Description

AbSplice is a method that predicts aberrant splicing across human tissues, as described in Wagner, Çelik et al., 2023. This track displays precomputed AbSplice scores for all possible single-nucleotide variants genome-wide. The scores represent the probability that a given variant causes aberrant splicing in a given tissue. AbSplice scores can be computed from VCF files and are based on quantitative tissue-specific splice site annotations (SpliceMaps). While SpliceMaps can be generated for any tissue of interest from a cohort of RNA-seq samples, this track includes 49 tissues available from the Genotype-Tissue Expression (GTEx) dataset.

Display Conventions

The AbSplice score is a probability estimate of how likely aberrant splicing of some sort takes place in a given tissue. The authors suggest three cutoffs which are represented by color in the track.

• High (red) - An AbSplice score over 0.2 indicates a high likelihood of aberrant splicing in at least one tissue.
• Medium (orange) - A score between 0.05 and 0.2 indicates a medium likelihood.
• Low (blue) - A score between 0.01 and 0.05 indicates a low likelihood.
• Scores below 0.01 are not displayed.

Mouseover on items shows the gene name, maximum score, and tissues that had this score. Clicking on any item brings up a table with scores for all 49 GTEX tissues.

Data Access

The raw data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information.

Precomputed AbSplice-DNA scores in all 49 GTEx tissues are available at Zenodo.

Methods

Data was converted from the files (AbSplice_DNA_ hg38 _snvs_high_scores.zip) provided by the authors at zenodo.org. Files in the score_cutoff=0.01 directory were concatenated. To convert the data to bigBed format, scores and their tissues were selected from the AbSplice_DNA fields and maximum scores, and then calculated using a custom Python script, which can be found in the makeDoc from our GitHub repository.

Credits

Thanks to Nils Wagner for helpful comments and suggestions.

References

Wagner N, Çelik MH, Hölzlwimmer FR, Mertes C, Prokisch H, Yépez VA, Gagneur J. Aberrant splicing prediction across human tissues. Nat Genet. 2023 May;55(5):861-870. PMID: 37142848