Profile-based protein representation derived by PSSM (Position-Specific Scoring Matrix) and auto cross covariance
The PSSM computed by
The lag parameter. Must be less than the number of amino acids in the sequence (i.e. the number of columns in the PSSM matrix).
This function calculates the feature vector based on the PSSM by running PSI-Blast and auto cross covariance tranformation.
lag * 20^2 named numeric vector,
the element names are derived by the amino acid name abbreviation
(crossed amino acid name abbreviation) and lag index.
Nan Xiao <https://nanx.me>
Wold, S., Jonsson, J., Sj\"orstr\"om, M., Sandberg, M., & R\"annar, S. (1993). DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Analytica chimica acta, 277(2), 239–253.
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x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[] dbpath = tempfile('tempdb', fileext = '.fasta') invisible(file.copy(from = system.file('protseq/Plasminogen.fasta', package = 'Rcpi'), to = dbpath)) pssmmat = extractProtPSSM(seq = x, database.path = dbpath) pssmacc = extractProtPSSMAcc(pssmmat, lag = 3) tail(pssmacc)
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