view README.txt @ 2:e99b1bbe44f8 draft

Line-wrap the README file for display on the Tool Shed

This Tool Shed Repository contains a workflow for comparing three RXLR
prediction methods with a Venn Diagram, and creates a FASTA file of any
proteins passing all three methods.


References
==========

Stephen C. Whisson, Petra C. Boevink, Lucy Moleleki, Anna O. Avrova,
Juan G. Morales, Eleanor M. Gilroy, Miles R. Armstrong, Severine Grouffaud,
Pieter van West, Sean Chapman, Ingo Hein, Ian K. Toth, Leighton Pritchard
and Paul R. J. Birch A translocation signal for delivery of oomycete
effector proteins into host plant cells. Nature 450:115-118, 2007.
http://dx.doi.org/10.1038/nature06203

Joe Win, William Morgan, Jorunn Bos, Ksenia V. Krasileva, Liliana M. Cano,
Angela Chaparro-Garcia, Randa Ammar, Brian J. Staskawicz and Sophien Kamoun.
Adaptive evolution has targeted the C-terminal domain of the RXLR effectors
of plant pathogenic oomycetes. The Plant Cell 19:2349-2369, 2007.
http://dx.doi.org/10.1105/tpc.107.051037

Souvik Bhattacharjee, N. Luisa Hiller, Konstantinos Liolios, Joe Win,
Thirumala-Devi Kanneganti, Carolyn Young, Sophien Kamoun and Kasturi Haldar.
The malarial host-targeting signal is conserved in the Irish potato famine
pathogen. PLoS Pathogens, 2(5):e50, 2006.
http://dx.doi.org/10.1371/journal.ppat.0020050


Availability
============

This workflow is available on the main Galaxy Tool Shed:
http://toolshed.g2.bx.psu.edu/view/peterjc/rxlr_venn_workflow

Development is being done on github here:
https://github.com/peterjc/picobio/tree/master/galaxy_workflows/rxlr_venn_workflow


Dependencies
============

These dependencies should be resolved automatically via the Galaxy Tool Shed:
 * http://toolshed.g2.bx.psu.edu/view/peterjc/tmhmm_and_signalp
 * http://toolshed.g2.bx.psu.edu/view/peterjc/seq_filter_by_id
 * http://toolshed.g2.bx.psu.edu/view/peterjc/venn_list

However, at the time of writing those Galaxy tools have their own dependencies
required for this workflow which require manual installation (SignalP v3.0,
HMMER v2.0, and the R/Bioconductor package limma).