ShenLab Webtools

SpliceTransformer predicts tissue-specific splicing affect

Variant prediction

Input should be in "chromosome_offset_reference_alternation" format. (hg38)

VariantΔSpliceAdiposeMuscleBlood VesselBrainKidneyHeartLiverLungSkinNerve
No data

Predict splicing changes and specifically affected tissues for the variant.

ΔSplice score > 0.3 means the variant is likely to affect splicing with pathogenicity.

ΔSplice score > 0.1 means the variant is likely to affect splicing but have uncertain significance.

ΔSplice score < 0.1 means the variant is likely to have no effect on splicing.

Tissue columns shows predicted tissue specificity for the variant.

If no columns is "Yes", it means the variant is likely to affect splicing in all tissues.

  

You can also directly fetch the api like
The result will be in json format. A string like "score|1011111110" represents the prediction, where the "score" is predicted ΔSplice score, and each digit in the number represents tissue specificity for Adipose, Muscle, Blood_Vessel, Brain, Kidney, Heart, Liver, Lung, Skin, Nerve respectively.

SpTransformer source code is available at GithubThe model can be deployed locally to predict massive variants from VCF files.