morrislab/mrl-sample

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: pseudo for pseudouridine, m1pseudo for 1-methylpseudouridine. unmod denotes 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 codon
• u_oof_start: Binary track which reports whether the 5' UTR contains an out-of-frame upstream start codon
• kozak_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