Numerical Characterization of Neuropsychiatric Disorder-Related Genes (RPH3AL, TLX3, PATZ1, LEPREL4, and PKN2) Using an 8-Dimensional Vector Approach
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Original Research | 2026 | Volume 2 | Issue 1 | Page 34-43
Rajneesh Prajapat*1, Ashwini Praveen Khairkar 2, Praveen Khairkar 3
1,2 Department of Biochemistry, Pacific Institute of Medical Sciences, Sai Tirupati University,
Udaipur, Rajasthan, India
3 Department of Psychiatry & Clinical Neuroscience, Pacific Institute of Medical Sciences, Sai
Tirupati University, Udaipur, Rajasthan, India
Corresponding Authors: Rajneesh Prajapat, Department of Biochemistry, Pacific Institute of Medical Sciences, Sai Tirupati University, Udaipur, Rajasthan, India, Email: rajneesh030041@gmail.com, Tel: +91-7976055027
ABSTRACT
Background: Neuropsychiatric disorders, including autism spectrum disorder (ASD), epilepsy, schizophrenia, and related neurodevelopmental conditions, are characterized by complex genetic architectures involving synaptic, neuronal, and signaling pathways. The identification and characterization of disease-associated genes remain critical for understanding the molecular mechanisms underlying these disorders. Alignment-free computational approaches have emerged as efficient alternatives to conventional sequence alignment methods for large-scale genomic analysis.
Objective: This study aims to investigate the sequence characteristics and relationships of five neuropsychiatric disorder-associated genes, namely RPH3AL, TLX3, PATZ1, LEPREL4, and PKN2, using an 8-dimensional vector representation framework based on graphical and statistical analysis of DNA sequences.
Methods: Reference DNA sequences were retrieved from the NCBI database following systematic literature mining and gene selection. Each sequence was transformed into a numerical series using a probabilistic nucleotide mapping scheme (A=0.2,; T=-0.2,; C=0.3,; G=-0.3). A two-dimensional zigzag graphical representation was generated by mapping nucleotide positions to numerical values. Slopes connecting each graphical point to the origin were calculated, and their statistical properties, including mean and variance, were extracted under four mapping schemes to construct an 8-dimensional feature vector. Sequence similarity was evaluated using Euclidean distance analysis, and phylogenetic relationships were inferred through the Unweighted Pair Group Method with Arithmetic Mean (UPGMA).
Results: The proposed graphical representation generated distinct zigzag patterns for each gene, reflecting differences in nucleotide composition and sequence organization. The derived 8-dimensional vectors provided compact numerical descriptors capable of distinguishing among the selected genes. Euclidean distance analysis enabled quantitative assessment of sequence similarity, while UPGMA clustering revealed meaningful relationships among genes involved in synaptic regulation, transcriptional control, extracellular matrix organization, and intracellular signaling pathways.
Conclusion: The 8-dimensional vector representation provides an effective alignment-free framework for numerical characterization and comparative analysis of neuropsychiatric disorder-associated genes. The combination of graphical representation, statistical feature extraction, Euclidean distance measurement, and phylogenetic clustering offers a computationally efficient approach for investigating genomic relationships and may contribute to future studies on disease-associated variants, biomarker discovery, and precision neurogenomics.
Keywords: Neuropsychiatric disorders; RPH3AL; TLX3; PATZ1; LEPREL4; PKN2; 8-dimensional