MutPred Splice Logo
Identifying coding region variants which disrupt pre mRNA-splicing and the underlying mechanism (v1.3.2)
MutPred Splice Goal
MutPred Splice is designed prioritize exonic variants (e.g missense or samesense) which are likely to disrupt pre mRNA splicing from NGS data.
Updates
Jan 2014 - MutPred Splice Version 1.3.2 released
Jan 2014 - RefSeq annotation set updated to GRCh37p10 and model updated
Dec 2013 - MutPred Splice Version 1.3.1 released
Interpretation of results
The output of MutPred Splice are:
1, General Score, which is the probability that the variant disrupts splicing. We use a general score >=0.6 to identify a variant which disrupts splicing.
e.g. general score >=0.6 labels a variant as a Splice Affecting Variant (SAV)
e.g. general score <0.6 labels a variant as a Splice Neutral Variant (SNV)
2, Additional supporting evidence is provided by a confident hypothesis about the splicing mechanism disrupted.
Practical advice
MutPred Splice can be used to prioritise your dataset into three partions:
1, High Confident calls of splicing variants - predicted SAV (general score >=0.6) where a confident hypothesis is available.
2, Confident calls of splicing variants - predicted SAV (general score >=0.6) where a confident hypothesis not available.
3, Not predicted to disrupt splicing (SNV) (general score <0.6).
We recommend the use of multiple programs when assessing the impact of a variant on splicing. One example of a workflow would be to use MutPred Splice to identify candidate splicing variants from a large dataset and then investigate this subset of SAVs in detail with a tool like Human Splicing Finder.
If you are wish to analyse a large dataset, please contact us (see below)
Feedback / Comments / Suggestions
Please feel free to email your comments and suggestions
Contact
Email mortm [at] cf.ac.uk
Citing this web site
If you use our tool in your work, please cite our paper:
MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing Matthew Mort, Tim Sterne-Weiler, Biao Li, Edward V Ball, David N Cooper, Predrag Radivojac, Jeremy R Sanford and Sean D Mooney