S2SNet: A Tool for Transforming Characters and Numeric Sequences into Star Network Topological Indices in Chemoinformatics, Bioinformatics, Biomedical, and Social-Legal Sciences

Abstract

The study of complex systems such as proteins/DNA/RNA or dynamics of tax law systems can be carried out with the complex network theory. This allows the numerical quantification of the significant information contained by the sequences of amino acids, nucleotides or types of tax laws. In this paper we describe S2SNet, a new Python tool with a graphical user interface that can transform any sequence of characters or numbers into series of invariant star network topological indices. The application is based on Python reusable processing procedures that perform different functions such as reading sequence data, transforming numerical series into character sequences, changing letter codification of strings and drawing the star networks of each sequence using Graphviz package as graphical back-end. S2SNet was previously used to obtain classification models for natural/random proteins, breast/colon/prostate cancer-related proteins, DNA sequences of mycobacterial promoters and for early detection of diseases and drug-induced toxicities using the blood serum proteome mass spectrum. In order to show the extended practical potential of S2SNet, this work presents several examples of application for proteins, DNA/RNA, blood proteome mass spectra and time evolution of the financial law recurrence. The obtained topological indices can be used to characterize systems by creating classification models, clustering or pattern search with statistical, Neural Network or Machine Learning methods. The free availability of S2SNet, the flexibility of analyzing diverse systems and the Python portability make it an ideal tool in fields such as

Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices

“…The input data source is composed of amino acid sequences (primary structure) from proteins with nucleotide (NB) and non‐nucleotide binding (non‐NB) properties in FASTA format. All sequences of amino acids are transformed into Star Graphs and the corresponding topological indices using S2SNet application19. These TIs are the input for data mining techniques from Weka software20.…”

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors

“…The other Markovian TIs class was derived from star graphs (SGs). SGs are also abstract 2D representations firstly defined for proteins by Randić [108] and later extended to represent DNA/RNA sequences and proteome spectra in the S2SNet Python-based tool as a source of several types of TIs [128]. SG is an artificial 2D representation of protein sequences having an imaginary center emitting "rays" like a star.…”

Experimental–Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov–Shannon Entropy Invariants

“…This transform converts the Raman spectra values to character sequences and the corresponding star graphs (SGs) are constructed using S2SNet tool. 52 A star graph is a special type of tree with N vertices, where one has N -1 degrees of freedom and the remaining N -1 vertices have a single degree of freedom. 53 In the case of protein sequences, the graph is built by adding all the amino acids into 23 possible branches ("rays" corresponding to the types of amino acids).…”

“…S2SNet has been used to calculate nonembedded and embedded Sh for each CNT Raman spectrum with the following parameters: no weights for the nodes, Markov normalization, k = 0-5. The formula of Sh invariants is described in eq 1, and the details about the SG Shannon entropy formulation are presented in ref 52.…”