A deep intronic TCTN2 variant activating a cryptic exon predicted by SpliceRover in a patient with Joubert syndrome

Hiraide, Takuya; Shimizu, Kenji; Okumura, Yoshinori; Miyamoto, Sachiko; Nakashima, Mitsuko; Ogata, Tsutomu; Saitsu, Hirotomo

doi:10.1038/s10038-023-01143-3

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…SpliceRover employs convolutional neural networks to identify splice sites, offering a more nuanced analysis compared to traditional probabilistic methods [27]. It detected a significant cryptic exon in Joubert syndrome [44]. Additionally, tools like the Variant Effect Scoring Tool (VEST) use algorithms like random forest to prioritize gene variants for diseases like Freeman-Sheldon syndrome and Miller syndrome, outperforming other tools in missense variant prioritization [28].…”

Section: Mutation Detection or Predictionmentioning

confidence: 99%

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

Alganmi

2024

BioMedInformatics

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Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain.

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Section: Mutation Detection or Predictionmentioning

confidence: 99%

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

Alganmi

2024

BioMedInformatics

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RNA sequencing and target long-read sequencing reveal an intronic transposon insertion causing aberrant splicing

Kawakami,

Hiraide,

Watanabe

et al. 2023

J Hum Genet

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Pathogenic cryptic variants detectable through exome data reanalysis significantly increase the diagnostic yield in Joubert syndrome

D’Abrusco,

Serpieri,

Taccagni

et al. 2024

Eur J Hum Genet

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Joubert syndrome (JS) is a genetically heterogeneous neurodevelopmental ciliopathy. Despite exome sequencing (ES), several patients remain undiagnosed. This study aims to increase the diagnostic yield by uncovering cryptic variants through targeted ES reanalysis. We first focused on 26 patients in whom ES only disclosed heterozygous pathogenic coding variants in a JS gene. We reanalyzed raw ES data searching for copy number variants (CNVs) and intronic variants affecting splicing. We validated CNVs through real-time PCR or chromosomal microarray, and splicing variants through RT-PCR or minigenes. Cryptic variants were then searched in additional 44 ES-negative JS individuals. We identified cryptic “second hits” in 14 of 26 children (54%) and biallelic cryptic variants in 3 of 44 (7%), reaching a definite diagnosis in 17 of 70 (overall diagnostic gain 24%). We show that CNVs and intronic splicing variants are a common mutational mechanism in JS; more importantly, we demonstrate that a significant proportion of such variants can be disclosed simply through a focused reanalysis of available ES data, with a significantly increase of the diagnostic yield especially among patients previously found to carry heterozygous coding variants in the KIAA0586, CC2D2A and CPLANE1 genes.

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A deep intronic TCTN2 variant activating a cryptic exon predicted by SpliceRover in a patient with Joubert syndrome

Cited by 5 publications

References 38 publications

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

RNA sequencing and target long-read sequencing reveal an intronic transposon insertion causing aberrant splicing

Pathogenic cryptic variants detectable through exome data reanalysis significantly increase the diagnostic yield in Joubert syndrome

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