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.

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

Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.

GEO (Gene Expression Omnibus) is a public functional genomics data repository supporting MIAME-compliant data submissions. There are also tools provided to help users query and download experiments and curated gene expression profiles.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan–Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

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

Summary Since 2017, GEO shares have fallen sharply from $30 to ~$8.50 per share, at one point below even the book value of $8.19 per share. President Biden recently signed an executive order that banned the renewal of Department of Justice contracts with private prisons, but the effect on GEO is way way less than the market thinks. The border crisis renders ICE dependent on GEO for capacity, making it near impossible for ICE to cut ties in the near future. With a market cap of just $1.02 Billion, GEO has the potential to increase 2-3x in the next 6-12 months. cropped image of african american prisoner reading book LightFieldStudios/iStock via Getty Images Thesis GEO Group (GEO) is a deeply mispriced provider of privately-owned prisons, falling from a price of $30+ in early 2017 to the current price of $8.50 per share. GEO has fallen primarily as a result of concerns about legislation regarding private prisons, a canceled dividend, the likely shift away from a REIT structure, and high levels of debt. These overblown concerns have created a pretty solid structural opportunity. kmosby1992@gmail.com password kmosby1992@gmail.com Subscribe Company overview GEO operates in several segments, such as GEO care, International services, and U.S. Secure Services. Source: Annual report 1 - U.S. Secure Services U.S. Secure services account for the majority of their revenue, 67%, and includes their correctional facilities and processing centers. Secure services manage 74,000 beds across 58 facilities as of the 2020 annual report. GEO transport is included in U.S. secure services, but we felt it warranted its own paragraph. GEO transport provides secure transportation services to government agencies. With 400 customized, U.S. Department of Transportation compliant vehicles, GEO transport drove more than 14 million miles in 2020. 2 - GEO Care GEO care is a series of programs designed to reintegrate inmates and troubled youth into society. They operate through reentry centers, non-residential reentry programs, and youth treatment programs. GEO care operates approximately 4-dozen reentry centers, which provide housing, employment assistance, rehabilitation, substance abuse counseling, and vocational and education programs to current and former inmates. Through their reentry segment, they operate more than 70 non-residential reentry programs that provide behavioral assessments, treatment, supervision, and education. GEO care made up 23% of total 2020 revenue. Geo monitoring is included in GEO care. Through a wholly-owned subsidiary, BI Inc., GEO offers monitoring technology for parolees, probationers, pretrial defendants, and individuals involved in the immigration process. As of the 2020 annual report, BI helps monitor ~155,000 individuals across all 50 states. 3 - International operations International operations made up only 10% of revenue in 2020, but it is showing signs of growth. GEO recently landed a 10-year contract with the United kingdom, which they expect to total $760 million in revenue over the course of the contract. They also landed an 8-year contract with the Scottish Prison Service, which grants an annualized revenue of $39 million and has a 4-year renewal period. Why is GEO Mispriced? While there are several reasons for the dramatic reduction in share price over the last 4 years, the main reason was the looming fear of legislation destroying privately owned prisons. To a degree, this fear materialized on January 26th, 2021, when President Biden signed an Executive Order ordering the Attorney General not to renew any Department of Justice contracts with "privately operated criminal detention facilities." At face value, this order seems as though it would have a devastating impact on GEO. However, only ~25% of total revenue is impacted in any form by this order. The executive order only concerns branches of the Department of Justice. Only 2 DOJ branches have business connections with GEO, the US Marshals (USMS), and the Bureau of Prisons (BOP). Source: Annual report It is imperative to note that Immigration and Customs Enforcement (ICE), is not a branch of the DOJ and is therefore unaffected by this order. Individual states, as well as other countries, are unaffected by this order Bureau of Prisons GEO currently holds several agreements with the BOP relating to operations of prisons across the country. As of year-end 2020, agreements involving the BOP accounted for 14% of total revenue. All revenue from the BOP will not disappear, as the executive order does not impact reentry facilities. In 2Q21, after the executive order was made, GEO renewed 5 BOP reentry contracts. GEO even scored a new contract with the BOP, regarding the construction and operation of a new facility in Tampa. United States Marshal Service The United States Marshal Service does not own o... Visit https://dataone.org/datasets/sha256%3A900514e651e0d2c774ad90f358c9db90884c2baf98c068f470b290b3c4b3103a for complete metadata about this dataset.

All the processed gene expression profiles available from GEO database and R codes for scRNA-seq analysis or BayesPrism analysis have been deposited in the figshare platform.

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

The data used in this study comprise the GSE5281 and GSE48350 datasets in the Geo Database, m6A-associated genes in the genecard database. Through R software to extract the effective data, get the difference analysis, enrichment analysis, gene expression and other data for this study for the follow-up study.

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.

We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.

All uploaded files are UTF-8, .csv-formatted matrices. The *_expected_count.csv.gz files are unlogged, raw expression counts as reported by rsem-quantify-expression (see details below). The associated *_metadata.csv.gz files contain metadata pertinent to each column of the corresponding expression matrix.Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).

Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.

Each recompute has at least the gene_id column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.Each associated metadata has at least the following columns:

geo_accession: The GEO sample ID of the sample.

ena_sample: The ENA sample ID of the sample.

ena_run: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.

The remaining columns are derived from GEO metadata files and other ENA-provided data. Please refer to the x.FASTQ package for more information.

Pipeline Details

The alignment and quantification was made with the x.FASTQ tool available on Github installed locally on an Arch Linux machine on commit 3a93dd77a70df59c74f7b15216c26f12cd918e81 running the Linux 6.7.8-zen1-1-zen kernel with a 11th Gen Intel i7-1185G7 (8) CPU and a Intel TigerLake-LP GT2 [Iris Xe Graphics] GPU. Please note that no sample filtering or omissions were done based on sample quality or sequencing depth. However, sensible trimming (e.g. low-quality bases and common adapters) was performed on all the samples.

Reference genome was downloaded from Ensembl, version hg38. STAR was used to create the index genome with overhang set to 149.

Network topological parameters from gene expression data from GEO dataset for adult and paediatric patient.

aThe Tabchy-TFAC data set (GSE20271) has 31 samples that overlap with the Iwamoto data set (GSE22093); therefore, these two data sets are not completely independent.

GEO data set......................................

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.

ibm-aimc/geo-data-1b dataset hosted on Hugging Face and contributed by the HF Datasets community

Summary

This metadata record provides details of the data supporting the claims of the related manuscript: “FOXA1 and adaptive response determinants to HER2 targeted therapy in TBCRC 036”.

The related study aimed to determine the global alterations in gene enhancers and transcriptional changes to identify factors involved in the adaptive response to HER2 inhibition. In parallel, it analysed the in vivo human adaptive molecular responses to HER2 targeting in a window-of-opportunity clinical trial using both RNAseq and a chemical proteomics method (MIB/MS) to assess the functional kinome.

Type of data: mass spectrometry proteomics data; normalised patient RNA sequencing data; cell line RNA sequencing data; cell line ChIPseq data

Subject of data: Homo sapiens; Eukaryotic cell lines

Recruitment: Eligible women included those with newly diagnosed Stage I-IV HER2+ breast cancer scheduled to undergo definitive surgery (either lumpectomy or mastectomy). Stage I-IIIc patients could not be candidates for a therapeutic neoadjuvant treatment. Study subjects provided informed written consent that included details of the nontherapeutic nature of the trial.

Trial registration number: https://clinicaltrials.gov/ct2/show/NCT01875666

Data access

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier https://identifiers.org/pride.project:PXD021865.

Normalized patient RNAseq data (https://identifiers.org/geo:GSE161743), cell line RNAseq (https://identifiers.org/geo:GSE160001 and https://identifiers.org/geo:GSE160001), and cell line ChIPseq (https://identifiers.org/geo:GSE160667) are all part of the SuperSeries https://identifiers.org/geo:GSE160670 available through the Gene Expression Omnibus.

Processed and normalized data are provided as supplemental materials associated with the article on the journal website, and also attached to this data record in the Excel spreadsheets called Supplementary Data 1-10 and the PDF called Supplementary material file.PDF. Accompanying Supplementary Information and Supplementary Data files contain relevant data used to produce the included figures and are available with this article. A detailed list of which data files underlie which figures and tables in the related article is included in the file ‘Angus_et_al_2021_underlying_data_files_list.xlsx’, which is shared with this data record.

The data supporting Figure 3c is in the GraphPad Prism file called ‘siGrowth’, which is not shared publicly as it is in a non-open format, but it can be made available upon reasonable request to the corresponding author.

Corresponding author(s) for this study

Gary L. Johnson, PhD, Department of Pharmacology, 4079 Genetic Medicine Building, University of North Carolina School of Medicine, Chapel Hill, NC 27599. Email: glj@med.unc.edu. Phone: 919-843-3106.

Study approval

Approved by the UNC Office of Human Research Ethics and conducted in accordance with the Declaration of Helsinki. IRB# 13-1826

LENGTH (LinkEd opeN Geo daTa Hub) serves as platform for linked open geo datasets and related resources providing relevant information with aim to support knowledge sharing and further use and re-use.

Geo data with the support of the semantic technology can serve as medium to visualize and link various phenomena and activities in space across many domains.

This website have been developed with the support of the SmartOpenData FP7 project .

An observed FPR based on all of 35203 genes is computed given a |logFC| cutoff in parenthesis.