Bin Liu1, Fule Liu2, Longyun Fang2, Xiaolong Wang3, Kuo-Chen Chou3. 1. School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Gordon Life Science Institute, Belmont, MA 02478, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, 21589, Saudi Arabia School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Gordon Life Science Institute, Belmont, MA 02478, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, 21589, Saudi Arabia School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Gordon Life Science Institute, Belmont, MA 02478, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, 21589, Saudi Arabia. 2. School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Gordon Life Science Institute, Belmont, MA 02478, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, 21589, Saudi Arabia. 3. School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Gordon Life Science Institute, Belmont, MA 02478, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, 21589, Saudi Arabia School of Computer Science and Technology and Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China, Gordon Life Science Institute, Belmont, MA 02478, USA and Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Abstract
UNLABELLED: In order to develop powerful computational predictors for identifying the biological features or attributes of DNAs, one of the most challenging problems is to find a suitable approach to effectively represent the DNA sequences. To facilitate the studies of DNAs and nucleotides, we developed a Python package called representations of DNAs (repDNA) for generating the widely used features reflecting the physicochemical properties and sequence-order effects of DNAs and nucleotides. There are three feature groups composed of 15 features. The first group calculates three nucleic acid composition features describing the local sequence information by means of kmers; the second group calculates six autocorrelation features describing the level of correlation between two oligonucleotides along a DNA sequence in terms of their specific physicochemical properties; the third group calculates six pseudo nucleotide composition features, which can be used to represent a DNA sequence with a discrete model or vector yet still keep considerable sequence-order information via the physicochemical properties of its constituent oligonucleotides. In addition, these features can be easily calculated based on both the built-in and user-defined properties via using repDNA. AVAILABILITY AND IMPLEMENTATION: The repDNA Python package is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/repDNA/. CONTACT: bliu@insun.hit.edu.cn or kcchou@gordonlifescience.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: In order to develop powerful computational predictors for identifying the biological features or attributes of DNAs, one of the most challenging problems is to find a suitable approach to effectively represent the DNA sequences. To facilitate the studies of DNAs and nucleotides, we developed a Python package called representations of DNAs (repDNA) for generating the widely used features reflecting the physicochemical properties and sequence-order effects of DNAs and nucleotides. There are three feature groups composed of 15 features. The first group calculates three nucleic acid composition features describing the local sequence information by means of kmers; the second group calculates six autocorrelation features describing the level of correlation between two oligonucleotides along a DNA sequence in terms of their specific physicochemical properties; the third group calculates six pseudo nucleotide composition features, which can be used to represent a DNA sequence with a discrete model or vector yet still keep considerable sequence-order information via the physicochemical properties of its constituent oligonucleotides. In addition, these features can be easily calculated based on both the built-in and user-defined properties via using repDNA. AVAILABILITY AND IMPLEMENTATION: The repDNA Python package is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/repDNA/. CONTACT: bliu@insun.hit.edu.cn or kcchou@gordonlifescience.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.