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The Pseudo K-tuple nucleotide composition or PseKNC, is a method for converting a nucleotide sequence (DNA or RNA) into a numerical vector so as to be used in pattern recognition techniques. Generally, the K-tuple can refer to a dinucleotide (when K=2) or a trinucleotide (when K=3). Depending on the instance, the technique can also be called PseDNC or PseTNC.^[1]

The method was derived from an analogous method in proteomics known as PseAAC (Pseudo Amino Acid Composition) that is applied to protein sequences.^[2]

PseKNC was derived from an analogous method in proteomics known as PseAAC (Pseudo Amino Acid Composition).^[2] Previously, investigations either relied on sequential models for making predictions of certain protein properties (which, in its simplest case, just refers to the amino acid composition of the protein), or a discrete model which represents a vector of twenty elements, each of which represent the frequency of each amino acid in the protein sample. The discrete model, however, fails to account for sequence-order information. The PseACC model extends the 20-length vector in the discrete model with λ components, each of which in some way captures sequence-order information, and this vector becomes the basis for making predictions.^[3]

Analogously, a discrete model of a nucleotide sequence based on its dinucleotide composition would lay involve a vector of 16 elements, the value of which one representing the frequency of each dinucleotide in the sequence:^[1]

${\displaystyle \mathbf {D} ={\begin{bmatrix}f(AA)f(AC)\cdots f(TT)\end{bmatrix}}^{\mathbf {T} }}$

Where D is the DNA sequence, T is the transpose operator, and f(AA) is the normalized occurrence frequency of AA in the DNA sequence. A trinucleotide representation can be denoted as:^[1]

${\displaystyle \mathbf {D} ={\begin{bmatrix}f(AAA)f(AAC)\cdots f(TTT)\end{bmatrix}}^{\mathbf {T} }}$

As can be seen, these discrete models fail to consider any global or long-range sequence-order information. To address this for both DNA and RNA sequences, the pseudo K-tuple nucleotide composition or PseKNC was proposed.^[4][5][6]

PseKNC extends the discrete model by adding λ components to represent sequence-order and physico-chemical properties of the nucleotide sequence. The original KNC model will involve 4^K components. In a dinucleotide situation where K = 2, 4² = 16 components will be included. The extension by PseKNC results in (4^K + λ) components.^[1]

A wide diversity of applications have been developed with respect to the PseKNC method.^[7] For example, it has become an integral component of many algorithms designed to predict the locations of recombination hotspots and coldspots from sequence information.^[8][9]

For the convenience scientific community, a freely available web server called PseKNC^[4] and an open source package called PseKNC-General^[5] were developed in 2013 and 2014, respectively, that could convert large-scale sequence datasets to pseudo nucleotide compositions with numerous choices of physicochemical property combinations. PseKNC-General can generate several modes of pseudo nucleotide compositions, including conventional k-tuple nucleotide compositions, Moreau–Broto autocorrelation coefficient, Moran autocorrelation coefficient, Geary autocorrelation coefficient, Type I PseKNC and Type II PseKNC.

Another web server, Pse-in-One, allows users to hand-select all pre-existing PseAAC and PseKNC methods for protein, RNA, and DNA sequences, along with any selection of the existing availability of physicochemical property combinations for these options.^[10]