KNIndex: a comprehensive database of physicochemical properties fork-tuple nucleotides

Abstract

With the development of high-throughput sequencing technology, the genomic sequences increased exponentially over the last decade. In order to decode these new genomic data, machine learning methods were introduced for genome annotation and analysis. Due to the requirement of most machines learning methods, the biological sequences must be represented as fixed-length digital vectors. In this representation procedure, the physicochemical properties of k-tuple nucleotides are important information. However, the values of the physicochemical properties of k-tuple nucleotides are scattered in different resources. To facilitate the studies on genomic sequences, we developed the first comprehensive database, namely KNIndex (https://knindex.pufengdu.org), for depositing and visualizing physicochemical properties of k-tuple nucleotides. Currently, the KNIndex database contains 182 properties including one for mononucleotide (DNA), 169 for dinucleotide (147 for DNA and 22 for RNA) and 12 for trinucleotide (DNA). KNIndex database also provides a user-friendly web-based interface for the users to browse, query, visualize and download the physicochemical properties of k-tuple nucleotides. With the built-in conversion and visualization functions, users are allowed to display DNA/RNA sequences as curves of multiple physicochemical properties. We wish that the KNIndex will facilitate the related studies in computational biology.

StackEPI: identification of cell line-specific enhancer–promoter interactions based on stacking ensemble learning

“…At present, the physicochemical properties of k-tuple nucleotides are also commonly used in computational prediction of bioinformatics, such as type III secreted effectors [ 35 ], type VI secreted effectors [ 36 ], enhancers [ 37 ], IL-6 inducing peptides [ 38 ], and replication origin sites [ 39 ]. Recently, Zhang et al [ 40 ] sorted out 182 physicochemical properties from the existing physicochemical properties database and published scientific literature, which are stored in the web-based KNIndex database, which can be obtained on https://knindex.pufengdu.org . In this study, we used the standardized physicochemical properties of k-tuple nucleotides provided by Zhang.…”

“…Positive replication origin sites [39]. Recently, Zhang et al [40] sorted out 182 physicochemical properties from the existing physicochemical properties database and published scientific literature, which are stored in the web-based KNIndex database, which can be obtained on https:// knind ex. pufen gdu.…”

XGEM: Predicting Essential miRNAs by the Ensembles of Various Sequence-Based Classifiers With XGBoost Algorithm

“…BioSeq-Analysis 2.0 ( Liu et al, 2019 ) and repRNA ( Liu et al, 2016b ) were used to generate these features. Although the algorithms for generating these features have been elaborated in various works of the literature ( Chen et al, 2015 , 2018 ; Liu et al, 2016a , 2019 ; Zhang et al, 2021 ), we briefly described them here for the convenience of readers.…”

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

“…For example, on the BS01 binding site of the protein (3I11A), the binding residues bound with Co 2+ ligands were located at 86, 88, 90, and 149 positions in the same sequence, respectively. These binding residues may have long-range correlation ( Chen et al, 2018 ; Zhang et al, 2020 ). Then, for every protein chain, we scanned from the first binding residue and counted the distance between the two binding residues sequentially.…”