1School of Basic Medical Science, Qingdao University, 38 Dengzhou Road, Qingdao, China2State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang, China3Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC, Australia4Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA5Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA6Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China7Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia8Gordon Life Science Institute, Boston, MA, USA9Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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Summary:Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit.Availability and implementation:http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/.Supplementary information:Supplementary data are available at Bioinformatics online.