Article Synopsis
- Predicting the effect of genetic variants on splicing is tough, especially for non-canonical splice sites, often leading to missed diagnoses.
- Introme is a new tool that uses machine learning to combine predictions from various splice detection tools and factors in gene architecture to assess the impact of variants on splicing.
- In tests with 21,000 variants, Introme showed superior accuracy (auPRC: 0.98) compared to other tools for identifying clinically significant splice variants, and it's accessible on GitHub.
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Article Abstract
Predicting the impact of coding and noncoding variants on splicing is challenging, particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing splice prediction tools are complementary but knowing which to use for each splicing context remains difficult. Here, we describe Introme, which uses machine learning to integrate predictions from several splice detection tools, additional splicing rules, and gene architecture features to comprehensively evaluate the likelihood of a variant impacting splicing. Through extensive benchmarking across 21,000 splice-altering variants, Introme outperformed all tools (auPRC: 0.98) for the detection of clinically significant splice variants. Introme is available at https://github.com/CCICB/introme .
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10190034 | PMC |
| http://dx.doi.org/10.1186/s13059-023-02936-7 | DOI Listing |
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