mogam-ai/CDS-BART-Tc-Riboswitches

📊 Tc-Riboswitch Dataset

This dataset contains synthetic tetracycline (Tc) riboswitches along with their functional performance metrics. It's a curated resource for training machine learning models to predict gene expression from RNA sequences. The original dataset is from CodonBERT.

⁉️ Dataset Contents

• Sequence: The mRNA sequence of the synthetic riboswitch.
• Switching_factor: A numerical value that quantifies the riboswitch's regulatory effect. This value represents the change in gene expression in the presence versus the absence of tetracycline.

🎯 Purpose

This dataset serves as a benchmark for fine-tuning models on a regression task, predicting riboswitch behavior from its sequence. We used this dataset to fine-tune CDS-BART, a BART-based foundation model trained on massive mRNA sequences. Demonstrating its ability to perform downstream tasks related to mRNA regulation which are fine-tuned for various mRNA-related downstream task. CDS-BART available at GitHub

🔧Usage

from datasets import load_dataset

dataset = load_dataset('mogam-ai/CDS-BART-Tc-Riboswitches')

📚 Dataset Reference

• Groher, Ann-Christin, et al. "Tuning the performance of synthetic riboswitches using machine learning." ACS Synthetic Biology 8.1 (2018): 34-44.
• Li, Sizhen, et al. "CodonBERT large language model for mRNA vaccines." Genome research 34.7 (2024): 1027-1035.