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. All data used for evaluating the computational approach
and for training the final models are
available for download.
Installation of R package procoil via Bioconductor
The R package
procoil is available from
Bioconductor.
The current version of the package is 1.6.0 and has been released as part of
Bioconductor 2.10
on April 2, 2012. To install
procoil, follow the simple standard procedure
for installing Bioconductor packages, i.e. enter the following into your R session:
source("http://www.bioconductor.org/biocLite.R")
biocLite("procoil")
Please note that Bioconductor 2.10 requires an R version ≥ 2.15.
Manual installation on older R versions
We recommend potential users to use the latest version of R and Bioconductor and to follow the installation
procedure described above. The R package
procoil 1.6.0, however, also works with older R versions.
If you have an R version ≥ 2.10.1, you can still use
procoil 1.6.0, but you have to download and install
the package manually:
- Download the suitable package file from the Bioconductor 2.10 procoil page.
- Follow the standard procedure for installing the package file on your system.
Getting started
To use the package, enter "
library(procoil)" in your R session.
To get a basic introduction, enter "
help(procoil)" or open the
user manual by entering "
vignette("procoil")".
Documentation
- User Manual:
PDF
(also contains documentation of Web interface)
- Reference Manual:
PDF
How to cite PrOCoil
If you use PrOCoil 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.
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
