SeqHIVE: a Python package to convert the biological sequences to informative vectors for sequence property predictions

Sequence-based analysis and prediction form a cornerstone of bioinformatics investigations of the sequence-structure-function paradigm across DNA, RNA, and proteins. The exponential growth in sequence data necessitates sequence encoding methods and advanced predictive models. This study introduces a comprehensive machine learning and deep learning platform SeqHIVE, which aims to streamline the development of prediction pipelines for nucleic acid and protein sequences. SeqHIVE is implemented as a graphical user interface (GUI)–driven platform, enabling intuitive construction, evaluation, and visualization of sequence-based predictive models. SeqHIVE integrates a broad spectrum of 19 biological sequence encoding algorithms, 5 feature selection algorithms, and 26 classifiers for the biological sequence prediction tasks. It automates the processes of sequence-based feature extraction, model construction, predictive performance assessment, statistical analysis, and data visualization. SeqHIVE is engineered to serve both expert bioinformatics researchers with extensive customizable options, and biologists through a user-friendly interface and an intuitive design process. The utility of SeqHIVE is exemplified through a case study on iLearnPlus DNA locus prediction task. The tool SeqHIVE, the documentation, and its source code are freely available at: https://healthinformaticslab.org/supp/ . and is also hosted on the GitHub repository: https://github.com/EienKune/SeqHIVE .