Enterprise AI Analysis
OmniReg-GPT: a high-efficiency foundation model for comprehensive genomic sequence understanding
The human genome contains a sophisticated array of elements that regulate gene activity and organismal functions. Developing a large window foundation model capable of efficiently processing long sequence inputs is essential yet challenging for decoding the multi-layered and complex landscape of the cis-regulatory elements. Here, we introduce OmniReg-GPT, a generative foundation model designed for the low-resource pretraining of long genomic sequences by optimized attention mechanism. During pretraining, OmniReg-GPT captures the complete distribution of regulatory elements across nucleotide to megabase scales with efficient training speed and memory usage. We demonstrate exceptional performance in downstream regulatory applications spanning the entire spectrum of genomic scales, including various cis-regulatory elements identification, context dependent gene expression prediction, single-cell chromatin accessibility analysis, and 3D chromatin contact modeling. As a generative model, OmniReg-GPT also holds the potential to generate candidate cell-type-specific enhancers through prompt engineering. Overall, OmniReg-GPT extends the boundaries of foundation models in the genomic field, and provides a valuable pretraining model resource which can be extensively applied for genomic researches.
Unlocking the Genome's Regulatory Code with OmniReg-GPT
OmniReg-GPT represents a breakthrough in genomic foundation models, offering unparalleled efficiency and accuracy in understanding and generating complex genomic sequences. Its innovative architecture and comprehensive pretraining empower researchers to decode multi-scale regulatory elements, predict gene expression with single-cell resolution, model 3D chromatin interactions, and even design novel functional enhancers, pushing the boundaries of genomic research.
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Max Sequence Length Processed
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Avg. scRNA-seq Prediction
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Peak 3D Prediction
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Gene Expression Accuracy
Deep Analysis & Enterprise Applications
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OmniReg-GPT introduces a novel hybrid attention mechanism, integrating local and global attention to efficiently process genomic sequences up to 200 kb. This architecture drastically reduces computational complexity from quadratic to linear, making it possible to pretrain on long sequences with significantly less GPU memory and higher training throughput compared to traditional Transformer models. This efficiency is critical for decoding complex, multi-layered cis-regulatory elements across vast genomic distances.
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Max Sequence Length (kb)
OmniReg-GPT demonstrates superior performance across a broad spectrum of genomic understanding tasks, outperforming several state-of-the-art DNA foundation models. Its comprehensive pretraining on large genomic windows allows it to capture multi-scale regulatory grammar, from cis-regulatory element identification to complex 3D chromatin interactions.
| Feature | OmniReg-GPT | Other Models |
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| Genomic Task Performance (MCC/AUROC) |
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OmniReg-GPT excels in predicting context-dependent gene expression and single-cell chromatin accessibility. Its ability to model regulatory grammar enables accurate prediction of gene activity in both cell-type-agnostic and cell-type-specific scenarios, achieving high AUROC scores. For scATAC-seq, it accurately predicts peak accessibility and deduces cell-type specific TF binding activities, capturing inherent cellular heterogeneity.
Enterprise Process Flow
Genomic Sequence Input (20kb)
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OmniReg-GPT Embeddings
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Classification Layer
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Single-Cell Peak Accessibility Prediction
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Cell-Type Specific TF Activity Inference
OmniReg-GPT successfully models 3D chromatin organization at megabase scales, predicting Hi-C contact frequency maps from sequence information alone. It demonstrates robust performance in chromosome-wide predictions, achieving high insulation score correlations and accurately identifying topological domains and chromatin loops, even at base-pair resolution. This capability is crucial for understanding long-range regulatory networks that control gene expression.
Predicting 3D Chromatin Architecture
Problem: Traditional models struggle with long-range dependencies and megabase-scale resolution in 3D chromatin prediction from sequence data.
Solution: OmniReg-GPT's efficient hybrid attention and large receptive field enable it to process 2-Mb genomic windows and learn base-pair resolution chromatin interactions, integrating local and global genomic signals.
Result: Achieved high median insulation score correlations (e.g., Pearson 0.85 for chr10) and accurately identified topological domains and chromatin loops, demonstrating strong predictive power for complex 3D genome architecture.
Beyond predictive tasks, OmniReg-GPT holds significant generative potential. It can design cell-type-specific enhancers through prompt engineering, demonstrating an average activity enhancement of up to 30.5% for generated enhancers in K562 cells. This zero-shot capability to generate novel, functional regulatory sequences opens new avenues for synthetic biology and therapeutic applications, moving beyond analysis to active design of genetic elements.
30.5
Enhancer Activity Enhancement (%)
Calculate Your Potential ROI with Enterprise AI in Genomics
Estimate the impact of integrating advanced AI models like OmniReg-GPT into your genomic research workflows. See how enhanced efficiency and predictive power can translate into significant cost and time savings.
Estimated Annual Savings
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Research Hours Reclaimed Annually
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Your Roadmap to Genomic AI Transformation
Implementing cutting-edge AI like OmniReg-GPT requires a strategic approach. Our phased roadmap ensures a smooth transition and maximal impact for your enterprise.
Phase 1: Genomic Data Integration
Integrate diverse genomic sequencing data (e.g., Hi-C, scATAC-seq, gene expression) into OmniReg-GPT for a unified, comprehensive view of regulatory elements.
Phase 2: Custom Model Adaptation
Fine-tune OmniReg-GPT with proprietary or specific disease-related genomic datasets to tailor its predictive capabilities to unique research or clinical applications.
Phase 3: Multi-Omics Analysis & Validation
Leverage OmniReg-GPT’s multi-scale understanding to conduct in-depth multi-omics analyses, validate predictions with experimental data, and identify novel regulatory insights.
Phase 4: Functional Sequence Design & Testing
Utilize OmniReg-GPT’s generative capabilities for in silico design of functional genomic elements (e.g., cell-type-specific enhancers) and validate their efficacy in experimental settings.
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