portal, the first Galaxy toolshed for synthetic biology and metabolic engineering. It allows one to easily create workflows or use those already developed by the community. The portal is a growing community effort where developers can add new tools and users can evaluate the tools performing design for their specific projects. The tools and workflows currently shared on the Galaxy-SynBioCAD portal cover an end-to-end metabolic pathway design process from the selection of strain and target to the calculation of DNA parts to be assembled to build libraries of strains to be engineered to produce the target.Standard formats are used throughout to enforce the compatibility of the tools. These include SBML for strain and pathway and SBOL for genetic layouts. The portal has been benchmarked on 81 literature pathways, overall, we find we have a 65% (and 88%) success rate in retrieving the literature pathways among the top 10 (50) pathways predicted and generated by the workflows.
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影响因子:
4.7
作者:
Jonathan D. Tyzack;António J. M. Ribeiro;N. Borkakoti;J. Thornton
通讯作者:
J. Thornton
DOI:
10.1101/189357
发表时间:
2017
期刊:
bioRxiv
影响因子:
--
作者:
Neil Swainston;M. Dunstan;A. Jervis;C. Robinson;Pablo Carbonell;Alan R. Williams;J. Faulon;N. Scrutton;D. Kell
通讯作者:
D. Kell
影响因子:
5.8
作者:
Hucka, M;Finney, A;Wang, J
通讯作者:
Wang, J
影响因子:
14.9
作者:
Carbonell P;Parutto P;Herisson J;Pandit SB;Faulon JL
通讯作者:
Faulon JL
影响因子:
14.9
作者:
Duigou T;du Lac M;Carbonell P;Faulon JL
通讯作者:
Faulon JL