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).
The real-world datasets used for fine-tuning are available here:
The corresponding test sets used in the paper can be reproduced using the preprocessing scripts in our GitHub repository under data/.
Synthetic Dataset Generation
Synthetic datasets are generated using 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.txtContains the original DNA sequences, one per linereads.txtContains 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.ptA 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 notebook in our GitHub repository.