Genome-Factory: An Integrated Library for Tuning, Deploying, and Interpreting Genomic Models

• URL: http://arxiv.org/abs/2509.12266v1
• Date: Sat, 13 Sep 2025 03:31:55 GMT
• Title: Genome-Factory: An Integrated Library for Tuning, Deploying, and Interpreting Genomic Models
• Authors: Weimin Wu, Xuefeng Song, Yibo Wen, Qinjie Lin, Zhihan Zhou, Jerry Yao-Chieh Hu, Zhong Wang, Han Liu,
• Abstract summary: Genome-Factory is an integrated Python library for tuning, deploying, and interpreting genomic models.<n>For data collection, Genome-Factory offers an automated pipeline to download genomic sequences and preprocess them.<n>For inference, Genome-Factory enables both embedding extraction and DNA sequence generation.<n>For interpretability, Genome-Factory introduces the first open-source biological interpreter based on a sparse auto-encoder.
• Score: 15.523936567029624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Genome-Factory, an integrated Python library for tuning, deploying, and interpreting genomic 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. It also includes quality control, such as GC content normalization. For model tuning, Genome-Factory supports three approaches: full-parameter, low-rank adaptation, and adapter-based fine-tuning. It is compatible with a wide range of 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 for users to incorporate additional benchmarks. For interpretability, Genome-Factory introduces the first open-source biological interpreter based on a sparse auto-encoder. This module disentangles embeddings into sparse, near-monosemantic latent units and links them to interpretable genomic features by regressing on external readouts. To improve accessibility, Genome-Factory features both a zero-code command-line interface and a user-friendly web interface. 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 end-to-end usability and practical value for real-world genomic analysis.

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