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RNA splicing analysis in genomic medicine

RNA splicing analysis in genomic medicine
RNA splicing analysis in genomic medicine
High-throughput next-generation sequencing technologies have led to a rapid increase in the number of sequence variants identified in clinical practice via diagnostic genetic tests. Current bioinformatic analysis pipelines fail to take adequate account of the possible splicing effects of such variants, particularly where variants fall outwith canonical splice site sequences, and consequently the pathogenicity of such variants may often be missed. The regulation of splicing is highly complex and as a result, in silico prediction tools lack sufficient sensitivity and specificity for reliable use. Variants of all kinds can be linked to aberrant splicing in disease and the need for correct identification and diagnosis grows ever more crucial as novel splice-switching antisense oligonucleotide therapies start to enter clinical usage. RT-PCR provides a useful targeted assay of the splicing effects of identified variants, while minigene assays, massive parallel reporter assays and animal models can also be used for more detailed study of a particular splicing system, given enough time and resources. However, RNA-sequencing (RNA-seq) has the potential to be used as a rapid diagnostic tool in genomic medicine. By utilising data science approaches and machine learning, it may prove possible to finally understand and interpret the ‘splicing code’ and apply this knowledge in human disease diagnostics.
61-73
Wai, Htoo
4428517b-33b3-42cb-9818-ca64763ab7bc
Douglas, Andrew
2c789ec4-a222-43bc-a040-522ca64fea42
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Wai, Htoo
4428517b-33b3-42cb-9818-ca64763ab7bc
Douglas, Andrew
2c789ec4-a222-43bc-a040-522ca64fea42
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91

Wai, Htoo, Douglas, Andrew and Baralle, Diana (2018) RNA splicing analysis in genomic medicine. The International Journal of Biochemistry & Cell Biology, 108, 61-73. (doi:10.1016/j.biocel.2018.12.009).

Record type: Article

Abstract

High-throughput next-generation sequencing technologies have led to a rapid increase in the number of sequence variants identified in clinical practice via diagnostic genetic tests. Current bioinformatic analysis pipelines fail to take adequate account of the possible splicing effects of such variants, particularly where variants fall outwith canonical splice site sequences, and consequently the pathogenicity of such variants may often be missed. The regulation of splicing is highly complex and as a result, in silico prediction tools lack sufficient sensitivity and specificity for reliable use. Variants of all kinds can be linked to aberrant splicing in disease and the need for correct identification and diagnosis grows ever more crucial as novel splice-switching antisense oligonucleotide therapies start to enter clinical usage. RT-PCR provides a useful targeted assay of the splicing effects of identified variants, while minigene assays, massive parallel reporter assays and animal models can also be used for more detailed study of a particular splicing system, given enough time and resources. However, RNA-sequencing (RNA-seq) has the potential to be used as a rapid diagnostic tool in genomic medicine. By utilising data science approaches and machine learning, it may prove possible to finally understand and interpret the ‘splicing code’ and apply this knowledge in human disease diagnostics.

Text
Splicing Review revised normal version For submission - Accepted Manuscript
Restricted to Repository staff only until 27 December 2019.
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More information

Accepted/In Press date: 14 December 2018
e-pub ahead of print date: 27 December 2018
Published date: 27 December 2018

Identifiers

Local EPrints ID: 426962
URI: https://eprints.soton.ac.uk/id/eprint/426962
PURE UUID: 1a802fd6-a040-4e9f-b455-1560e338bf1c
ORCID for Andrew Douglas: ORCID iD orcid.org/0000-0001-5154-6714
ORCID for Diana Baralle: ORCID iD orcid.org/0000-0003-3217-4833

Catalogue record

Date deposited: 19 Dec 2018 17:30
Last modified: 15 Aug 2019 00:41

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