RetrainScorer2: Retrain the SCORER 2.0 algorithm with user-defined training...

In SCORER2: SCORER 2.0: an algorithm for distinguishing parallel dimeric and trimeric coiled-coil sequences

Description

Function used to train the SCORER 2.0 algorithm with user-defined training data. It is recommended that the training data contains at least 30 amino-sequence/register assignment pairs for each oligomeric state in the training set.

Usage

RetrainScorer2(seq, reg, type)

Arguments

seq	A vector of amino acid sequences; where each element of the vector is a character string of amino-acid sequence. Valid characters are all uppercase letters except ‘B’, ‘J’, ‘O’, ‘U’, ‘X’, and ‘Z’; invalid characters will not be tolerated and their use will result in a failure of the program.
reg	A vector of heptad register assigned to each amino acid sequence; valid characters are the lowercase letters ‘a’ to ‘g’. Register characters are not required to be in proper order. The register can start with any of the seven letters. It must always have the same length as the matching amino-acid sequence in "seq".
type	A vector containing the known oligomeric state of the coiled-coil sequences in the training data. Acceptable oligomeric states are "DIMER" and "TRIMER" only.

Value

Returns a profile scoring matrix derived from the user-defined training data

Author(s)

Thomas L. Vincent [e-mail]

References

Craig T. Armstrong, Thomas L. Vincent, Peter J. Green and Dek N. Woolfson. (2011) SCORER 2.0: an algortihm for distinguishing parallel dimeric and trimeric coiled-coil sequences. Bioinformatics. DOI: 10.1093/bioinformatics/btr299

Examples

# load training data
data(training)
seq <- training[, 1]
reg <- training[, 2]
type <- training[, 3]

# retrain SCORER 2.0 to obtain new pssm
pssm <- RetrainScorer2(seq, reg, type)

SCORER2 documentation built on May 2, 2019, 4:06 a.m.