GeneBank Genie: An interactive toolkit for integrated multivariate analysis and visualization of GenBank records

Importance of the work: GeneBank Genie fills a critical gap by providing integrated desktop software for accessible and comprehensive analysis of GenBank data. Objectives: To develop intuitive software facilitating simultaneous automated parsing, preliminary genomic analysis, visualization and sequence extraction from multiple GenBank records. Materials and Methods: GeneBank Genie was built in the Python programming environment, using Tkinter for the graphical user interface (GUI) design, Biopython and custom Python script for sequence processing and scikit-learn for principal component analysis (PCA) and clustering analytics. Results: The developed software called GeneBank Genie was used successfully to analyze a dataset of 333 Orthoptera mitochondrial genomes, automatically computing nucleotide compositions, the percentage of nitrogenous bases that are either guanine or cytosine (GC content) and gene annotations. The PCA revealed distinct genomic clustering patterns and Mahalanobis distances were used to identify outliers effectively. Taxonomic visualizations demonstrated robust exploratory capabilities based on interactive Sankey diagrams, dendrograms, correlation heatmaps and K-means clustering. Additionally, rapid extraction of gene sequences illustrated practical applications for molecular research workflows. Main finding: GeneBank Genie uniquely integrates automated batch processing of GenBank records, PCA-based analytics and intuitive visualizations, greatly simplifying genomic data exploration for biologists. GeneBank Genie is freely available at: https://github.com/yashmgupta/GeneBank-Genie