Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features

Naito, Tatsuhiko

doi:10.1002/humu.23794

Cited by 17 publications

(14 citation statements)

References 28 publications

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“…Previously, CAGI has had just one small splicing challenge [https://genomeinterpretation.org/content/Splicing-2012]. CAGI5 included two full‐scale splicing challenges (Mount et al, ) and these have resulted in five papers from participants (Chen, Lu, Zhao, & Yang, ; Cheng, Çelik, Nguyen, Avsec, & Gagneur, ; Gotea, Margolin, & Elnitski, ; Naito, ; Wang, Wang, & Hu, ). The issue also contains an overview paper from one of the splicing data providers (Rhine et al, ).…”

Section: Introductionmentioning

confidence: 99%

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

et al. 2019

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Interpretation of genomic variation plays an essential role in the analysis of cancer and monogenic disease, and increasingly also in complex trait disease, with applications ranging from basic research to clinical decisions. Many computational impact prediction methods have been developed, yet the field lacks a clear consensus on their appropriate use and interpretation. The Critical Assessment of Genome Interpretation (CAGI, /'kā‐jē/) is a community experiment to objectively assess computational methods for predicting the phenotypic impacts of genomic variation. CAGI participants are provided genetic variants and make blind predictions of resulting phenotype. Independent assessors evaluate the predictions by comparing with experimental and clinical data. CAGI has completed five editions with the goals of establishing the state of art in genome interpretation and of encouraging new methodological developments. This special issue (https://onlinelibrary.wiley.com/toc/10981004/2019/40/9) comprises reports from CAGI, focusing on the fifth edition that culminated in a conference that took place 5 to 7 July 2018. CAGI5 was comprised of 14 challenges and engaged hundreds of participants from a dozen countries. This edition had a notable increase in splicing and expression regulatory variant challenges, while also continuing challenges on clinical genomics, as well as complex disease datasets and missense variants in diseases ranging from cancer to Pompe disease to schizophrenia. Full information about CAGI is at https://genomeinterpretation.org.

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Section: Introductionmentioning

confidence: 99%

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

et al. 2019

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“…Many successful applications in the field of genomics have been reported 25 . A typical application of deep learning for genomics is the prediction of the effects of non-coding and coding variants, where models encode the inputs of flanking nucleotide sequence data [26][27][28][29] . Another application is non-linear unsupervised learning of high-dimensional quantitative data from transcriptome 30,31 .…”

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confidence: 99%

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

et al. 2021

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Conventional human leukocyte antigen (HLA) imputation methods drop their performance for infrequent alleles, which is one of the factors that reduce the reliability of trans-ethnic major histocompatibility complex (MHC) fine-mapping due to inter-ethnic heterogeneity in allele frequency spectra. We develop DEEP*HLA, a deep learning method for imputing HLA genotypes. Through validation using the Japanese and European HLA reference panels (n = 1,118 and 5,122), DEEP*HLA achieves the highest accuracies with significant superiority for low-frequency and rare alleles. DEEP*HLA is less dependent on distance-dependent linkage disequilibrium decay of the target alleles and might capture the complicated region-wide information. We apply DEEP*HLA to type 1 diabetes GWAS data from BioBank Japan (n = 62,387) and UK Biobank (n = 354,459), and successfully disentangle independently associated class I and II HLA variants with shared risk among diverse populations (the top signal at amino acid position 71 of HLA-DRβ1; P = 7.5 × 10−120). Our study illustrates the value of deep learning in genotype imputation and trans-ethnic MHC fine-mapping.

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“…23 Numerous algorithms have been presented for the prioritization of SAVs. [24][25][26][27][28][29] Recently, machine learning methods surpassed previous state-of-the-art results in the prediction of pathogenic SAVs including sequence based deep neural networks 30,31 and gradient boosting trees. 15 However, it is not straightforward to interpret the results of these methods.…”

Section: Introductionmentioning

confidence: 99%

Interpretable prioritization of splice variants in diagnostic next-generation sequencing

Daniš

Jacobsen

Carmody

et al. 2021

Preprint

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A critical challenge in genetic diagnostics is the computational assessment of candidate splice variants, specifically the interpretation of nucleotide changes located outside of the highly conserved dinucleotide sequences at the 5′ and 3′ ends of introns. To address this gap, we developed the Super Quick Informationcontent Random-forest Learning of Splice variants (SQUIRLS) algorithm. SQUIRLS generates a small set of interpretable features for machine learning by calculating the information-content (IC) of wildtype and variant sequences of canonical and cryptic splice sites, assessing changes in candidate splicing regulatory sequences, and incorporating characteristics of the sequence such as exon length, disruptions of the AG exclusion zone, and conservation. We curated a comprehensive collection of disease-associated splicealtering variants at positions outside of the highly conserved AG/GT dinucleotides at the termini of introns. SQUIRLS trains two random-forest classifiers for the donor and for the acceptor and combines their outputs by logistic regression to yield a final score. We show that SQUIRLS transcends previous state of the art accuracy in classifying splice variants as assessed by rank analysis in simulated exomes and is significantly faster than competing methods. SQUIRLS provides tabular output files for incorporation into diagnostic pipelines for exome and genome analysis, as well as visualizations that contextualize predicted effects of variants on splicing to make it easier to interpret splice variants in diagnostic settings

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Predicting the impact of single nucleotide variants on splicing via sequence‐based deep neural networks and genomic features

Cited by 17 publications

References 28 publications

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

Reports from the fifth edition of CAGI: The Critical Assessment of Genome Interpretation

A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes

Interpretable prioritization of splice variants in diagnostic next-generation sequencing

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