PdmIRD: missense variants pathogenicity prediction for inherited retinal diseases in a disease-specific manner

Abramovs N, Brass A, Tassabehji M (2020) GeVIR is a continuous gene-level metric that uses variant distribution patterns to prioritize disease candidate genes. Nat Genet 52:35–39. https://doi.org/10.1038/s41588-019-0560-2

Article  CAS  PubMed  Google Scholar 

Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249. https://doi.org/10.1038/nmeth0410-248

Article  CAS  PubMed  PubMed Central  Google Scholar 

Alirezaie N, Kernohan KD, Hartley T et al (2018) ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants. Am J Hum Genet 103:474–483. https://doi.org/10.1016/j.ajhg.2018.08.005

Article  CAS  PubMed  PubMed Central  Google Scholar 

Amberger JS, Hamosh A (2018) Searching Online Mendelian Inheritance in Man (OMIM): A Knowledgebase of Human Genes and Genetic Phenotypes. Curr Protoc Bioinforma 58:1–20. https://doi.org/10.1002/cpbi.27.Searching

Article  Google Scholar 

Auton A, Abecasis GR, Altshuler DM et al (2015) A global reference for human genetic variation. Nature 526:68–74. https://doi.org/10.1038/nature15393

Article  CAS  PubMed  Google Scholar 

Bateman A (2019) UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res 47:D506–D515. https://doi.org/10.1093/nar/gky1049

Article  CAS  Google Scholar 

Bowne SJ, Daiger SP, Malone KA et al (2003) Characterization of RP1L1, a highly polymorphic paralog of the retinitis pigmentosa 1 (RP1) gene. Mol vis 9:129–137

CAS  PubMed  Google Scholar 

Brandes N, Goldman G, Wang CH et al (2023) Genome-wide prediction of disease variant effects with a deep protein language model. Nat Genet 55:1512–1522. https://doi.org/10.1038/s41588-023-01465-0

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bu F, Zhong M, Chen Q et al (2022) DVPred: a disease-specific prediction tool for variant pathogenicity classification for hearing loss. Hum Genet 141:401–411. https://doi.org/10.1007/s00439-022-02440-1

Article  PubMed  Google Scholar 

Cao Y, Li L, Xu M et al (2020) The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals. Cell Res. https://doi.org/10.1038/s41422-020-0322-9

Article  PubMed  PubMed Central  Google Scholar 

Carss KJ, Arno G, Erwood M et al (2017) Comprehensive Rare Variant Analysis via Whole-Genome Sequencing to Determine the Molecular Pathology of Inherited Retinal Disease. Am J Hum Genet 100:75–90. https://doi.org/10.1016/j.ajhg.2016.12.003

Article  CAS  PubMed  Google Scholar 

Chen TC, Huang DS, Lin CW et al (2021) Genetic characteristics and epidemiology of inherited retinal degeneration in Taiwan. Npj Genomic Med 6:1–8. https://doi.org/10.1038/s41525-021-00180-1

Article  CAS  Google Scholar 

Cheng N, Li M, Zhao L et al (2020) Comparison and integration of computational methods for deleterious synonymous mutation prediction. Brief Bioinform 21:970–981. https://doi.org/10.1093/bib/bbz047

Article  CAS  PubMed  Google Scholar 

Cheng J, Novati G, Pan J et al (2023) Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (-80) 7492:1–18

Google Scholar 

Cornelis SS, Bax NM, Zernant J et al (2017) In Silico Functional Meta-Analysis of 5,962 ABCA4 Variants in 3,928 Retinal Dystrophy Cases. Hum Mutat 38:400–408. https://doi.org/10.1002/humu.23165

Article  CAS  PubMed  Google Scholar 

Den Hollander AI, Davis J, Van Der Velde-Visser SD et al (2004) CRB1 mutation spectrum in inherited retinal dystrophies. Hum Mutat 24:355–369. https://doi.org/10.1002/humu.20093

Article  CAS  Google Scholar 

Ellingford JM, Barton S, Bhaskar S et al (2016) Molecular findings from 537 individuals with inherited retinal disease. J Med Genet 53:761–767. https://doi.org/10.1136/jmedgenet-2016-103837

Article  CAS  PubMed  Google Scholar 

Fadista J, Oskolkov N, Hansson O, Groop L (2017) LoFtool: A gene intolerance score based on loss-of-function variants in 60 706 individuals. Bioinformatics 33:471–474. https://doi.org/10.1093/bioinformatics/btv602

Article  CAS  PubMed  Google Scholar 

Fang M, Su Z, Abolhassani H et al (2022) VIPPID: A gene-specific single nucleotide variant pathogenicity prediction tool for primary immunodeficiency diseases. Brief Bioinform 23:1–10. https://doi.org/10.1093/bib/bbac176

Article  CAS  Google Scholar 

Frazer J, Notin P, Dias M et al (2021) Disease variant prediction with deep generative models of evolutionary data. Nature 599:91–95. https://doi.org/10.1038/s41586-021-04043-8

Article  CAS  PubMed  Google Scholar 

Gao FJ, Gao FJ, Gao FJ et al (2020) Mutation spectrum of the bestrophin-1 gene in a large Chinese cohort with bestrophinopathy. Br J Ophthalmol 104:846–851. https://doi.org/10.1136/bjophthalmol-2019-314679

Article  PubMed  Google Scholar 

Grimm DG, Azencott CA, Aicheler F et al (2015) The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Hum Mutat 36:513–523. https://doi.org/10.1002/humu.22768

Article  PubMed  PubMed Central  Google Scholar 

Hanany M, Rivolta C, Sharon D (2020) Worldwide carrier frequency and genetic prevalence of autosomal recessive inherited retinal diseases. Proc Natl Acad Sci U S A 117:2710–2716. https://doi.org/10.1073/pnas.1913179117

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ichi US, Isaka Y, Miyagawa M, Ya NS (2022) Variants in CDH23 cause a broad spectrum of hearing loss: from non-syndromic to syndromic hearing loss as well as from congenital to age-related hearing loss. Hum Genet 141:903–914. https://doi.org/10.1007/s00439-022-02431-2

Article  CAS  Google Scholar 

Ioannidis NM, Rothstein JH, Pejaver V et al (2016) REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet 99:877–885. https://doi.org/10.1016/j.ajhg.2016.08.016

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jagadeesh KA, Wenger AM, Berger MJ et al (2016) M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat Genet 48:1581–1586. https://doi.org/10.1038/ng.3703

Article  CAS  PubMed  Google Scholar 

Jiang T, Wang K, Fang L (2021) MutFormer: A context-dependent transformer-based model to predict pathogenic missense mutations. arXiv

Karczewski KJ, Weisburd B, Thomas B et al (2017) The ExAC browser: Displaying reference data information from over 60 000 exomes. Nucleic Acids Res 45:D840–D845. https://doi.org/10.1093/nar/gkw971

Article  CAS  PubMed  Google Scholar 

Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4:1073–1082. https://doi.org/10.1038/nprot.2009.86

Article  CAS  PubMed  Google Scholar 

Kumaran M, Devarajan B (2023) eyeVarP: a computational framework for the identification of pathogenic variants specific to eye disease. Genet Med. https://doi.org/10.1016/j.gim.2023.100862

Article  PubMed  Google Scholar 

Kwilas AR, Donahue RN, Tsang KY, Hodge JW (2015) An expanded sequence context model broadly explains variability in polymorphism levels across the human genome. Cancer Cell 2:1–17. https://doi.org/10.1038/ng.3511.An

Article  Google Scholar 

Lam BL, Leroy BP, Black G et al (2021) Genetic testing and diagnosis of inherited retinal diseases. Orphanet J Rare Dis 16:1–9. https://doi.org/10.1186/s13023-021-02145-0

Article  CAS  Google Scholar 

Landrum MJ, Lee JM, Benson M et al (2018) ClinVar: Improving access to variant interpretations and supporting evidence. Nucleic Acids Res 46:D1062–D1067. https://doi.org/10.1093/nar/gkx1153

Article  CAS  PubMed  Google Scholar 

Lek M, Karczewski KJ, Minikel EV et al (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:285–291. https://doi.org/10.1038/nature19057

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li Q, Liu X, Gibbs RA et al (2014) Gene-specific function prediction for non-synonymous mutations in monogenic diabetes genes. PLoS ONE. https://doi.org/10.1371/journal.pone.0104452

Article  PubMed  PubMed Central  Google Scholar 

Li S, Van Der Velde KJ, De Ridder D et al (2020) CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations. Genome Med 12:1–11. https://doi.org/10.1186/s13073-020-00775-w

Article  Google Scholar 

Liu H-K, Dang X, Guan L-P et al (2020a) A Phenotype-Specific Framework for Identifying the Eye Abnormalities Causative Nonsynonymous-Variants. SSRN Electron J. https://doi.org/10.2139/ssrn.3586993

Article  Google Scholar 

Liu X, Li C, Mou C et al (2020b) dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Med 12:1–8. https://

留言 (0)

沒有登入
gif