The transcriptional complexity of organisms far exceeds initial expectations, with most genomic sequences being transcribed into diverse types of RNA. This complexity, combined with the rapid accumulation of biological data, has driven the development of bioinformatics tools aimed at analyzing nucleic acid sequences. A central challenge lies in effectively representing these sequences as vectors while preserving as much of the original information as possible, given that most computational algorithms operate on vectorized data. This chapter categorizes nucleic acid sequence representation methods into three primary types: sequence information, physicochemical properties, and secondary structure. Sequence-based features, such as k-mer frequencies and open reading frames (ORFs), capture fundamental characteristics of the sequence. Physicochemical properties, including features derived from pseudo-protein concepts and dinucleotide autocorrelation, reveal molecular attributes and sequence-order effects. Secondary structure features, such as minimum free energy (MFE) and base pairing patterns, provide insights into RNA folding and stability. By systematically organizing these methods, this chapter offers a practical framework for encoding nucleic acid data, enabling improved bioinformatics analysis and advanced computational applications.