Fast and Scalable Gene Embedding Search: A Comparative Study of FAISS and ScaNN
This addresses the need for efficient large-scale similarity search in bioinformatics, offering a promising alternative to tools like BLAST, though it is incremental as it applies existing vector search methods to a specific domain.
The study tackled the problem of scaling similarity search for DNA sequencing data by evaluating FAISS and ScaNN on gene embeddings, resulting in computational advantages like improved memory and runtime efficiency and better retrieval quality compared to traditional methods.
The exponential growth of DNA sequencing data has outpaced traditional heuristic-based methods, which struggle to scale effectively. Efficient computational approaches are urgently needed to support large-scale similarity search, a foundational task in bioinformatics for detecting homology, functional similarity, and novelty among genomic and proteomic sequences. Although tools like BLAST have been widely used and remain effective in many scenarios, they suffer from limitations such as high computational cost and poor performance on divergent sequences. In this work, we explore embedding-based similarity search methods that learn latent representations capturing deeper structural and functional patterns beyond raw sequence alignment. We systematically evaluate two state-of-the-art vector search libraries, FAISS and ScaNN, on biologically meaningful gene embeddings. Unlike prior studies, our analysis focuses on bioinformatics-specific embeddings and benchmarks their utility for detecting novel sequences, including those from uncharacterized taxa or genes lacking known homologs. Our results highlight both computational advantages (in memory and runtime efficiency) and improved retrieval quality, offering a promising alternative to traditional alignment-heavy tools.