Chen Wei, Lei Tian-Yu, Jin Dian-Chuan, Lin Hao, Chou Kuo-Chen
School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China; Gordon Life Science Institute, Belmont, MA 02478, USA.
School of Sciences, and Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China.
Anal Biochem. 2014 Jul 1;456:53-60. doi: 10.1016/j.ab.2014.04.001. Epub 2014 Apr 13.
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.
伪寡核苷酸组成，即伪K元核苷酸组成（PseKNC），可用于通过离散模型或向量来表示DNA或RNA序列，并且仍可通过其组成寡核苷酸的物理化学性质保留相当多的序列顺序信息，特别是全局或长程序列顺序信息。因此，PseKNC方法在增强处理计算基因组学和基因组序列分析中许多问题的能力方面可能具有很高的潜力。然而，处理不同的DNA或RNA问题可能需要不同类型的PseKNC。在此，我们提供了一个灵活且用户友好的PseKNC网络服务器（网址为http://lin.uestc.edu.cn/pseknc/default.aspx），用户可以通过选择各种参数和物理化学性质，根据自己的需要轻松生成许多不同模式的PseKNC。此外，为方便绝大多数实验科学家，还提供了一份逐步指南，介绍如何使用当前的网络服务器生成他们所需的PseKNC，而无需遵循复杂的数学方程，本文仅为完整性展示了PseKNC的公式及其发展过程。预计PseKNC网络服务器将成为计算基因组学和基因组序列分析中非常有用的工具。
