  Recent studies in DNA sequence classification have leveraged sophisticated
machine learning techniques, achieving notable accuracy in categorizing complex
genomic data. Among these, methods such as k-mer counting have proven effective
in distinguishing sequences from varied species like chimpanzees, dogs, and
humans, becoming a staple in contemporary genomic research. However, these
approaches often demand extensive computational resources, posing a challenge
in terms of scalability and efficiency. Addressing this issue, our study
introduces a novel adaptation of Jiang et al.'s compressor-based,
parameter-free classification method, specifically tailored for DNA sequence
analysis. This innovative approach utilizes a variety of compression
algorithms, such as Gzip, Brotli, and LZMA, to efficiently process and classify
genomic sequences. Not only does this method align with the current
state-of-the-art in terms of accuracy, but it also offers a more
resource-efficient alternative to traditional machine learning methods. Our
comprehensive evaluation demonstrates the proposed method's effectiveness in
accurately classifying DNA sequences from multiple species. We present a
detailed analysis of the performance of each algorithm used, highlighting the
strengths and limitations of our approach in various genomic contexts.
Furthermore, we discuss the broader implications of our findings for
bioinformatics, particularly in genomic data processing and analysis. The
results of our study pave the way for more efficient and scalable DNA sequence
classification methods, offering significant potential for advancements in
genomic research and applications.
