Galaxy as a platform for identifying candidate pathogen effectors
Methods in Molecular Biology, ISSN: 1064-3745, Vol: 1127, Page: 3-15
2014
- 5Citations
- 12Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations5
- Citation Indexes5
- CrossRef5
- Captures12
- Readers12
- 12
Book Chapter Description
The Galaxy web platform provides an integrated system for its users to run multiple computational tools, linking their output in order to perform sophisticated analysis without requiring any programming or installation of software beyond a modern web-browser. Analyses can be saved as reusable workfl ows, and shared with other Galaxy users, allowing them to easily perform the same analysis or protocol on their own data. We describe example Galaxy workfl ows for the identifi cation of candidate pathogen effector proteins. Our main example focuses on nematode plant pathogens where signal peptide and transmembrane prediction tools are used to identify predicted secreted proteins.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84908483995&origin=inward; http://dx.doi.org/10.1007/978-1-62703-986-4_1; http://www.ncbi.nlm.nih.gov/pubmed/24643548; https://link.springer.com/10.1007/978-1-62703-986-4_1; https://dx.doi.org/10.1007/978-1-62703-986-4_1; https://link.springer.com/protocol/10.1007/978-1-62703-986-4_1
Springer Science and Business Media LLC
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