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
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