Dengue virus (DENV) is the causative agent of Dengue Hemorrhagic Fever and Dengue Shock Syndrome, and continues to represent a major public health hazard. DENVs are antigenically classified in four serotypes and each serotype is further divided into respective genotypes. The association between DENV subtypes and the kind & severity of disease caused by them is known. Experimental and computational approaches for subtyping are routinely used for the purpose of diagnosis and treatment of DENV, in addition to the study of phylodynamics. All virus-specific molecular subtyping tools make use of sequence alignments at backend. But as the volume of molecular data increases, alignment-dependent methods become computationally intensive. Hence, the need for alternative efficient approaches for subtyping of viruses becomes apparent. Recently, the concept of Return time distribution (RTD) was proposed and validated for alignment-free clustering and molecular phylogeny. The RTD-based approach is extended here for the subtyping of DENVs. 
Subtyping methodology involves compilation of curated genomic data of known subtypes, computing RTD of these sequences at different levels of k-mers, derivation a distance matrix and clustering. The subtype of the unknown is predicted based on its clustering with known subtypes.
Dataset consisting of 1359 DENV genomes with sequence identity (>92%) were clustered using the RTD based approach at k=5. Serotype specific clades, despite geographical and temporal variation in the dataset, were observed with 100% accuracy. The method was also found to be efficient in terms of time and implementation, apart from accuracy in the subtyping of DENV
