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DNAGPT: A Generalized Pre-trained Tool for Versatile DNA Sequence Analysis Tasks

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arxiv 2307.05628 v3 pith:YSNGWFB5 submitted 2023-07-11 q-bio.GN cs.LG

Daoan Zhang , Weitong Zhang , Yu Zhao , Jianguo Zhang , Bing He , Chenchen Qin , Jianhua Yao This is my paper · ORCID
classification q-bio.GNcs.LG
keywords tasksdnagptmodelsequenceanalysisdatadesignedgeneralized
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Pre-trained large language models demonstrate potential in extracting information from DNA sequences, yet adapting to a variety of tasks and data modalities remains a challenge. To address this, we propose DNAGPT, a generalized DNA pre-training model trained on over 200 billion base pairs from all mammals. By enhancing the classic GPT model with a binary classification task (DNA sequence order), a numerical regression task (guanine-cytosine content prediction), and a comprehensive token language, DNAGPT can handle versatile DNA analysis tasks while processing both sequence and numerical data. Our evaluation of genomic signal and region recognition, mRNA abundance regression, and artificial genomes generation tasks demonstrates DNAGPT's superior performance compared to existing models designed for specific downstream tasks, benefiting from pre-training using the newly designed model structure.

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Cited by 5 Pith papers

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    GeneFuse fuses genomic language model embeddings with MRI via multi-scale FiLM modulation and uncertainty-gated residuals, improving dementia screening AUROC by 0.12 over image-only on the ADNI cohort.

  2. GeneBreaker: Jailbreak Attacks against DNA Language Models with Pathogenicity Guidance

    cs.CR 2025-05 conditional novelty 6.0 of 10

    GeneBreaker, a new attack framework, steers DNA language models to generate sequences with over 90% identity to human pathogens, with success rates up to 60% on the largest Evo2 model.

  3. From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics

    cs.SE 2025-07 conditional novelty 5.0 of 10

    A qualitative study of ten bioinformatics workflows finds LLMs can generate usable Galaxy and Nextflow pipelines, with Gemini best for Galaxy and DeepSeek-V3 best for Nextflow.

  4. Evaluation of Coding Schemes for Transformer-based Gene Sequence Modeling

    cs.CL 2025-07 conditional novelty 5.0 of 10

    BPE tokenization and rotary position embeddings usually outperform k-mers and other positional encodings in from-scratch Transformer DNA classifiers, but the advantage is task-dependent.

  5. DNAZEN: Enhanced Gene Sequence Representations via Mixed Granularities of Coding Units

    cs.LG 2025-05 conditional novelty 5.0 of 10

    DNAZEN augments a Transformer genomic model with PMI-extracted G-gram representations and whole G-gram masking, improving MCC on 21 of 28 GUE datasets against DNABERT-2 after further pre-training.

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