Gene Expression Omnibus is a public functional genomics data repository supporting MIAME-compliant submissions of array- and sequence-based data. Tools are provided to help users query and download experiments and curated gene expression profiles.

Gene expression data from Gene Expression Omnibus (GEO) database.

Gene Expression Omnibus (GEO) Dataset: GSE68086

This dataset, available on the Gene Expression Omnibus (GEO) platform, provides valuable insights into cancer diagnostics through the analysis of tumor-educated platelets (TEPs). It highlights the potential of liquid biopsies for non-invasive cancer detection across multiple cancer types.

Dataset Overview:

• Title: RNA-seq of tumor-educated platelets enables blood-based pan-cancer, multiclass, and molecular pathway cancer diagnostics.
• Organism: Homo sapiens
• Experiment Type: Expression profiling by high-throughput sequencing
• Sample Size: 283 blood platelet samples
  • 228 tumor-educated platelet (TEP) samples from patients with six different malignant tumors.
  • 55 samples from healthy individuals.

Cancer Types Included: - Non-small cell lung cancer - Colorectal cancer - Pancreatic cancer - Glioblastoma - Breast cancer - Hepatobiliary carcinomas

Methodology:

• Sample Collection: Blood platelets were isolated from whole blood using EDTA anti-coagulant.
• RNA Extraction: Total RNA was extracted from platelet pellets using the mirVana RNA isolation kit.
• Sequencing: cDNA synthesis and amplification were performed using the SMARTer Ultra Low RNA Kit, followed by Covaris shearing and Illumina HiSeq 2500 sequencing.
• Quality Control: Performed using Bioanalyzer 2100 with RNA 6000 Picochip, DNA 7500, and DNA High Sensitivity chips.

Data Processing:

• Quality control using Trimmomatic
• Mapping to the hg19 reference genome using STAR (version 2.3.0)
• Intron-spanning reads selected using Picard-tools (version 1.115)
• Read summarization using HTseq (version 0.6.1)

Data Structure:

• Samples: 285 columns (including controls)
• Features: 57,736 Ensembl gene IDs (rows)
• Data Type: Intron-spanning read counts

Files Included:

1. GSE68086_TEP_data_matrix.txt.gz (3.6 MB): Original gzipped text file containing intron-spanning RNA-seq read counts.
2. GSE68086_TEP_data_matrix.csv: Converted CSV file of the original data.
3. GSE68086_series_matrix.txt: Series matrix file containing detailed sample information.
4. GSE68086_series_matrix.csv: Converted CSV version of the series matrix file.

Potential Applications:

• Non-invasive cancer diagnostics: Exploring liquid biopsies for cancer detection.
• Identification of cancer-specific biomarkers.
• Study of cancer-induced changes in platelet RNA profiles.
• Comparative analysis across different cancer types.

Machine Learning Models for:

• Binary classification: Healthy vs. cancer patients.
• Multiclass classification: Distinguishing between different cancer types.
• Molecular pathway analysis for identifying cancer-specific pathways.

Importance:

This dataset offers significant potential for advancing cancer diagnostics by leveraging tumor-educated platelets as biomarkers for early detection and classification of various cancer types. It represents a promising approach to non-invasive, blood-based cancer screening using gene expression profiles.

Data Access and Analysis:

• GEO Accession: GSE68086
• Online Analysis: Available through GEO2R
• R Package: Data can be accessed and analyzed using the GEOquery package.

Citation: Best MG, Sol N, Kooi I, Tannous J, et al. RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics. Cancer Cell, 2015 Nov 9;28(5):666-676. PMID: 26525104

We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository.

- This release includes GEO series published up to Dec-31, 2020;

geo-htseq.tar.gz archive contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of NCBI GEO series.

- output/suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions.

- output/suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO.

- output/publications.csv, publication info of NCBI GEO series.

- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series

- output/spots.csv, NCBI SRA sequencing run metadata.

- output/cancer.csv, cancer related experiment accessions.

- output/transcription_factor.csv, TF related experiment accessions.

- output/single-cell.csv, single cell experiment accessions.

- blacklist.txt, list of supplementary files that were either too large to import or were causing computing environment crash during import.

Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq.

geo-htseq-updates.tar.gz archive contains files:

- results/detools_from_pmc.csv, differential expression analysis programs inferred from published articles

- results/n_data.csv, manually curated sample size info for NCBI GEO HT-seq series

- results/simres_df_parsed.csv, pi0 values estimated from differential expression results obtained from simulated RNA-seq data

- results/data/parsed_suppfiles_rerun.csv, pi0 values estimated using smoother method from anti-conservative p-value sets

Human DNA methylation data stored in NCBI (GEO) Dataset GSM281962; liver tissue sample 7041_CV_RRBS https://seek.lisym.org/samples/135

Human RNA-Seq data set GSM2819712 stored in NCBI (GEO)

The GEO Profiles database stores gene expression profiles derived from curated GEO DataSets. Each Profile is presented as a chart that displays the expression level of one gene across all Samples within a DataSet. Experimental context is provided in the bars along the bottom of the charts making it possible to see at a glance whether a gene is differentially expressed across different experimental conditions. Profiles have various types of links including internal links that connect genes that exhibit similar behaviour, and external links to relevant records in other NCBI databases. GEO Profiles can be searched using many different attributes including keywords, gene symbols, gene names, GenBank accession numbers, or Profiles flagged as being differentially expressed.

The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.The OMUFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

The Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath.The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI VIS swath, OMVANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir.The OMUANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

Dataset

This dataset was created by Victor Jacob Asuquo

Released under Apache 2.0

Contents

Open Postcode Geo is a postcode dataset and API optimised for geocoding applications. You can use Open Postcode Geo to geocode a dataset, geocode user input, and therefore build a proximity search. Data is derived from the ONS (Office for National Statistics) postcode database and is free to use, subject to including attributions to ONS, OS (Ordinance Survey) and Royal Mail. Information is also provided on a range of topics, including education, health, crime, business, etc. Postcodes can be entered at area, district, sector, and unit level - see Postcode map for the geographical relationship between these.

Ovarian cancer is a malignant tumor with different clinicopathological and molecular characteristics. Due to its nonspecific early symptoms, the majority of patients are diagnosed with local or extensive metastasis, severely affecting treatment and prognosis. The occurrence of ovarian cancer is influenced by multiple complex mechanisms including genomics, transcriptomics, and proteomics. Integrating multiple types of omics data aids in predicting the survival rate of ovarian cancer patients. However, existing methods only fuse multi-omics data at the feature level, neglecting the shared and complementary neighborhood information among samples of multi-omics data, and failing to consider the potential interactions between different omics data at the molecular level. In this paper, we propose a prognostic model for ovarian cancer prediction named Dual Fusion Channels and Stacked Graph Convolutional Neural Network (DFASGCNS). The DFASGCNS utilizes dual fusion channels to learn feature representations of different omics data and the associations between samples. Stacked graph convolutional network is used to comprehensively learn the deep and intricate correlation networks present in multi-omics data, enhancing the model’s ability to represent multi-omics data. An attention mechanism is introduced to allocate different weights to important features of different omics data, optimizing the feature representation of multi-omics data. Experimental results demonstrate that compared to existing methods, the DFASGCNS model exhibits significant advantages in ovarian cancer prognosis prediction and survival analysis. Kaplan-Meier curve analysis results indicate significant differences in the survival subgroups predicted by the DFASGCNS model, contributing to a deeper understanding of the pathogenesis of ovarian cancer and providing more reliable auxiliary diagnostic information for the prognosis assessment of ovarian cancer patients.

We introduce a new high-throughput transcriptomics (HTTr) platform comprised of a collagen sandwich primary rat hepatocyte culture and the TempO-Seq assay for screening and prioritizing potential hepatotoxicants. We selected 14 chemicals based on their risk of drug-induced liver injury (DILI) and tested them in hepatocytes at two treatment concentrations. HTTr data was generated using the TempO-Seq whole transcriptome and S1500+ assays. The HTTr platform exhibited high reproducibility between technical replicates (r>0.9) but biological replication was greater for TempO-Seq S1500+ (r>0.85) than for the whole transcriptome (r>0.7). Reproducibility between biological replicates was dependent on the strength of transcriptional effects induced by a chemical treatment. Despite targeting a smaller number of genes, the S1500+ assay clustered chemical treatments and produced gene set enrichment analysis (GSEA) scores comparable to those of the whole transcriptome. Connectivity mapping showed a high-level of reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project with high concordance between the S1500+ gene set and whole transcriptome. Taken together, our results provide guidance on selecting the number of technical and biological replicates and support the use of TempO-Seq S1500+ assay for a high-throughput platform for screening hepatotoxicants. FASTQ files and read counts data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) (GSE152128). This dataset is associated with the following publication: Lee, F., I. Shah, Y.T. Soong, J. Xing, I.C. Ng, F. Tasnim, and H. Yu. Reproducibility and Robustness of High-Throughput S1500+ Transcriptomics on Primary Rat Hepatocytes for Chemical-Induced Hepatotoxicity Assessment. Current Research in Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 282-295, (2021).

2012 Methodology Study Public with Geo

BackgroundDiabetic nephropathy (DN) is a common complication of diabetes, characterized by damage to renal tubules and glomeruli, leading to progressive renal dysfunction. The aim of our study is to explore the key role of metabolic reprogramming (MR) in the pathogenesis of DN.MethodsIn our study, three transcriptome datasets (GSE30528, GSE30529, and GSE96804) were sourced from the Gene Expression Omnibus (GEO) database. These datasets were integrated for batch effect correction and subsequently subjected to differential expression analysis to identify differentially expressed genes (DEGs) between DN and control samples. The identified DEGs were cross-referenced with genes associated with MR to derive MR associated differentially expressed genes (MRRDEGs). These MRRDEGs underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. To identify key genes and develop diagnostic models, four machine learning algorithms were employed in conjunction with weighted gene co-expression network analysis (WGCNA) and the protein interaction tool CytoHubba. Gene set enrichment analysis (GSEA) and CIBERSORT analysis were conducted on the key genes to assess immune cell infiltration in DN. Additionally, a competitive endogenous RNA (ceRNA) network was constructed using the key genes. Finally, the expression levels of core genes in human samples were validated through quantitative real-time PCR (qRT-PCR).ResultsWe identified 256 MRRDEGs, highlighting metabolic and inflammatory pathways in DN. KEGG analysis linked these genes to the MAPK signaling pathway, suggesting its key role in DN. Six key genes were pinpointed using WGCNA, PPI, and machine learning, with their diagnostic value confirmed by ROC analysis. CIBERSORT revealed a strong link between these genes and immune cell infiltration, indicating the immune response’s role in DN. GSEA showed these genes’ involvement in inflammatory and metabolic processes. A ceRNA network was predicted to clarify gene regulation. qRT-PCR confirmed the expression patterns of CXCR2, NAMPT, and CUEDC2, aligning with bioinformatics results.ConclusionThrough bioinformatics analysis, a total of six potential MRRDEGs were identified, among which CUEDC2, NAMPT, CXCR2 could serve as potential biomarkers.

BackgroundSystemic lupus erythematosus (SLE) is a complex autoimmune disease. Current treatments mainly rely on immunosuppressants, which lack specificity and pose challenges during treatment. This study aims to deeply explore the molecular pathogenic mechanism of SLE through gene expression databases (GEO) and bioinformatics analysis methods, combined with Mendelian randomization analysis, to provide key clues for new therapeutic targets.MethodsIn this study, the SLE-related gene chip dataset GSE65391 was selected from the GEO database, and the data were preprocessed and statistically analyzed using R language and bioinformatics tools. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), GO, and KEGG enrichment analysis were used to screen differentially expressed genes (DEGs) for functional annotation and pathway localization. Furthermore, Mendelian randomization analysis was conducted to identify core genes closely related to SLE risk, and immune cell infiltration analysis and compound molecular docking studies were performed on the core gene ISG15.ResultsThe study successfully screened 3,456 DEGs and identified core gene modules highly related to SLE through WGCNA analysis, including key genes closely related to the pathogenesis of SLE, such as STAT1, DDX58, ISG15, IRF7, and IFIH1. In particular, this study found a significant positive correlation between the ISG15 gene and SLE, suggesting that it may be a potential risk factor for SLE. Additionally, through molecular docking technology, it was discovered that the ISG15 gene can effectively bind to two compounds, genistein, and flavopiridol, which have anti-inflammatory and immunosuppressive effects, respectively. This provides new potential drug targets for SLE treatment.DiscussionAs an immunomodulatory cytokine, ISG15 plays a crucial role in the pathogenesis of SLE. This study found that variations in the ISG15 gene may increase the risk of SLE and exacerbate inflammatory responses and tissue damage through multiple mechanisms. Furthermore, molecular docking revealed that genistein and flavopiridol can effectively bind to ISG15, offering a new approach for SLE treatment. These two compounds, with their anti-inflammatory and immunosuppressive properties, have the potential to slow the progression of SLE by influencing the expression and function of ISG15.ConclusionThrough comprehensive bioinformatics analysis and Mendelian randomization analysis, this study deeply explored the molecular pathogenic mechanism of SLE and successfully identified ISG15 as a potential therapeutic target for SLE. Simultaneously, molecular docking technology revealed that two compounds, genistein and flavopiridol, have potential therapeutic effects with ISG15, providing new potential drugs for SLE treatment. These discoveries not only enhance our understanding of the pathogenesis of SLE but also provide important clues for developing new treatment strategies.

C9ORF72 GGGGCC expanded repeats produce splicing dysregulation which correlates with disease severity in amyotrophic lateral sclerosis .This data set is of 45 samples of which 15 are control and 30 are diseased. The genes were chosen based on the P.value. There are both up regulated and down regulated genes.

The GEO Data Portal is the authoritative source for data sets used by UNEP and its partners in the Global Environment Outlook (GEO) report and other integrated environment assessments. The GEO Data Portal gives access to a broad socio-economic data sets from authoritative sources at global, regional, sub-regional and national levels. The contents of the Data Portal cover environmental themes such as climate, forests and freshwater and many others, as well as socioeconomic categories, including education, health, economy, population and environmental policies.

A publicly accessible database containing data on Affymetrix DNA microarray experiments, and Serial Analysis of Gene Expression, mostly on human and mouse stem cell samples and their derivatives to facilitate the discovery of gene functions relevant to stem cell control and differentiation. It has grown in both size and scope into a system with analysis tools that examine either the whole database at once, or slices of data, based on tissue type, cell type or gene of interest. There is currently more than 210 stem cell samples in 60 different experiments, with more being added regularly. The samples were originated by researchers of the Stem Cell Network and processed at the Core Facility of Stemcore Laboratories under the management of Ms. Pearl Campbell in the frame of the Stem Cell Genomics Project. Periodically, new expression data is submitted to the Gene Expression Omnibus (GEO) repository at the National Center for Biotechnological Information, in order to allow researchers to compare the data deposited in StemBase to a large amount of gene expression data sets. StemBase is different from GEO in both focus and scope. StemBase is concerned exclusively with stem cell related data. we are focused in Stem Cell research. We have made a significant effort to ensure the quality and consistency of the data included. This allows us to offer more specialized analysis tools related to Stem Cell data. GEO is intended as a large scale public archive. Deposition in a public repository such as GEO is required by most important scientific journals and it is advantageous for a further diffusion of the data since GEO is more broadly used than StemBase.

The GEO Data Portal is the authoritative source for data sets used by UNEP and its partners in the Global Environment Outlook (GEO) report and other integrated environment assessments. Its online database holds more than 500 different variables, as national, subregional, regional and global statistics or as geospatial data sets (maps), covering themes like Freshwater, Population, Forests, Emissions, Climate, Disasters, Health and GDP. Display them on-the-fly as maps, graphs, data tables or download the data in different formats. By using the GetCapabilities request, one can see the whole list of available data layers.