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RLoopBench

RLoopBench is a benchmark for evaluating whether DNA foundation models generalize to R-loop-forming sequence prediction. The benchmark compares rule-based features, classical sequence encodings, task-specific deep learning models, and DNA foundation model embeddings under a unified linear-probe evaluation framework.

R-loops are three-stranded nucleic acid structures consisting of an RNA--DNA hybrid and a displaced single-stranded DNA strand. Because R-loop formation is associated with transcription, replication stress, genome instability, and disease-related genome dysfunction, R-loop prediction provides a biologically distinct test case beyond conventional gene regulatory benchmarks.

Repository overview

This repository provides:

  • Example linear-probe training code using 3-mer encoding
  • Pretrained linear-probe classifiers for each representation
  • Inference scripts for applying trained linear probes

Methods included

RLoopBench evaluates the following representation paradigms.

Rule-based method

  • QmRLFS-finder

Please refer to http://r-loop.org/?pg=qmrlfs for details.

Classical sequence representations

  • 3-mer frequency
  • 4-mer frequency
  • One-hot encoding

Trained models and inference scripts are provided in models/. These scripts can be run using the conda environment specified in environment.yml.

conda env create -f environment.yml
conda activate rloop_bench

We also provide an example workflow in training/ showing how to generate 3-mer embeddings, visualize embeddings, and train a linear-probe classifier.

Task-specific deep learning models

Please refer to the original repositories for installation and usage details.

DNA foundation model embeddings

The trained linear-probe models and reference inference scripts for these representations are provided in models/.

Please use the foundation-model inference scripts together with each DNA foundation model's own implementation environment. The scripts in this repository are intended as reference implementations for reproducing the embedding-to-linear-probe inference step.