Wei Chen1, Xitong Zhang2, Jordan Brooker2, Hao Lin3, Liqing Zhang2, Kuo-Chen Chou1. 1. 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 2. 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. 3. 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.
Abstract
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.
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.