COUSCOus: Contact prediction using shrinked covariance.

In COUSCOus: A Residue-Residue Contact Detecting Method

Description

COUSCOus is a residue-residue contact detecting method.

Usage

COUSCOus(fasta.file, verbose = TRUE)

Arguments

fasta.file	Protein sequence alignment file name in FASTA format.
verbose	Print tracing information. The default value is TRUE.

Details

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.

Value

A data frame with 3 columns (i, j, pCorr) containing all possible pairs of residues along with their COUSCOus correlation value.

Author(s)

Reda Rawi, Matyas A. Sustik, Ben Calderhead
Maintainer: Reda Rawi <[e-mail]>

Examples

# Alignment file
file.fasta <- system.file( "examples/1oaiA0.fa", package = "COUSCOus" )

# Run COUSCOus
df.predictions <- COUSCOus( file.fasta )

COUSCOus documentation built on May 2, 2019, 9:27 a.m.