Pseudo K-tuple nucleotide composition

Last updated

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]

Contents

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

Background

PseAAC

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]

Analogous problem in genomics

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]

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]

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

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 4K components. In a dinucleotide situation where K = 2, 42 = 16 components will be included. The extension by PseKNC results in (4K + λ) components. [1]

Applications

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]

Web servers

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]

References

  1. 1 2 3 4 Chen, Wei; Lei, Tian-Yu; Jin, Dian-Chuan; Lin, Hao; Chou, Kuo-Chen (2014). "PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition" . Analytical Biochemistry. 456: 53–60. doi:10.1016/j.ab.2014.04.001.
  2. 1 2 Chou, Kuo-Chen (2001). "Prediction of protein cellular attributes using pseudo-amino acid composition". Proteins: Structure, Function, and Genetics. 43 (3): 246–55. doi:10.1002/prot.1035. PMID   11288174. S2CID   28406797.
  3. ↑ Chou, Kuo-Chen (2011-03-21). "Some remarks on protein attribute prediction and pseudo amino acid composition". Journal of Theoretical Biology. 273 (1): 236–247. doi:10.1016/j.jtbi.2010.12.024. ISSN   0022-5193. PMC   7125570 .
  4. 1 2 Chen, Wei; Lei, Tian-Yu; Jin, Dian-Chuan; Lin, Hao; Chou, Kuo-Chen (2014). "PseKNC: A flexible web server for generating pseudo K-tuple nucleotide composition". Analytical Biochemistry. 456: 53–60. doi:10.1016/j.ab.2014.04.001. PMID   24732113.
  5. 1 2 Chen, Wei; Zhang, Xitong; Brooker, Jordan; Lin, Hao; Zhang, Liqing; Chou, Kuo-Chen (2015). "PseKNC-General: A cross-platform package for generating various modes of pseudo nucleotide compositions". Bioinformatics. 31 (1): 119–20. doi: 10.1093/bioinformatics/btu602 . PMID   25231908.
  6. ↑ Chen, Wei; Lin, Hao; Chou, Kuo-Chen (2015). "Pseudo nucleotide composition or PseKNC: An effective formulation for analyzing genomic sequences". Molecular BioSystems. 11 (10): 2620–34. doi:10.1039/c5mb00155b. PMID   26099739.
  7. ↑ Chen, Wei; Lin, Hao; Chou, Kuo-Chen (2015). "Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences" . Molecular BioSystems. 11 (10): 2620–2634. doi:10.1039/C5MB00155B. ISSN   1742-206X.
  8. ↑ Liu, Bin; Wang, Shanyi; Long, Ren; Chou, Kuo-Chen (2017-01-01). "iRSpot-EL: identify recombination spots with an ensemble learning approach". Bioinformatics. 33 (1): 35–41. doi:10.1093/bioinformatics/btw539. ISSN   1367-4803.
  9. ↑ Ye, Dong-Xin; Yu, Jun-Wen; Li, Rui; Hao, Yu-Duo; Wang, Tian-Yu; Yang, Hui; Ding, Hui (2024-06-12). "The Prediction of Recombination Hotspot Based on Automated Machine Learning" . Journal of Molecular Biology: 168653. doi:10.1016/j.jmb.2024.168653. ISSN   0022-2836.
  10. ↑ Liu, Bin; Liu, Fule; Wang, Xiaolong; Chen, Junjie; Fang, Longyun; Chou, Kuo-Chen (2015-07-01). "Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences". Nucleic Acids Research. 43 (W1): W65 –W71. doi:10.1093/nar/gkv458. ISSN   0305-1048. PMC   4489303 . PMID   25958395.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.