Abstract:We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core contribution is to simplify and unify the workflow for genomic model development: data collection, model tuning, inference, benchmarking, and interpretability. For data collection, Genome-Factory offers an automated pipeline to download genomic sequences and preprocess them. For model tuning, Genome-Factory supports both full and parameter-efficient fine-tuning across diverse genomic models. For inference, Genome-Factory enables both embedding extraction and DNA sequence generation. For benchmarking, we include two existing benchmarks and provide a flexible interface to incorporate additional benchmarks. For interpretability, Genome-Factory introduces an open-source biological interpreter based on a sparse auto-encoder. We validate the utility of Genome-Factory across three dimensions: (i) Compatibility with diverse models and fine-tuning methods; (ii) Benchmarking downstream performance using two open-source benchmarks; (iii) Biological interpretation of learned representations with DNABERT-2. These results highlight its practical value for real-world genomic analysis. GitHub: this https URL.
| Subjects: | Genomics (q-bio.GN); Machine Learning (cs.LG) |
| Cite as: | arXiv:2509.12266 [q-bio.GN] |
| (or arXiv:2509.12266v2 [q-bio.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2509.12266 arXiv-issued DOI via DataCite |
From: Weimin Wu [view email]
[v1]
Sat, 13 Sep 2025 03:31:55 UTC (596 KB)
[v2]
Thu, 14 May 2026 18:59:32 UTC (636 KB)
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