APR.Feature: Adjacent position relationship feature.
In EncDNA: Encoding of Nucleotide Sequences into Numeric Feature Vectors

Description Usage Arguments Details Value Author(s) References See Also Examples

This feature was proposed by Li et al.(2012). In fact this is similar to the PN.FDTF encoding scheme (Huang et al., 2006). In this encoding, correlation between adjacent nucleotides are taken into account. For any nucleotide sequence with n nucleotides, every two consecutive positions between 1 and n, i.e., (1, 2), (2, 3)... (n-1, n) constitute an APR feature set. For each pair of positions, frequencies of 16 dinucleotides are first computed for both positive and negative dataset, and then the diffence matrix is obtained by substracting the 16*(n-1) dinucleotide frequency matrix of positive set from that of negative set. The difference matrix is then be used for encoding of nucleotide sequences. In this encoding procedure each sequence with n nucleotides can be encoded into a vector of (n-1) numeric observations.

1	APR.Feature(positive_class, negative_class, test_seq)

`positive_class`	Nucleotide sequence dataset of positive class, must be an object of class `DNAStringSet`.
`negative_class`	Nucleotide sequence dataset of negative class, must be an object of class `DNAStringSet`.
`test_seq`	Nucleotide sequences to be encoded into numeric feature vectors, must be an object of class `DNAStringSet`.

The class DNAStringSet can be obtained by using the function readDNAStringSet avialble in Biostrings package Bioconductor. Here, the sequences must be supplied in FASTA format. Both positive and negative datasets are required for this encoding scheme.

A numeric matrix of order m*(n-1), where m is the number of sequences in test_seq and n is the length of sequence.

Prabina Kumar Meher, Indian Agricultural Statistics Research Institute, New Delhi-110012, INDIA

Li, J.L., Wang, L.F., Wang, H.Y., Bai, L.Y. and Yuan, Z.M. (2012). High-accuracy splice sites prediction based on sequence component and position features. Genetics and Molecular Research, 11(3): 3432-3451.

PN.Fdtf.Feature, WAM.Feature

data(droso)
positive <- droso$positive
negative <- droso$negative
test <- droso$test
pos <- positive[1:200]
neg <- negative[1:200]
tst <- test
enc <- APR.Feature(positive_class=pos, negative_class=neg, test_seq=tst)
enc