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# PrOCoil - Predicting the Oligomerization of Coiled Coil Proteins We have developed an SVM-based classification method for predicting whether a given coiled coil sequence is a trimer or dimer (assuming that it is one of both). This method also allows for a deep analysis of the sequence which residues are mainly responsible for the outcome. The software is available as an R package 'procoil' and as a simple-to-use [Web application]( **Important Note:** the prediction models have been updated with the release of version 2.0.0 of the 'procoil' R package. The updated data sets and some information on how they have been collected are available from the [PrOCoil Data Repository (v2)]( If you want to use the original prediction models as published by Mahrenholz et al. (2011), please follow the instructions in Section 5.5.3 of the [user manual]( The data sets on which the original PrOCoil models were based are still available from the [PrOCoil Data Repository (v1)]( ## Installation The package can be installed from [Bioconductor]( Therefore, the the simplest way to install the package is to enter ``` if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("procoil") ``` into your R session. If, for what reason ever, you prefer to install the package manually, follow the instructions in the [user manual]( ## User support If you encounter any issues or if you have any question that might be of interest also for other users, before writing a private message to the package developers/maintainers, please create an issue in this repository and also consider posting on [Bioconductor Support]( or on [StackOverflow]( For other matters regarding the package, please contact the package author(s). ## Citing this package If you use this package for research that is published later, you are kindly asked to cite it as follows: - C. C. Mahrenholz, I. G. Abfalter, U. Bodenhofer, R. Volkmer, and S. Hochreiter (2011). Complex networks govern coiled coil oligomerization - predicting and profiling by means of a machine learning approach. *Mol. Cell. Proteomics,* **10**(5):M110.004994, 2011. DOI: [10.1074/mcp.M110.004994](