Literature DB >> 24732113

PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition.

Wei Chen1, Tian-Yu Lei2, Dian-Chuan Jin2, Hao Lin3, Kuo-Chen Chou4.   

Abstract

The pseudo oligonucleotide composition, or pseudo K-tuple nucleotide composition (PseKNC), can be used to represent a DNA or RNA sequence with a discrete model or vector yet still keep considerable sequence order information, particularly the global or long-range sequence order information, via the physicochemical properties of its constituent oligonucleotides. Therefore, the PseKNC approach may hold very high potential for enhancing the power in dealing with many problems in computational genomics and genome sequence analysis. However, dealing with different DNA or RNA problems may need different kinds of PseKNC. Here, we present a flexible and user-friendly web server for PseKNC (at http://lin.uestc.edu.cn/pseknc/default.aspx) by which users can easily generate many different modes of PseKNC according to their need by selecting various parameters and physicochemical properties. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to generate their desired PseKNC without the need to follow the complicated mathematical equations, which are presented in this article just for the integrity of PseKNC formulation and its development. It is anticipated that the PseKNC web server will become a very useful tool in computational genomics and genome sequence analysis.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  DNA sequence representation; Global sequence order information; Physicochemical properties; PseAAC; Pseudo oligonucleotide composition; Web server

Mesh:

Substances:

Year:  2014        PMID: 24732113     DOI: 10.1016/j.ab.2014.04.001

Source DB:  PubMed          Journal:  Anal Biochem        ISSN: 0003-2697            Impact factor:   3.365


  95 in total

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Review 7.  Some illuminating remarks on molecular genetics and genomics as well as drug development.

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8.  i6mA-DNCP: Computational Identification of DNA N6-Methyladenine Sites in the Rice Genome Using Optimized Dinucleotide-Based Features.

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Journal:  Genes (Basel)       Date:  2019-10-20       Impact factor: 4.096

9.  Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome.

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10.  Comparison of genomic data via statistical distribution.

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