README.md

metadata

task_categories:
  - text-generation
tags:
  - biology
  - dna
  - trace-reconstruction

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.

• Paper: Trace Reconstruction with Language Models
• Code: MLI-lab/TReconLM

The real-world datasets used for fine-tuning are available here:

• Microsoft Clustered Nanopore Reads
• Noisy DNA Dataset

The corresponding test sets used in the paper can be reproduced using the preprocessing scripts in our GitHub repository under data/.

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 notebook in our GitHub repository.

To run inference via the command line:

python src/inference.py exps=<experiment>