Fast and Scalable Gene Embedding Search: A Comparative Study of FAISS and ScaNN
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913955430006784 |
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| author | Refahi, Mohammad Saleh Hearne, Gavin Muller, Harrison Lynch, Kieran Sokhansanj, Bahrad A. Brown, James R. Rosen, Gail |
| author_facet | Refahi, Mohammad Saleh Hearne, Gavin Muller, Harrison Lynch, Kieran Sokhansanj, Bahrad A. Brown, James R. Rosen, Gail |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16978 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and Scalable Gene Embedding Search: A Comparative Study of FAISS and ScaNN Refahi, Mohammad Saleh Hearne, Gavin Muller, Harrison Lynch, Kieran Sokhansanj, Bahrad A. Brown, James R. Rosen, Gail Genomics Artificial Intelligence Machine Learning 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. |
| title | Fast and Scalable Gene Embedding Search: A Comparative Study of FAISS and ScaNN |
| topic | Genomics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.16978 |