FML: a molecular approach
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB28960 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Molecular cytogenetic techniques such as microarray analysis have allowed for a “genotype-first” approach to the characterization of chromosome abnormalities: in the absence of clinical features suggestive of a specific syndrome, patients with similar copy number imbalances can be examined for common clinical features. Using a genotype-first approach, we characterized microdeletions at 20q13.33 in six patients referred for genetic evaluation of developmental delay, mental retardation, and/or congenital anomalies. These deletions are relatively rare, with only 11 cases reported. A comparison to previously reported cases of 20q13.33 microdeletion shows phenotypic overlap, with clinical features that include mental retardation, developmental delay, speech and language deficits, seizures, and behavior problems such as autistic spectrum disorder. Based on analysis of the smallest region of overlap (SRO) among cases reported here and in previous studies, we discuss several possible candidate genes for specific clinical features, including ARFGAP1, CHRNA4, KCNQ2, and MYT1. Deletion of this region may play an important role in cognitive development. aCGH control vs. patient, total of 6 patients
2010-12-31 | E-GEOD-21536 | biostudies-arrayexpress
Project description:Next Generation Sequencing targeted approach for the molecular diagnosis of Retinoblastoma.
| PRJEB9126 | ENA
Project description:A case of genetic hypoglycemia in an adult: A Molecular Approach
| PRJNA797262 | ENA
Project description:Molecular cytogenetic techniques such as microarray analysis have allowed for a “genotype-first” approach to the characterization of chromosome abnormalities: in the absence of clinical features suggestive of a specific syndrome, patients with similar copy number imbalances can be examined for common clinical features. Using a genotype-first approach, we characterized microdeletions at 20q13.33 in six patients referred for genetic evaluation of developmental delay, mental retardation, and/or congenital anomalies. These deletions are relatively rare, with only 11 cases reported. A comparison to previously reported cases of 20q13.33 microdeletion shows phenotypic overlap, with clinical features that include mental retardation, developmental delay, speech and language deficits, seizures, and behavior problems such as autistic spectrum disorder. Based on analysis of the smallest region of overlap (SRO) among cases reported here and in previous studies, we discuss several possible candidate genes for specific clinical features, including ARFGAP1, CHRNA4, KCNQ2, and MYT1. Deletion of this region may play an important role in cognitive development.
2010-12-31 | GSE21536 | GEO
Project description:Context: We present a case of a young adult with GCK mutation who had neonatal hypoglycaemia, re-emerging with hypoglycaemia later in life. Mechanistic insight is gained from detailed clinical, cellular and genomic analysis. Conclusion: This case highlights the variable phenotype of GCK mutations. In depth molecular analyses in the islets has revealed possible mechanisms for nesidioblastosis and insulin hypersecretion.
2022-03-12 | GSE193693 | GEO
Project description:Daphnia magna is a bio-indicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability. D. magna were exposed to 36 Chemicals and 5 control series in quadruplicate.
2013-01-17 | E-GEOD-43564 | biostudies-arrayexpress
Project description:Platelet reactivity (PR) in cardiovascular (CV) patients is variable between individuals and modulates clinical outcome. However, the determinants of platelet reactivity are largely unknown. Integration of data derived from high-throughput omics technologies may yield novel insights into the molecular mechanisms that govern platelet reactivity. The aim of this study was to identify candidate genes modulating platelet reactivity in aspirin-treated cardiovascular patients PR was assessed in 110 CV patients treated with aspirin 100mg/d by aggregometry using several agonists. 12 CV patients with extreme high or low PR were selected for transcriptomics, proteomics and miRNA analysis.
2016-02-18 | GSE66427 | GEO
Project description:Daphnia magna is a bio-indicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability.
2013-01-17 | GSE43564 | GEO