COUSCOus: A Residue-Residue Contact Detecting Method

Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) <DOI:10.1214/aos/1176345010> to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.

Install the latest version of this package by entering the following in R:
install.packages("COUSCOus")
AuthorReda Rawi [aut,cre], Matyas A. Sustik [aut], Ben Calderhead [aut]
Date of publication2016-02-28 16:26:52
MaintainerReda Rawi <rrawi@qf.org.qa>
LicenseGPL (>= 3)
Version1.0.0

View on CRAN

Files

inst
inst/examples
inst/examples/1oaiA0.fa
src
src/calculate_single_site_frequencies.c
src/aa2num.c
src/glassofast.f90
src/form_covarience_matrix.c
src/calculate_sequence_weights.c
src/guess_rho_matrix.c
src/calculate_pair_site_frequencies.c
NAMESPACE
R
R/helper_preprocess.R R/helper_precision.R R/helper_shrinkage.R R/COUSCOus.R R/helper_prediction.R
MD5
DESCRIPTION
man
man/COUSCOus-internal.Rd man/COUSCOus.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.