---
license: cc-by-4.0
tags:
  - biology
  - rna
  - genomics
configs:
  - config_name: utr3
    data_files:
    - split: train
      path: utr-variants-bohn-utr3.parquet
  - config_name: utr5
    data_files:
    - split: train
      path: utr-variants-bohn-utr5.parquet
---

# Overview

This utr variant effect prediction task measures the pathogenicity of single nucleotide polymorpism (SNPs) and indels (insertion-deletions) in 3' and 5' UTRs. This dataset is a reprocessing of the dataset from DeepGenomics (Bohn et al. Frontiers in Molecular Biosciences. 2023), see the original paper for the data generation process. We provide the mRNA transcript sequence context for the variant and wildtype sequences using the specified transcript id from RefSeq v109.

This dataset is redistributed as part of mRNABench: https://github.com/morrislab/mRNABench

# Data Format

Description of data columns:
- `target`: Whether the specified sequence contains a pathogenic mutation.
- `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.
- `description`: Description of the sequence, either in the format `chr{chr}:{position} {ref_base}:{alt_base}[,optional variant impact]` or `wild-type` for the unmodified sequence.

Note: The position of the variant is 1-indexed (ClinVar notation) and refers to the position of the variant on the positive strand.

# Data Source
This dataset is generated using a dataset collected by a team at DeepGenomics and is under a CC by 4.0 license.

Please attribute:

Original paper: https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2023.1257550/full  
Original paper GitHub: https://github.com/deepgenomics/UTR_variants_DL_manuscript  
3' UTR Variant data source: https://github.com/deepgenomics/UTR_variants_DL_manuscript/blob/main/data/utr3_plp_benchmark.tsv  
5' UTR Variant data source: https://github.com/deepgenomics/UTR_variants_DL_manuscript/blob/main/data/utr5_plp_benchmark.tsv  
Citation: Bohn E, Lau TTY, Wagih O, Masud T and Merico D (2023) A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect prediction. Front. Mol. Biosci. 10:1257550. doi: 10.3389/fmolb.2023.1257550