PseKNC通用版：一个用于生成各种伪核苷酸组成模式的跨平台软件包。
PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions.
作者信息
Chen Wei, Zhang Xitong, Brooker Jordan, Lin Hao, Zhang Liqing, Chou Kuo-Chen
机构信息
Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063009, China, Department of Computer Science, Virginia Tech, Blacksburg, VA 24060, School of Life Science and Technology, Bioinformatics and Computer-Aided Drug Discovery, Gordon Life Science Institute, Boston, MA 02478, Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, Department of Computer Science, Vassar College, Poughkeepsie, NY 12604, USA, Excellence in Genomic Medicine Research, Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China and Excellence in Genomic Medicine Research, Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063009, China, Department of Computer Science, Virginia Tech, Blacksburg, VA 24060, School of Life Science and Technology, Bioinformatics and Computer-Aided Drug Discovery, Gordon Life Science Institute, Boston, MA 02478, Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, Department of Computer Science, Vassar College, Poughkeepsie, NY 12604, USA, Excellence in Genomic Medicine Research, Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China and Excellence in Genomic Medicine Research, Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063009, Chin
Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063009, China, Department of Computer Science, Virginia Tech, Blacksburg, VA 24060, School of Life Science and Technology, Bioinformatics and Computer-Aided Drug Discovery, Gordon Life Science Institute, Boston, MA 02478, Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904, Department of Computer Science, Vassar College, Poughkeepsie, NY 12604, USA, Excellence in Genomic Medicine Research, Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China and Excellence in Genomic Medicine Research, Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
出版信息
Bioinformatics. 2015 Jan 1;31(1):119-20. doi: 10.1093/bioinformatics/btu602. Epub 2014 Sep 16.
SUMMARY
The avalanche of genomic sequences generated in the post-genomic age requires efficient computational methods for rapidly and accurately identifying biological features from sequence information. Towards this goal, we developed a freely available and open-source package, called PseKNC-General (the general form of pseudo k-tuple nucleotide composition), that allows for fast and accurate computation of all the widely used nucleotide structural and physicochemical properties of both DNA and RNA sequences. 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. In every mode, >100 physicochemical properties are available for choosing. Moreover, it is flexible enough to allow the users to calculate PseKNC with user-defined properties. The package can be run on Linux, Mac and Windows systems and also provides a graphical user interface.
AVAILABILITY AND IMPLEMENTATION
The package is freely available at: http://lin.uestc.edu.cn/server/pseknc.
摘要
后基因组时代产生的大量基因组序列需要高效的计算方法，以便从序列信息中快速准确地识别生物学特征。为了实现这一目标，我们开发了一个免费的开源软件包，称为PseKNC-General（伪k元核苷酸组成的通用形式），它可以快速准确地计算DNA和RNA序列中所有广泛使用的核苷酸结构和物理化学性质。PseKNC-General可以生成多种伪核苷酸组成模式，包括传统的k元核苷酸组成、莫罗-布罗托自相关系数、莫兰自相关系数、吉尔里自相关系数、I型PseKNC和II型PseKNC。在每种模式下，有超过100种物理化学性质可供选择。此外，它足够灵活，允许用户使用自定义性质来计算PseKNC。该软件包可以在Linux、Mac和Windows系统上运行，并且还提供了图形用户界面。
可用性和实现方式
该软件包可在以下网址免费获取：http://lin.uestc.edu.cn/server/pseknc 。
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