Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis.
RNA sequencing
diagnostics
machine learning
rare disease
splicing
variant interpretation
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
20 09 2023
20 09 2023
Historique:
received:
14
03
2023
revised:
10
07
2023
accepted:
20
07
2023
medline:
25
9
2023
pubmed:
15
8
2023
entrez:
14
8
2023
Statut:
ppublish
Résumé
Genomic variants affecting pre-messenger RNA splicing and its regulation are known to underlie many rare genetic diseases. However, common workflows for genetic diagnosis and clinical variant interpretation frequently overlook splice-altering variants. To better serve patient populations and advance biomedical knowledge, it has become increasingly important to develop and refine approaches for detecting and interpreting pathogenic splicing variants. In this review, we will summarize a few recent developments and challenges in using RNA sequencing technologies for rare disease investigation. Moreover, we will discuss how recent computational splicing prediction tools have emerged as complementary approaches for revealing disease-causing variants underlying splicing defects. We speculate that continuous improvements to sequencing technologies and predictive modeling will not only expand our understanding of splicing regulation but also bring us closer to filling the diagnostic gap for rare disease patients.
Identifiants
pubmed: 37580177
pii: 7242261
doi: 10.1093/bib/bbad284
pmc: PMC10516351
pii:
doi:
Substances chimiques
Proteins
0
Types de publication
Review
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NHGRI NIH HHS
ID : T32 HG000046
Pays : United States
Organisme : NIH HHS
ID : R01GM088342
Pays : United States
Organisme : NHGRI NIH HHS
ID : R56 HG012310
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM121827
Pays : United States
Organisme : NINDS NIH HHS
ID : U54 NS115198
Pays : United States
Organisme : NINDS NIH HHS
ID : K08 NS118119
Pays : United States
Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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