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Machine learning approaches for the prioritization of genomic variants impacting pre-mRNA splicing

Machine learning approaches for the prioritization of genomic variants impacting pre-mRNA splicing
Machine learning approaches for the prioritization of genomic variants impacting pre-mRNA splicing
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.
2073-4409
Rowlands, Charlie F.
33e03aa5-fcdd-4f08-ac34-45489338a03c
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Ellingford, Jamie M.
e84f25d6-9c76-44e8-b764-1ec81825032e
Rowlands, Charlie F.
33e03aa5-fcdd-4f08-ac34-45489338a03c
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Ellingford, Jamie M.
e84f25d6-9c76-44e8-b764-1ec81825032e

Rowlands, Charlie F., Baralle, Diana and Ellingford, Jamie M. (2019) Machine learning approaches for the prioritization of genomic variants impacting pre-mRNA splicing. Cells, 8 (12), [1513]. (doi:10.3390/cells8121513).

Record type: Review

Abstract

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.

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Rowlands_et_al_2019-Machine_learning_in_splicing_prediction3 - Accepted Manuscript
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More information

Accepted/In Press date: 21 November 2019
e-pub ahead of print date: 26 November 2019

Identifiers

Local EPrints ID: 436123
URI: http://eprints.soton.ac.uk/id/eprint/436123
ISSN: 2073-4409
PURE UUID: 8d7168e6-366f-434d-b9b1-bda540371d9b
ORCID for Diana Baralle: ORCID iD orcid.org/0000-0003-3217-4833

Catalogue record

Date deposited: 29 Nov 2019 17:30
Last modified: 26 Nov 2021 02:53

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Contributors

Author: Charlie F. Rowlands
Author: Diana Baralle ORCID iD
Author: Jamie M. Ellingford

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