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RNA Degradation Prediction Dataset
Public Health & Epidemiology
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Free
About
This dataset is designed to advance the understanding and stabilisation of messenger RNA (mRNA) molecules for COVID-19 vaccines. The global fight against the pandemic necessitates effective, widely distributable vaccines. While mRNA vaccines are currently the fastest candidates, a significant challenge is their inherent instability; RNA molecules spontaneously degrade, and even a single cut can render a vaccine ineffective. Unlike conventional vaccines that can be shipped under refrigeration, current mRNA vaccines require intense cold storage, severely limiting their global reach. This dataset aims to address this by providing augmented data, derived through iterations of a high-quality script, to help computational biochemists identify degradation-prone regions in RNA backbones. This critical knowledge can lead to the design of super stable mRNA molecules, facilitating broader and more equitable vaccine distribution worldwide. The dataset supports pairing scientific and crowdsourced intelligence to make measurable progress in vaccine development.
Columns
- id: A unique identifier for each entry in the dataset.
- sequence: Represents the specific RNA sequence.
- structure: Describes the secondary structure of the RNA molecule.
- log_gamma: A numerical value, likely representing a logarithmic gamma measurement, with a range of 0.00 to 100.00. The mean is 53.4 and standard deviation is 48.6.
- score: A score value, ranging from 0.00 to 100.00. The mean is 52.4 and standard deviation is 49.6.
- cnt: A count value, ranging from 2.00 to 7.00. The mean is 4.34 and standard deviation is 1.
- predicted_loop_type: Indicates the predicted loop type within the RNA structure.
Distribution
The dataset is provided in CSV format and is approximately 16.99 MB in size. It comprises 7 columns and contains 43.6k valid rows or records.
Usage
This dataset is ideal for:
- Developing methods to design more stable mRNA molecules for COVID-19 vaccines.
- Accelerating research into vaccine candidates and their logistical challenges.
- Analysing RNA molecule degradation patterns and identifying vulnerable regions.
- Improving global vaccine distribution strategies by reducing stringent refrigeration requirements.
- Advancing computational biochemistry research related to RNA stability.
Coverage
The dataset focuses on the structural and stability properties of mRNA molecules, rather than specific geographical, temporal, or demographic coverage related to human populations or regions.
License
CC0: Public Domain
Who Can Use It
- Computational Biochemists: For designing more stable mRNA structures.
- Vaccine Researchers: To accelerate the development and distribution of effective vaccines.
- Data Scientists and Machine Learning Practitioners: For building models to predict and mitigate mRNA degradation.
- Public Health Strategists: To inform logistics and distribution plans for future mRNA vaccines.
- Crowdsourcing Participants: For collaborative scientific problem-solving related to biological data.
Dataset Name Suggestions
- COVID-19 mRNA Vaccine Stability Dataset
- Augmented mRNA Stability Data for Vaccine Research
- RNA Degradation Prediction Dataset
- SARS-CoV-2 mRNA Vaccine Stability Insights
- Stable mRNA Design Data
Attributes
Original Data Source: RNA Degradation Prediction Dataset
QUALITY