ArticleNucleic acids research2017
mirDNMR: a gene-centered database of background de novo mutation rates in human.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Gene4Denovo2: an updated platform for human de novo mutations discovery and interpretation.Nucleic acids research · 2026Article
- Germline genome editing of human IVF embryos should not be subject to overly stringent restrictions.Journal of assisted reproduction and genetics · 2024Article
- Identification of de novo Mutations in the Chinese Autism Spectrum Disorder Cohort via Whole-Exome Sequencing Unveils Brain Regions Implicated in Autism.Neuroscience bulletin · 2023Article
- FertilityOnline: A Straightforward Pipeline for Functional Gene Annotation and Disease Mutation Discovery.Genomics, proteomics & bioinformatics · 2022Article
- Gene4Denovo: an integrated database and analytic platform for de novo mutations in humans.Nucleic acids research · 2020Article
- Nonrandom occurrence of multiple de novo coding variants in a proband indicates the existence of an oligogenic model in autism.Genetics in medicine : official journal of the American College of Medical Genetics · 2020Article
- Review
- Prioritized High-Confidence Risk Genes for Intellectual Disability Reveal Molecular Convergence During Brain Development.Frontiers in genetics · 2018Article
- The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes.Nucleic acids research · 2017Article
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Authors and funding
10 authors.
Funding
Abstract
De novo germline mutations (DNMs) are the rarest genetic variants proven to cause a considerable number of sporadic genetic diseases, such as autism spectrum disorders, epileptic encephalopathy, schizophrenia, congenital heart disease, type 1 diabetes, and hearing loss. However, it is difficult to accurately assess the cause of DNMs and identify disease-causing genes from the considerable number of DNMs in probands. A common method to this problem is to identify genes that harbor significantly more DNMs than expected by chance, with accurate background DNM rate (DNMR) required. Therefore, in this study, we developed a novel database named mirDNMR for the collection of gene-centered background DNMRs obtained from different methods and population variation data. The database has the following functions: (i) browse and search the background DNMRs of each gene predicted by four different methods, including GC content (DNMR-GC), sequence context (DNMR-SC), multiple factors (DNMR-MF) and local DNA methylation level (DNMR-DM); (ii) search variant frequencies in publicly available databases, including ExAC, ESP6500, UK10K, 1000G and dbSNP and (iii) investigate the DNM burden to prioritize candidate genes based on the four background DNMRs using three statistical methods (TADA, Binomial and Poisson test). As a case study, we successfully employed our database in candidate gene prioritization for a sporadic complex disease: intellectual disability. In conclusion, mirDNMR (https://www.wzgenomics.cn/mirdnmr/) can be widely used to identify the genetic basis of sporadic genetic diseases.
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