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Review

Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing

1
North West Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, St Mary’s Hospital, Manchester M13 9WJ, UK
2
Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PR, UK
3
Human Development and Health, Faculty of Medicine, University of Southampton, MP808, Tremona Road, Southampton SO16 6YD, UK
*
Authors to whom correspondence should be addressed.
Cells 2019, 8(12), 1513; https://doi.org/10.3390/cells8121513
Received: 29 October 2019 / Revised: 20 November 2019 / Accepted: 21 November 2019 / Published: 26 November 2019
Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient’s variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care. View Full-Text
Keywords: Mendelian disease; diagnostics; variant interpretation; variant prioritization; RNA splicing; bioinformatics; machine learning; genomic medicine; effect prediction Mendelian disease; diagnostics; variant interpretation; variant prioritization; RNA splicing; bioinformatics; machine learning; genomic medicine; effect prediction
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MDPI and ACS Style

Rowlands, C.F.; Baralle, D.; Ellingford, J.M. Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing. Cells 2019, 8, 1513. https://doi.org/10.3390/cells8121513

AMA Style

Rowlands CF, Baralle D, Ellingford JM. Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing. Cells. 2019; 8(12):1513. https://doi.org/10.3390/cells8121513

Chicago/Turabian Style

Rowlands, Charlie F, Diana Baralle, and Jamie M Ellingford. 2019. "Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing" Cells 8, no. 12: 1513. https://doi.org/10.3390/cells8121513

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