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--- |
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language: rna |
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tags: |
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- Biology |
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- RNA |
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license: |
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- agpl-3.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- text-generation |
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- fill-mask |
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task_ids: |
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- language-modeling |
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- masked-language-modeling |
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pretty_name: EternaBench-ChemMapping |
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library_name: multimolecule |
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--- |
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# EternaBench-ChemMapping |
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EternaBench-ChemMapping is a synthetic RNA dataset comprising 12,711 RNA constructs that have been chemically mapped using SHAPE and MAP-seq methods. |
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These RNA sequences are probed to obtain experimental data on their nucleotide reactivity, which indicates whether specific regions of the RNA are flexible or structured. |
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The dataset provides high-resolution, large-scale data that can be used for studying RNA folding and stability. |
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## Disclaimer |
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This is an UNOFFICIAL release of the [EternaBench-ChemMapping](https://github.com/eternagame/EternaBench) by Hannah K. Wayment-Steele, et al. |
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**The team releasing EternaBench-ChemMapping did not write this dataset card for this dataset so this dataset card has been written by the MultiMolecule team.** |
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## Dataset Description |
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- **Homepage**: https://multimolecule.danling.org/datasets/eternabench_cm |
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- **datasets**: https://huggingface.co/datasets/multimolecule/eternabench-cm |
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- **Point of Contact**: [Rhiju Das](https://biochemistry.stanford.edu/people/rhiju-das/) |
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The dataset includes a large set of synthetic RNA sequences with experimental chemical mapping data, which provides a quantitative readout of RNA nucleotide reactivity. These data are ensemble-averaged and serve as a critical benchmark for evaluating secondary structure prediction algorithms in their ability to model RNA folding dynamics. |
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## Example Entry |
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| index | design | sequence | secondary_structure | reactivity | errors | signal_to_noise | |
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| -------- | ---------------------- | ---------------- | ------------------- | -------------------------- | --------------------------- | --------------- | |
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| 769337-1 | d+m plots weaker again | GGAAAAAAAAAAA... | ................ | [0.642,1.4853,0.1629, ...] | [0.3181,0.4221,0.1823, ...] | 3.227 | |
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## Column Description |
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The EternaBench-ChemMapping dataset consists of the following columns, providing crucial insights for understanding RNA stability for vaccine design: |
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- **ID**: |
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A unique identifier for each RNA sequence entry. |
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- **design_name**: |
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The name given to each RNA design by contributors, used for easy reference. |
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- **sequence**: |
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The nucleotide sequence of the RNA molecule, represented using the standard RNA bases: |
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- **A**: Adenine |
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- **C**: Cytosine |
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- **G**: Guanine |
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- **U**: Uracil |
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- **secondary_structure**: |
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The secondary structure of the RNA represented in dot-bracket notation, using up to three types of symbols to indicate base pairing and unpaired regions, as per bpRNA's standard: |
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- **Dots (`.`)**: Represent unpaired nucleotides. |
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- **Parentheses (`(` and `)`)**: Represent base pairs in standard stems (page 1). |
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- **Square Brackets (`[` and `]`)**: Represent base pairs in pseudoknots (page 2). |
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- **Curly Braces (`{` and `}`)**: Represent base pairs in additional pseudoknots (page 3). |
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- **reactivity**: |
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A list of normalized reactivity values for each nucleotide, representing the likelihood that a nucleotide is unpaired. |
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High reactivity indicates high flexibility (unpaired regions), and low reactivity corresponds to paired or structured regions. |
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- **errors**: |
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Arrays of floating-point numbers indicating the experimental errors corresponding to the measurements in the **reactivity**. |
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These values help quantify the uncertainty in the degradation rates and reactivity measurements. |
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- **signal_to_noise**: |
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The signal-to-noise ratio calculated from the reactivity and error values, providing a measure of data quality. |
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## Related Datasets |
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- [EternaBench-ChemMapping](https://huggingface.co/datasets/multimolecule/eternabench-cm) |
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- [EternaBench-Switch](https://huggingface.co/datasets/multimolecule/eternabench-switch) |
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## License |
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This dataset is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html). |
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```spdx |
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SPDX-License-Identifier: AGPL-3.0-or-later |
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``` |
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## Citation |
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```bibtex |
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@article{waymentsteele2022rna, |
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author = {Wayment-Steele, Hannah K and Kladwang, Wipapat and Strom, Alexandra I and Lee, Jeehyung and Treuille, Adrien and Becka, Alex and {Eterna Participants} and Das, Rhiju}, |
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journal = {Nature Methods}, |
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month = oct, |
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number = 10, |
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pages = {1234--1242}, |
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publisher = {Springer Science and Business Media LLC}, |
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title = {{RNA} secondary structure packages evaluated and improved by high-throughput experiments}, |
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volume = 19, |
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year = 2022 |
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} |
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``` |
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