SynthesisBMC bioinformatics2017
dbMDEGA: a database for meta-analysis of differentially expressed genes in autism spectrum disorder.
Synthesis in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions.Metabolic brain disease · 2024Pooled it
- Convergence and divergence of genes informed by common and rare variants of autism spectrum disorders in tissue-specific pathways and gene networks.Translational psychiatry · 2026Article
- RNA sequencing of blood from sex- and age-matched discordant siblings supports immune and transcriptional dysregulation in autism spectrum disorder.Scientific reports · 2023Article
- Superoxide dismutase isozymes in cerebral organoids from autism spectrum disorder patients.Journal of neural transmission (Vienna, Austria : 1996) · 2022Article
- Integration of Transcriptome and Exome Genotyping Identifies Significant Variants with Autism Spectrum Disorder.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Article
- Placental DNA methylation levels at CYP2E1 and IRS2 are associated with child outcome in a prospective autism study.Human molecular genetics · 2019Article
- RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation.Molecular autism · 2019Article
- The Relevance of Variants With Unknown Significance for Autism Spectrum Disorder Considering the Genotype-Phenotype Interrelationship.Frontiers in psychiatry · 2019Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundAutism spectrum disorders (ASD) are hereditary, heterogeneous and biologically complex neurodevelopmental disorders. Individual studies on gene expression in ASD cannot provide clear consensus conclusions. Therefore, a systematic review to synthesize the current findings from brain tissues and a search tool to share the meta-analysis results are urgently needed.
methodsHere, we conducted a meta-analysis of brain gene expression profiles in the current reported human ASD expression datasets (with 84 frozen male cortex samples, 17 female cortex samples, 32 cerebellum samples and 4 formalin fixed samples) and knock-out mouse ASD model expression datasets (with 80 collective brain samples). Then, we applied R language software and developed an interactive shared and updated database (dbMDEGA) displaying the results of meta-analysis of data from ASD studies regarding differentially expressed genes (DEGs) in the brain.
resultsThis database, dbMDEGA ( https://dbmdega.shinyapps.io/dbMDEGA/ ), is a publicly available web-portal for manual annotation and visualization of DEGs in the brain from data from ASD studies. This database uniquely presents meta-analysis values and homologous forest plots of DEGs in brain tissues. Gene entries are annotated with meta-values, statistical values and forest plots of DEGs in brain samples. This database aims to provide searchable meta-analysis results based on the current reported brain gene expression datasets of ASD to help detect candidate genes underlying this disorder.
conclusionThis new analytical tool may provide valuable assistance in the discovery of DEGs and the elucidation of the molecular pathogenicity of ASD. This database model may be replicated to study other disorders.
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