iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences

Abstract

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:

iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

“…Accordingly, it remains a significant challenge in the future to identify further useful encoding schemes. There is much promise in this aspect from the availability of some recently developed powerful toolkits and Web servers for extracting a wide range of features, including Pse-Analysis [87], Bio-Seq Analysis [85], Pse-in-One [91], repDNA [81] and iFeature [134]. These toolkits could enable us to consider a much greater combination of different types of feature encoding schemes and explore the possibility of evolving iProt-Sub to a more robust framework while preserving or enhancing its model accuracy.…”

Bioinformatics analysis and collection of protein post-translational modification sites in human viruses

“…Enhanced grouped amino acid composition (EGAAC). EGAAC was first proposed by Chen et al (Chen, et al, 2018) and is the improved version of GAAC features. GAAC divides 20 standard amino acids into five groups based on their physical and chemical properties.…”

Prediction of protein-protein interactions based on elastic net and deep forest

“…At present, PAAC has 146 shown good properties in proteomics field (Cui, et al, 2019;Qiu, et al, 2018;Yu, et al, 147 2017a; Yu, et al, 2017b;Yu, et al, 2017c). Auto includes 148 Moran, and Geary (Chen, Zhang, Ma & Yu, 2019;Chen, et al, 2018). It represents the 149 physicochemical, position information, and the seven physicochemical properties in Auto can 150 be obtained in Supplementary Table S1.…”

“…In CTD (Chen, et al, 2018), amino acids are grouped into three groups based on 188 hydrophobicity: polar (P), neutral (N), and hydrophobic (H). Using ( The T descriptor first converts the original sequence into a replacement sequence, and T 197 includes three characteristics, the dipeptide composition frequency from the polar group to the 198 neutral group and the composition frequency from the neutral group to the polar group.…”