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README.md

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+# TReconLM synthetic test sets
+
+This dataset contains the synthetic test sets used to evaluate TReconLM, a transformer-based model for trace reconstruction of noisy DNA sequences (see [our paper](https://arxiv.org/abs/XXXX.XXXXX)).
+
+The real-world datasets used for fine-tuning are available here:
+
+- [Microsoft Clustered Nanopore Reads](https://github.com/microsoft/clustered-nanopore-reads-dataset)
+- [Noisy DNA Dataset](https://figshare.com/s/cd611884b34a8c89f4b4)
+
+The corresponding test sets used in the paper can be reproduced using the preprocessing scripts in our GitHub repository under [`data/`](https://github.com/MLI-lab/TReconLM/tree/main/data).
+
+## Synthetic Dataset Generation
+
+Synthetic datasets are generated using [`data_generation.py`](https://github.com/MLI-lab/TReconLM/blob/main/src/data_pkg/data_generation.py). Each test set is created by:
+
+- Sampling a ground-truth sequence of length *L*
+- Introducing insertions, deletions, and substitutions with rates sampled uniformly from *[0.01, 0.1]*
+- Randomly selecting the number of noisy reads *N* between *2* and *10*
+
+
+### Files Included
+
+- **`ground_truth.txt`**
+  Contains the original DNA sequences, one per line
+
+- **`reads.txt`**
+  Contains the noisy traces (corrupted copies of the ground-truth sequences)
+  - Each line is a single read
+  - Clusters are separated by: `===============================`
+  - The *i*-th cluster corresponds to the *i*-th line in `ground_truth.txt`
+
+- **`test_x.pt`**
+  A PyTorch tensor containing tokenized and padded input sequences used as model input, formatted as: read1|read2|...|readN : ground_truth
+
+## Usage
+
+Instructions for running inference using these datasets and our pretrained models are provided in the [`trace_reconstruction.ipynb`](https://github.com/MLI-lab/TReconLM/blob/main/trace_reconstruction.ipynb) notebook in our GitHub repository.