Investigation of human genetic variants in neurodegeneration
Ontology highlight
ABSTRACT:
INSTRUMENT(S): -
ORGANISM(S): Homo Sapiens
SUBMITTER: University of Bologna
PROVIDER: PRJEB47822 | EVA | 2021-10-02
REPOSITORIES: EVA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| 01-003_S3.vcf.gz | Vcf | |||
| 01-003_S3.vcf.gz.csi | Vcf | |||
| 01-003_S3.vcf.gz.tbi | Vcf | |||
| 02-110_S13.vcf.gz | Vcf | |||
| 02-110_S13.vcf.gz.csi | Vcf |
Items per page: 5 1 - 5 of 339 |
Similar Datasets
Project description:In this study, the authors had developed a machine learning model to predict immune checkpoint blockade (ICB) response by integrating genomic, molecular, demographic and clinical data from a curated cohort (MSK-IMPACT) with 1479 patients treated with ICB across 16 different types of cancer. This model significantly outperformed the predictions based on Tumor Mutational Burden (TCB). This model uses two types of random forests, one uses 16 features and the other uses 11 features. These features are selected based on their permutation importance score. The model was deployed on docker to reproduce the results and the data was shared to promote FAIReR sharing of machine learning models.
2024-07-23 | MODEL2407210002 | BioModels
Project description:The series is composed of thirty hybridizations for analysis of differentially expressed genes in normal placenta tissues from patients with normal labor and placenta tissues from patients with severe preeclampsia. Patients tissues samples were obtained from (Vasilis. Total RNA extraction was performed by using the MagNa Pure Compact RNA Isolation Kit (Roche Applied Science). Keywords: Gene expression study, Disease state Twenty-six out of 50 normal placentas were randomly selected to match with 17 preeclamptic placentas, accounting for parity. Microarrays were performed applying a direct comparison design.
2010-05-26 | E-GEOD-10588 | biostudies-arrayexpress
Project description:MAGs investigation of soil science
| PRJEB41477 | ENA
Project description:The objective of the study was to characterize the immunoreactivity profiles of IgG-reactive epitopes in COVID-19 patients with distinct disease trajectories as well as SARS-CoV-2-naïve sera, using a high-density SARS-CoV-2 whole proteome peptide microarray. The microarray comprised of a total of 5347 individual peptides, each consisting of 15 amino acids with an overlap of 13 amino acids printed in duplicate. The microarray also had a panel of the most relevant mutations from SARS-CoV-2 variants of concern like omicron, alpha, beta, gamma, delta, and others. This study consisted of 29 participants, including 10 naïve controls (5 pre-pandemic and 5 SARS-CoV-2 seronegative) and 19 RT-PCR-confirmed COVID-19 patients. The COVID-19 patients were stratified into two distinct cohorts based on their disease trajectories: the severe cohort (S), in which the patients presented moderate COVID-19 symptoms initially but eventually progressed toward severity; and the recovered cohort (R), in which severe COVID-19 patients progressed toward recovery. Our findings contribute to a deeper understanding of the immunopathogenesis of COVID-19 in patients with different disease trajectories, the effect of mutations on immunoreactivity, and potential cross-reactivity due to exposure to common cold viruses.
2025-03-31 | E-MTAB-14495 | biostudies-arrayexpress
Project description:We applied Next-Generation Sequencing (NGS) to miRNAs from blood samples of 48 AD (Alzheimer's Disease) patients and 22 unaffected controls, yielding a total of 140 unique mature miRNAs with significantly changed expression level. Of these, 82 were higher and 58 lower abundant in samples from AD patients. We selected a panel of 12 miRNAs for a qRT-PCR analysis on a larger cohort of 202 samples including not only AD patients and healthy controls but also patients with other CNS illnesses: Multiple Sclerosis, Parkinson's Disease, Major Depression, Bipolar Disorder, Schizophrenia, and Mild Cognitive Impairment, which is assumed to represent a transitional period before the development of AD. MiRNA target enrichment analysis of the selected 12 miRNAs indicated an involvement of miRNAs in nervous system development, neuron projection, neuron projection development, and neuron projection morphogenesis, respectively. Using this 12-miRNA signature we were able to differentiate between AD and controls with an accuracy of 93.3%, a specificity of 95.1%, and a sensitivity of 91.5%. The differentiation of AD from other neurological diseases was possible with accuracies between 73.8% and 77.8%. The differentiation of the other CNS disorders from controls yielded even higher accuracies. Examination of the miRNA profile in blood samples of 48 AD patients and 22 controls
2013-06-17 | E-GEOD-46579 | biostudies-arrayexpress
Project description:We descrie a cohort of 10 families, with 16 patients, that presented with severe early onset allergic disease since birth. Disease inhertance was in an autosomal dominant manner and heterozygous variants in STAT6 were identified in all patients. Whole blood bulk RNAsequencing was done on patient 6 to understand treatment specific transcriptomic changes in this patient.
2023-02-20 | GSE222646 | GEO
Project description:We applied Next-Generation Sequencing (NGS) to miRNAs from blood samples of 48 AD (Alzheimer's Disease) patients and 22 unaffected controls, yielding a total of 140 unique mature miRNAs with significantly changed expression level. Of these, 82 were higher and 58 lower abundant in samples from AD patients. We selected a panel of 12 miRNAs for a qRT-PCR analysis on a larger cohort of 202 samples including not only AD patients and healthy controls but also patients with other CNS illnesses: Multiple Sclerosis, Parkinson's Disease, Major Depression, Bipolar Disorder, Schizophrenia, and Mild Cognitive Impairment, which is assumed to represent a transitional period before the development of AD. MiRNA target enrichment analysis of the selected 12 miRNAs indicated an involvement of miRNAs in nervous system development, neuron projection, neuron projection development, and neuron projection morphogenesis, respectively. Using this 12-miRNA signature we were able to differentiate between AD and controls with an accuracy of 93.3%, a specificity of 95.1%, and a sensitivity of 91.5%. The differentiation of AD from other neurological diseases was possible with accuracies between 73.8% and 77.8%. The differentiation of the other CNS disorders from controls yielded even higher accuracies.
2013-06-17 | GSE46579 | GEO
Project description:Background and Aim: Crohn's disease (CD) and ulcerative colitis (UC) are the two major chronic inflammatory bowel diseases (IBD). Although their symptoms are similar, their pathological features and clinical treatments differ. Currently, distinguishing between these diseases involves invasive procedures such as colonoscopy and histopathology, causing discomfort and inconvenience to patients. The use of fecal proteins as non-invasive biomarkers offers a promising alternative due to their stability and proximity to inflamed tissues. This study focuses on using high-throughput data-independent acquisition (DIA) mass spectrometry to develop accurate biomarker signatures from complex stool samples. Methods: Stool samples obtained from 46 active CD patients and 23 active UC patients were analyzed. Using DIA-based SWATH mass spectrometry, we explored the stool proteome, identifying and quantifying approximately 1,250 proteins. The samples were divided into training and testing groups. After data processing, various feature selection algorithms were applied on training group to determine proteins that were significantly different between the CD and UC groups. Additionally, six machine learning algorithms including k-Nearest Neighbors, Naive Bayes, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, and glmnet were evaluated to identify the best-performing classifiers. Results: Sixteen proteins were selected based of several feature selection algorithms and the six ML models trained based on them. According to performance metrics of each algorithm on the training dataset, Naïve Bayes model was selected. For performance validation, the final predictive model was applied to 16 prospective samples as the test dataset. Remarkably, the model achieved an AUC of 0.95 on training dataset and AUC of 0.96 on the test dataset, demonstrating its robustness and lack of overfitting. Conclusion: This study demonstrates the effectiveness of SWATH-based proteomics and machine learning in developing predictive models to classify CD and UC. Further future validation on a larger cohort using targeted MRM mass spectrometry would be served to establish the clinical utility and reliability of this approach.
2025-12-01 | PXD057120 | Pride
Project description:The series is composed of thirty hybridizations for analysis of differentially expressed genes in normal placenta tissues from patients with normal labor and placenta tissues from patients with severe preeclampsia. Patients tissues samples were obtained from (Vasilis. Total RNA extraction was performed by using the MagNa Pure Compact RNA Isolation Kit (Roche Applied Science). Keywords: Gene expression study, Disease state
2008-06-20 | GSE10588 | GEO
Project description:We descrie a cohort of 10 families, with 16 patients, that presented with severe early onset allergic disease since birth. Disease inhertance was in an autosomal dominant manner and heterozygous variants in STAT6 were identified in all patients. Whole blood bulk RNAsequencing was done on patient 6 to understand treatment specific transcriptomic changes in this patient.
2023-02-20 | GSE215405 | GEO