Using Taverna Workflows for Gene Analysis
Author Information
Author(s): Li Peter, Castrillo Juan I, Velarde Giles, Wassink Ingo, Soiland-Reyes Stian, Owen Stuart, Withers David, Oinn Tom, Pocock Matthew R, Goble Carole A, Oliver Stephen G, Kell Douglas B
Primary Institution: University of Manchester
Hypothesis
Can Taverna workflows effectively analyze quantitative data from microarray experiments?
Conclusion
Taverna workflows allow data analysis experts to combine R scripts with other tools, enabling scientists to analyze their data without needing to learn programming.
Supporting Evidence
- Taverna workflows can integrate various computational tools for data analysis.
- The study demonstrated the use of R for statistical analysis within Taverna.
- Workflows can be shared among scientists to facilitate data analysis without programming knowledge.
Takeaway
This study shows how scientists can use a tool called Taverna to analyze data from experiments without needing to know how to code. It helps them find important information in their data easily.
Methodology
The study used Taverna workflows to analyze microarray data by retrieving data from a database, performing statistical tests using R, and annotating results with Gene Ontology terms.
Limitations
The need for prior knowledge of R and Java programming may limit the ability of entry-level users to construct complex workflows.
Statistical Information
P-Value
<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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