PredictHEC(myAAStringSet, type = "states", windowSize = 7, background = c(H = -0.12, E = -0.25, C = 0.23), HEC_MI1 = NULL, HEC_MI2 = NULL)
AAStringSet
object of sequences.
"states"
, "scores"
, or "probabilities"
.
type
is "states"
(the default), then the output is a character vector with the secondary structure assignment ("H", "E", or "C") for each residue in myAAStringSet
.Otherwise, the output is a list with one element for each sequence in myAAStringSet
. Each list element contains a matrix with dimensions 3 (H, E, or C) by the number of residues in the sequence. If type
is "scores"
, then values in the matrix represent log-odds ``scores''. If type
is "probabilities"
then the values represent the normalized probabilities of the three states at a position.
windowSize
residues of the position being assigned. This approach is about 65% accurate, and is one of the most accurate methods for assigning secondary structure that only use a single sequence. This implementation of GOR IV does not use decision constants or the number of contiguous states when assigning the final state. Note that characters other than the standard 20 amino acids are not assigned a state.
HEC_MI1
, HEC_MI2
fas <- system.file("extdata", "50S_ribosomal_protein_L2.fas", package="DECIPHER")
dna <- readDNAStringSet(fas)
aa <- translate(dna)
hec <- PredictHEC(aa)
head(hec)
Run the code above in your browser using DataLab