license: unknown
tags:
- biology
- rna
- genomics
configs:
- config_name: egfp
data_files:
- split: train
path: mrl-sample-egfp.parquet
- config_name: mcherry
data_files:
- split: train
path: mrl-sample-mcherry.parquet
- config_name: varying
data_files:
- split: train
path: mrl-sample-varying.parquet
- config_name: designed
data_files:
- split: train
path: mrl-sample-designed.parquet
Overview
Mean ribosome load (MRL) is a measure of translational efficient. This experimental dataset is a massively parallel translational assay, which assesses the translational impact of randomized 5'UTR sequences within an eGFP or mCherry reporter construct. For the eGFP experiments, two alternative RNA biologies are also evaluated. The varying and designed subsets use a variable length 5'UTR sequence and algorithmically designed 5'UTRs, respectively. Two duplicate were performed per experiment; the average between runs is reported here.
This dataset is redistributed as part of mRNABench: https://github.com/morrislab/mRNABench
Data Format
Description of data columns:
target_mrl_{subset-name}_{chemistry}: Mean ribosome loading for sequence. In eGFP datasets, two alternative RNA biologies are used:pseudofor pseudouridine,m1pseudofor 1-methylpseudouridine.unmoddenotes unmodified RNA biology.cds: Binary track which reports position of first nucleotide in each codon in CDS.splice: Binary track which reports position of the 3' end of each exon, indicating splice sites.
Additional data columns in mrl-sample-egfp:
u_start: Binary track which reports whether the 5' UTR contains an upstream start codonu_oof_start: Binary track which reports whether the 5' UTR contains an out-of-frame upstream start codonkozak_quality: String ("strong", "weak", or "mixed"), indicating the strength of the Kozak sequence, as determined by the original paper Sample et al. 2019.
Data Source
This dataset is a Hugging Face redistribution of the MRL dataset originally collected by Sample et al. 2019. The data has been obtained from the Gene Expression Omnibus, with accession number GSE114002 and BioProject PRJNA454863. Data on NCBI GEO is assumed to be free for use and distribution, but no specific license is provided by the authors. Listed below are the data sources and citation:
Original paper: https://pmc.ncbi.nlm.nih.gov/articles/PMC7100133/
Original dataset source: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114002
Citation: Sample, P.J., Wang, B., Reid, D.W. et al. Human 5′ UTR design and variant effect prediction from a massively parallel translation assay. Nat Biotechnol 37, 803–809 (2019). https://doi.org/10.1038/s41587-019-0164-5