scripts/readme.md

Paper Experiment Scripts

This directory is a complete command plan for producing the data needed by the paper:

predictor-guided evaluation of autoregressive and masked-diffusion genomic foundation models for controllable DeepSTARR enhancer generation.

All scripts are written to use the shared environment file:

source /Users/ouzhang/Desktop/genRL/project/scripts/00_setup/env.sh

By default, outputs are written to:

/Users/ouzhang/Desktop/genRL/project/paper_runs/
  data/
  models/
  results/
  figures/

You can override locations before running:

export RUN_ROOT=/path/to/paper_runs
export DEEPSTARR_DIR=/path/to/DeepSTARR-enhancer-activity
export GENERATOR_BASE_MODEL=/path/to/GENERATOR-eukaryote-1.2b-base
export GENERANNO_BASE_MODEL=/path/to/GENERanno-eukaryote-0.5b-base

0. Install Dependencies

cd /Users/ouzhang/Desktop/genRL/project
bash scripts/00_setup/install_requirements.sh

This installs the requirements from GENERATOR/, GENERanno/, and extra analysis packages: datasets, huggingface_hub, pyarrow, scipy, scikit-learn, matplotlib, seaborn, and biopython.

1. Download Data and Models

Download DeepSTARR from HuggingFace and save train/valid/test.parquet:

cd /Users/ouzhang/Desktop/genRL/project
bash scripts/01_data/download_deepstarr.sh

Summarize the dataset splits for the paper's dataset table:

bash scripts/01_data/summarize_deepstarr.sh

Prepare conditioned DeepSTARR splits using train-set activity quantiles:

bash scripts/01_data/prepare_conditioned_deepstarr.sh

Create a GC-matched random DNA negative baseline:

bash scripts/01_data/make_random_gc_baseline.sh

This creates:

${CONDITIONED_DEEPSTARR_DIR}/train.parquet
${CONDITIONED_DEEPSTARR_DIR}/valid.parquet
${CONDITIONED_DEEPSTARR_DIR}/test.parquet
${CONDITIONED_DEEPSTARR_DIR}/conditioning_metadata.json

Optional: download fresh HuggingFace model snapshots:

bash scripts/01_data/download_models.sh

If you already have local models, set:

export GENERATOR_BASE_MODEL=/Users/ouzhang/Desktop/genRL/project/GENERATOR-eukaryote-1.2b-base
export GENERANNO_BASE_MODEL=/Users/ouzhang/Desktop/genRL/project/models/GENERanno-eukaryote-0.5b-base

2. Train Activity Predictor

Train the DeepSTARR activity predictor:

bash scripts/02_predictor/train_predictor.sh

Default output:

${RESULT_ROOT}/deepstarr_regression/
${RESULT_ROOT}/deepstarr_regression/best_model/
${RESULT_ROOT}/deepstarr_regression/test_results.json

Plot predictor validation metrics:

bash scripts/02_predictor/plot_predictor_validation.sh

Expected paper metrics:

• Pearson for label_0, label_1, and overall.
• R2 for label_0, label_1, and overall.
• MAE/MSE for predictor reliability.

This predictor is the oracle used to score all generated enhancer sequences.

3. Train Autoregressive GENERator Baselines

Train unconditional AR SFT:

bash scripts/03_ar_generation/train_ar_unconditional.sh

Train bucket-conditioned AR SFT:

bash scripts/03_ar_generation/train_ar_conditioned.sh

The conditioned model uses:

<sp0> = low activity
<sp1> = mid activity
<sp2> = high activity

Generated model paths:

${AR_UNCOND_MODEL}
${AR_COND_MODEL}

4. Evaluate AR Generation

Evaluate unconditional AR generation:

bash scripts/03_ar_generation/evaluate_ar_unconditional.sh

Evaluate conditioned AR generation:

bash scripts/03_ar_generation/evaluate_ar_conditioned.sh

Outputs:

${RESULT_ROOT}/ar_unconditional_valid/generation_details.jsonl
${RESULT_ROOT}/ar_unconditional_valid/generation_summary.json
${RESULT_ROOT}/ar_conditioned_valid/generation_details.jsonl
${RESULT_ROOT}/ar_conditioned_valid/generation_summary.json

These files provide sequence validity, uniqueness, bp accuracy, and generated sequence details.

5. Score AR Samples with the Predictor

Score unconditional AR samples:

bash scripts/05_scoring/score_ar_unconditional.sh

Score conditioned AR samples:

bash scripts/05_scoring/score_ar_conditioned.sh

Outputs:

${RESULT_ROOT}/ar_unconditional_scoring/scoring_details.jsonl
${RESULT_ROOT}/ar_unconditional_scoring/scoring_summary.json
${RESULT_ROOT}/ar_conditioned_scoring/scoring_details.jsonl
${RESULT_ROOT}/ar_conditioned_scoring/scoring_summary.json

These are required for controllability analysis:

• predicted label_0
• predicted label_1
• predicted activity sum
• delta vs matched reference
• positive-delta rate
• bucket-level high/mid/low separation

6. Train Masked Discrete Diffusion

Train the bucket-conditioned discrete diffusion model:

bash scripts/04_diffusion/train_diffusion_conditioned.sh

Default output:

${DIFFUSION_MODEL}

The diffusion training objective randomly masks A/C/G/T positions and trains a masked LM to recover the original bases. The condition token is prepended when --conditioned is enabled.

7. Evaluate Diffusion

First, run PLL-only evaluation:

bash scripts/04_diffusion/evaluate_diffusion_pll_only.sh

This measures diffusion pseudo-log-likelihood:

${RESULT_ROOT}/diffusion_valid_pll/diffusion_scoring_details.jsonl
${RESULT_ROOT}/diffusion_valid_pll/diffusion_scoring_summary.json

Then run predictor-scored diffusion evaluation:

bash scripts/04_diffusion/evaluate_diffusion_with_predictor.sh

This is mandatory for the final paper because PLL is not an activity score. The output is:

${RESULT_ROOT}/diffusion_valid_predictor/diffusion_scoring_details.jsonl
${RESULT_ROOT}/diffusion_valid_predictor/diffusion_scoring_summary.json

Use this to compare diffusion against AR conditioned generation under the same predictor.

8. Compute Sequence Quality and Distribution Metrics

Run:

bash scripts/06_sequence_metrics/compute_sequence_metrics.sh

Outputs:

${RESULT_ROOT}/sequence_metrics/sequence_metrics_rows.csv
${RESULT_ROOT}/sequence_metrics/sequence_metrics_summary.json

Metrics include:

• valid DNA rate
• unique rate
• sequence length
• GC content
• max homopolymer length
• pairwise Hamming distance
• nearest-reference Hamming distance
• 3-mer and 4-mer Jensen-Shannon divergence to reference
• predictor score when available
• diffusion PLL when available

These metrics are the core data for the generator comparison table.

9. Optional Motif Analysis

Download a JASPAR motif file manually, then set:

export JASPAR_MOTIFS=/path/to/JASPAR2024_CORE_non-redundant_pfms_jaspar.txt

Run motif scan:

bash scripts/07_motif_analysis/run_motif_scan.sh

Output:

${RESULT_ROOT}/motif_analysis/motif_scan_summary.csv

Use this for motif enrichment and regulatory grammar figures.

If no JASPAR file is available, skip this step and report motif analysis as a future extension.

10. Build Paper Figures

Run:

bash scripts/08_visualization/make_paper_figures.sh

Outputs:

${FIGURE_ROOT}/paper/fig_generation_gc_content.png
${FIGURE_ROOT}/paper/fig_generation_homopolymer.png
${FIGURE_ROOT}/paper/fig_nearest_reference_distance.png
${FIGURE_ROOT}/paper/fig_predicted_activity_by_method.png
${FIGURE_ROOT}/paper/fig_activity_2d_scatter.png
${FIGURE_ROOT}/paper/fig_diffusion_pll.png
${FIGURE_ROOT}/paper/fig_generator_quality_summary.png
${FIGURE_ROOT}/paper/table_generator_comparison.csv

If motif analysis was run, it also creates:

${FIGURE_ROOT}/paper/fig_motif_hit_rate_heatmap.png

11. One-Command Runs

Core paper experiments, excluding motif analysis:

cd /Users/ouzhang/Desktop/genRL/project
bash scripts/09_all/run_all_core.sh

Core experiments plus motif analysis:

export JASPAR_MOTIFS=/path/to/JASPAR2024_CORE_non-redundant_pfms_jaspar.txt
bash scripts/09_all/run_all_with_optional_motifs.sh

12. Recommended Paper Tables and Figures

Tables

1. Dataset split statistics:

   • from ${RESULT_ROOT}/data/deepstarr_split_summary.json

2. Predictor performance:

   • from ${PREDICTOR_DIR}/test_results.json

3. Generator comparison:

   • from ${RESULT_ROOT}/sequence_metrics/sequence_metrics_summary.json
   • from ${FIGURE_ROOT}/paper/table_generator_comparison.csv

4. Controllability by bucket:

• AR: ${RESULT_ROOT}/ar_conditioned_scoring/scoring_summary.json
• diffusion: ${RESULT_ROOT}/diffusion_valid_predictor/diffusion_scoring_summary.json
• negative control: ${RESULT_ROOT}/controls/random_gc_matched.jsonl

Figures

1. Pipeline figure:

   • manually draw from data -> predictor -> AR/diffusion -> evaluation.

2. Predictor validation:

   • ${FIGURE_ROOT}/predictor/predictor_metrics.png

3. Generation quality:

   • ${FIGURE_ROOT}/paper/fig_generation_gc_content.png
   • ${FIGURE_ROOT}/paper/fig_generation_homopolymer.png
   • ${FIGURE_ROOT}/paper/fig_nearest_reference_distance.png

4. Controllability:

   • ${FIGURE_ROOT}/paper/fig_predicted_activity_by_method.png
   • ${FIGURE_ROOT}/paper/fig_activity_2d_scatter.png

5. Diffusion naturalness:

   • ${FIGURE_ROOT}/paper/fig_diffusion_pll.png

6. Motif/regulatory grammar:

   • ${FIGURE_ROOT}/paper/fig_motif_hit_rate_heatmap.png

13. Minimum Experiments Needed for a Defensible Paper

Do not submit the paper until these are complete:

1. Predictor trained and tested.
2. AR unconditional generated and scored.
3. AR conditioned generated and scored.
4. Diffusion conditioned generated and scored with the same predictor.
5. Sequence quality metrics computed for all methods.
6. At least one negative/control baseline included:
   • reference sequences
   • random GC-matched sequences
   • shuffled enhancers
   • or unconditional generator

The current project already has many pieces, but the most important missing piece is diffusion evaluation with --predictor_model. Without that, diffusion can only be discussed as naturalness/PLL analysis, not enhancer activity control.

14. Paper Strengthening Experiments

After 01_data, 02_predictor, and the first conditioned diffusion model in 04_diffusion are complete, run the extra experiments under:

scripts/10_paper_strengthening/

These scripts are ordered by priority.

P0: Minimum paper closure

This finishes the core paper comparison:

nohup bash scripts/10_paper_strengthening/p0_minimum_paper_closure.sh > p0_minimum_paper_closure.log 2>&1 &
tail -f p0_minimum_paper_closure.log

It runs:

• predictor validation plot
• AR unconditional training/evaluation/scoring
• AR conditioned training/evaluation/scoring
• diffusion predictor evaluation
• sequence metrics
• paper figure generation

P1: Core ablations

Mask-ratio ablation:

nohup bash scripts/10_paper_strengthening/p1_mask_ratio_ablation.sh > p1_mask_ratio_ablation.log 2>&1 &
tail -f p1_mask_ratio_ablation.log

Conditioning ablation:

nohup bash scripts/10_paper_strengthening/p1_conditioning_ablation.sh > p1_conditioning_ablation.log 2>&1 &
tail -f p1_conditioning_ablation.log

These are the most important extra experiments for the mutation-budget claim:

• lower mask ratios should preserve reference-like sequence structure
• higher mask ratios should increase novelty and diversity
• conditioned diffusion should show stronger low/mid/high activity separation than unconditioned diffusion

P2: Test split and denoising step ablation

Test split evaluation:

nohup bash scripts/10_paper_strengthening/p2_test_split_diffusion.sh > p2_test_split_diffusion.log 2>&1 &
tail -f p2_test_split_diffusion.log

Denoising step ablation:

nohup bash scripts/10_paper_strengthening/p2_diffusion_steps_ablation.sh > p2_diffusion_steps_ablation.log 2>&1 &
tail -f p2_diffusion_steps_ablation.log

Use this to report final held-out results and the quality/runtime trade-off for 16, 32, 64, and 128 denoising steps.

P3: Seed robustness

nohup bash scripts/10_paper_strengthening/p3_seed_repeats_diffusion.sh > p3_seed_repeats_diffusion.log 2>&1 &
tail -f p3_seed_repeats_diffusion.log

Use the resulting summaries to report mean and standard deviation over seeds.

P4: Paired refinement / evolutionary editing

This is the key experiment for the "diffusion as refiner" claim. It compares the same input sequence before and after a small masked-diffusion edit:

delta = predictor(edited_sequence) - predictor(reference_sequence)

It covers:

• paired delta distributions across rho = 0.01, 0.03, 0.05, 0.10, 0.20
• random mask vs entropy-guided vs logit-gap-guided position selection
• best-of-N refinement curves

Recommended full run:

PROJECT_ROOT=$(pwd) \
RUN_ROOT=$(pwd)/paper_runs \
DEEPSTARR_DIR=$(pwd)/datas/DeepSTARR-enhancer-activity \
GENERANNO_BASE_MODEL=$(pwd)/models/GENERanno-eukaryote-0.5b-base \
DIFFUSION_MODEL=$(pwd)/saved_model/deepstarr_discrete_diffusion \
PREDICTOR_MODEL=$(pwd)/paper_runs/results/deepstarr_regression/best_model \
REFINEMENT_SPLIT=test \
REFINEMENT_NUM_SEQUENCES=1024 \
REFINEMENT_NUM_SAMPLES=8 \
REFINEMENT_MASK_RATIOS="0.01 0.03 0.05 0.10 0.20" \
REFINEMENT_STRATEGIES="random entropy logit_gap" \
REFINEMENT_DIFFUSION_STEPS=32 \
REFINEMENT_BATCH_SIZE=16 \
PREDICTOR_SCORE_BATCH_SIZE=256 \
TRANSFORMERS_NO_TF=1 \
USE_TF=0 \
TOKENIZERS_PARALLELISM=false \
nohup bash scripts/11_refinement/run_paired_refinement.sh > paired_refinement.log 2>&1 &
tail -f paired_refinement.log

Outputs are written to:

paper_runs/results/refinement/paired_refinement_test

Recommended server environment

On the GPU server, set project-local data/model paths before these scripts:

cd /inspire/hdd/project/intelligentcreativedesign/dangshengqi-253114050252/z-anna/genrl-enhancer-diffusion

export PROJECT_ROOT=$(pwd)
export RUN_ROOT=${PROJECT_ROOT}/paper_runs
export HF_ENDPOINT=https://hf-mirror.com
export TRANSFORMERS_NO_TF=1
export USE_TF=0
export TOKENIZERS_PARALLELISM=false

source scripts/00_setup/env.sh

export DEEPSTARR_DIR=${PROJECT_ROOT}/datas/DeepSTARR-enhancer-activity
export DEEPSTARR_DATASET_ID=${DEEPSTARR_DIR}
export GENERATOR_BASE_MODEL=${PROJECT_ROOT}/models/GENERator-eukaryote-1.2b-base
export GENERANNO_BASE_MODEL=${PROJECT_ROOT}/models/GENERanno-eukaryote-0.5b-base
export PREDICTOR_DIR=${PROJECT_ROOT}/paper_runs/results/deepstarr_regression
export PREDICTOR_MODEL=${PREDICTOR_DIR}/best_model
export DIFFUSION_MODEL=${PROJECT_ROOT}/saved_model/deepstarr_discrete_diffusion