DNA Storage Reliability

Calibrated sequence-level failure prediction and adaptive redundancy allocation for DNA data storage.

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Overview

DNA data storage encodes digital information into synthetic DNA oligonucleotides. Errors introduced during synthesis, PCR amplification, storage decay, and sequencing can cause Reed-Solomon (RS) error correction to fail — resulting in data loss.

Current practice allocates the same number of RS parity bytes to every oligo, regardless of how error-prone its sequence is. This project proposes a machine learning approach: predict which oligos are likely to fail, then adaptively reallocate parity bytes from safe sequences to risky ones, keeping the total storage budget neutral.

The pipeline covers the full experimental workflow:

• Synthetic DNA channel simulation (DeSP-inspired Monte Carlo model)
• Feature extraction from raw sequence composition
• Calibrated ML model training (XGBoost, Random Forest, Logistic Regression)
• Adaptive redundancy allocation vs. uniform and oracle baselines
• Feature ablation and distribution shift robustness analysis
• Paper-ready figure generation

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Experimental Design

The pipeline runs across 16 dataset configurations (4 × 2 × 2):

Axis	Values
Substitution rate	1%, 5%, 9%, 12%
Coverage depth (K)	K=5, K=3
Encoding scheme	Simple (2-bit), Constrained (R∞-P8)

Each configuration generates 2,000 oligos, simulates 30 Monte Carlo channel runs per oligo, and records failure frequency as the ML target.

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Project Structure

├── src/
│   ├── sequence_generator.py     # Synthetic oligo generation + channel simulation
│   ├── feature_extractor.py      # ~80 sequence features per oligo
│   └── dataset_assembler.py      # Train/val/test splits (70/15/15)
├── models/
│   ├── train.py                  # XGBoost / RF / LR training with grid search
│   ├── calibrate.py              # Platt scaling, isotonic regression, temperature scaling
│   └── evaluate.py               # ECE, Brier score, PR-AUC, AUROC
├── allocation/
│   ├── mechanism.py              # Budget-neutral parity reallocation logic
│   └── experiment.py             # Allocation experiments (XGBoost vs. Oracle vs. Uniform)
├── analysis/
│   ├── ablation.py               # Feature group ablation analysis
│   ├── distribution_shift.py     # Cross-regime transfer experiments
│   └── figures.py                # Paper figures (Figs 2–5, S1, S4)
├── configs/
│   └── experiment_config.yaml    # All hyperparameters and grid settings
└── run_pipeline.py               # End-to-end orchestrator with checkpointing

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Installation

pip install numpy pandas scipy scikit-learn xgboost shap matplotlib seaborn pyyaml pyarrow

Python 3.10+ recommended.

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Running the Pipeline

Run all stages end-to-end:

python run_pipeline.py

Resume from a specific stage after interruption:

python run_pipeline.py --from-stage train

Run a single stage:

python run_pipeline.py --only figures --force

Check pipeline status:

python run_pipeline.py --status

Pipeline Stages

Stage	Description
datasets	Generate sequences, simulate channel, extract features
train	Train and calibrate all models across 16 configs
gate_check	Validate model quality before allocation experiments
allocation	Run adaptive vs. uniform vs. oracle allocation (48 experiments)
ablation	Feature group ablation across all 16 configs
distribution_shift	Cross-substitution-regime transfer robustness tests
figures	Generate all paper figures

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Results

After the full pipeline completes:

results/
├── ablation/            # 16 CSVs — per-group metric deltas
├── distribution_shift/  # 4 CSVs  — ECE/Brier/PR-AUC under transfer
└── figures/
    ├── fig2_reliability_diagrams.png
    ├── fig3_shap_importance.png
    ├── fig4_frr_vs_delta.png
    ├── fig5_distribution_shift.png
    ├── fig_s1_feature_distributions.png
    └── fig_s4_cost_reliability.png

Key metric — Failure Reduction Rate (FRR): how much the adaptive allocation reduces failures vs. the uniform baseline, at the same total parity budget.

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Key Design Decisions

• Calibration matters: raw model scores are used directly as risk values in the allocation mechanism, so probability calibration (Platt scaling for XGBoost, isotonic for RF) is critical — not just discrimination
• Single-class guard: at low substitution rates and high coverage, virtually no sequences fail. A DummyClassifier is used in these cases rather than forcing a meaningless classifier
• Budget neutrality: the allocation mechanism strictly enforces that total parity bytes added equals total parity bytes removed — no free lunch
• Monte Carlo evaluation: FRR is estimated over 30 independent channel simulation runs per configuration to account for stochastic variation

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Configuration

All experiment parameters are in configs/experiment_config.yaml, including substitution rates, coverage depths, model hyperparameter grids, RS thresholds, and allocation delta values.