CAMELLIA: an alignment-free pipeline integrating k-mer feature selection and supervised classification for genomic...

Scalable analysis of biological sequences is essential for functional characterization, biomarker discovery, and therapeutic and vaccine development. Alignment-based pipelines often fail to capture intraspecific variation and become computationally limited on large datasets, while alignment-free methods treat each class as homogeneous, ignoring sample heterogeneity. We present CAMELLIA (Combining AlignMent-free fEature seLection cLassIficAtion), an alignment-free framework that combines feature selection with explainable machine learning for genomic sequence analysis and classification. CAMELLIA identifies subgroups within classes and ensures underrepresented subgroups are captured by the selected features, yielding a more accurate and scalable data-driven representation. Sequences are represented as graphs, from which structural patterns are extracted as features for supervised classifiers, enabling accurate class discrimination. In an HIV-1 case study, CAMELLIA accurately classified sequences into eight subtypes, reduced processing time versus MEGA (MUSCLE) and FAMSA, matched CASTOR-KRFE under identical splits, and outperformed COMET and REGA in accuracy. Beyond predictive performance, CAMELLIA recovered structural patterns beyond class labels, improving feature selection and interpretability. We further examined feature-count sensitivity, showing how selected feature numbers relate to underfitting or noise-driven overfitting. By balancing scalability, accuracy, and interpretability, CAMELLIA offers a new lens for genomic sequence classification and sets a new standard for data-driven, heterogeneity-aware analysis.