Gene expression data collected by EST, GeneChip, CAGE, or RNA-seq Each data is named "RefEx_expression_[experimental method][the number of tissue classes]_[species]" in general. e.g. RefEx_expression_EST10_human Nevertheless, some data collected by CAGE are named "RefEx_expression_CAGE_all_[species]". e.g. RefEx_expression_CAGE_all_human

Csv files containing all detectable genes.

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.

The published dataset consists of four sperate datasets:

• BC-TCGA consists of 17,814 genes and 590 samples (including 61 normal tissue samples and 529 breast cancer tissue samples).
• GSE2034 includes 12,634 genes and 286 breast cancer samples (including 107 recurrence tumor samples and 179 no recurrence samples).
• GSE25066 has 492 breast cancer samples available (including 100 pathologic complete response (PCR) samples and 392 residual disease (RD) samples) and 12,634 genes.
• Simulation Data includes 100 positive samples and 100 negative samples with 10,000 features, and each feature in SData follows normal distributions: N(0, 0.1) and N(0 ± r, 0.1) for positive and negative samples, respectively, where r ∈ [−0.125, 0.125].

All of the datasets are used in the experiments in the paper (Comparison among dimensionality reduction techniques based on Random Projection for cancer classification, Xie et al., 2016).

Welcome to the Anopheles gambiae Gene Expression Database at UC Irvine. Presented here is a relational database that combines data from microarray experiments, functional annotation, and the An. gambiae genome project to provide insight into gene expression and regulation in this mosquito vector of human malaria. Microarray analyses included in this site were based on the Affymetrix GeneChip Plasmodium/Anopheles Genome Array. Abundance of specific mRNAs represented in the array were determined for larvae (3rd and 4th instars), adult males (3 days post emergence), non-blood fed females (3 days post emergence) and females at 3, 24, 48, 72, and 96 hours following a blood meal, and females aged 18 days with or without a bloodmeal. Functional annotation integrated into the site for keyword searching combines keywords indexed in the ENSEMBL Mosquito Genome database, NCBI non-redundant databases and conserved motifs databases (GO, PFAM, SMART). Sequence data was captured from the ENSEMBL Mosquito Genome database.

[NOTE: PLEXdb is no longer available online. Oct 2019.] PLEXdb (Plant Expression Database) is a unified gene expression resource for plants and plant pathogens. PLEXdb is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data. PLEXdb (http://www.plexdb.org), in partnership with community databases, supports comparisons of gene expression across multiple plant and pathogen species, promoting individuals and/or consortia to upload genome-scale data sets to contrast them to previously archived data. These analyses facilitate the interpretation of structure, function and regulation of genes in economically important plants. A list of Gene Atlas experiments highlights data sets that give responses across different developmental stages, conditions and tissues. Tools at PLEXdb allow users to perform complex analyses quickly and easily. The Model Genome Interrogator (MGI) tool supports mapping gene lists onto corresponding genes from model plant organisms, including rice and Arabidopsis. MGI predicts homologies, displays gene structures and supporting information for annotated genes and full-length cDNAs. The gene list-processing wizard guides users through PLEXdb functions for creating, analyzing, annotating and managing gene lists. Users can upload their own lists or create them from the output of PLEXdb tools, and then apply diverse higher level analyses, such as ANOVA and clustering. PLEXdb also provides methods for users to track how gene expression changes across many different experiments using the Gene OscilloScope. This tool can identify interesting expression patterns, such as up-regulation under diverse conditions or checking any gene’s suitability as a steady-state control. Resources in this dataset:Resource Title: Website Pointer for Plant Expression Database, Iowa State University. File Name: Web Page, url: https://www.bcb.iastate.edu/plant-expression-database [NOTE: PLEXdb is no longer available online. Oct 2019.] Project description for the Plant Expression Database (PLEXdb) and integrated tools.

RNA-seq gene expression profiles for tissue samples from GTEx

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

Database to retrieve and compare gene expression patterns between animal species. Bgee first maps heterogeneous expression data (currently bulk RNA-Seq, scRNA-Seq, Affymetrix, in situ hybridization, and EST data) to anatomy and development of different species. Bgee is based exclusively on curated healthy wild-type expression data (e.g., no gene knock-out, no treatment, no disease), to provide a comparable reference of gene expression.

Premise of the study: The root apex is an important region involved in environmental sensing, but comprises a very small part of the root. Obtaining root apex transcriptomes is therefore challenging when the samples are limited. The feasibility of using tiny root sections for transcriptome analysis was examined, comparing RNA sequencing (RNA-Seq) to microarrays in characterizing genes that are relevant to spaceflight.Methods:Arabidopsis thaliana Columbia ecotype (Col-0) roots were sectioned into Zone 1 (0.5 mm; root cap and meristematic zone) and Zone 2 (1.5 mm; transition, elongation, and growth-terminating zone). Differential gene expression in each was compared.Results: Both microarrays and RNA-Seq proved applicable to the small samples. A total of 4180 genes were differentially expressed (with fold changes of 2 or greater) between Zone 1 and Zone 2. In addition, 771 unique genes and 19 novel transcriptionally active regions were identified by RNA-Seq that were not detected in microarrays. However, microarrays detected spaceflight-relevant genes that were missed in RNA-Seq. Discussion: Single root tip subsections can be used for transcriptome analysis using either RNA-Seq or microarrays. Both RNA-Seq and microarrays provided novel information. These data suggest that techniques for dealing with small, rare samples from spaceflight can be further enhanced, and that RNA-Seq may miss some spaceflight-relevant changes in gene expression.

Microgravity exposure as well as chronic muscle disuse are two of the main causes of physiological adaptive skeletal muscle atrophy in humans and murine animals in physiological condition. The aim of this study was to investigate at both morphological and global gene expression level skeletal muscle adaptation to microgravity in mouse soleus and extensor digitorum longus (EDL). Adult male mice C57BL/N6 were flown aboard the BION-M1 biosatellite for 30 days on orbit (BF) or housed in a replicate flight habitat on Earth (BG) as reference flight control. In this study we investigated for the first time gene expression adaptation to 30 days of microgravity exposure in mouse soleus and EDL highlighting potential new targets for improvement of countermeasures able to ameliorate or even prevent microgravity-induced atrophy in future spaceflights. Overall Design: C57BL/N6 mice were randomly divided in 3 groups: Bion Flown (BF) mice flown aboard the Bion M1 biosatellite in microgravity environment for 30 days; Bion Ground (BG) mice housed in the same habitat of flown animals but exposed to earth gravity; and Flight Control (FC) mice housed in a standard animal facility.

Accumulating data support the concept that ionizing radiation therapy (RT) has the potential to convert the tumor into an in situ individualized vaccine; however this potential is rarely realized by RT alone. Transforming growth factor xce xb2 (TGF xce xb2) is an immunosuppressive cytokine that is activated by RT and inhibits the antigen-presenting function of dendritic cells and the differentiation of effector CD8+ T cells. Here we tested the hypothesis that TGF xce xb2 hinders the ability of RT to promote anti-tumor immunity. Development of tumor-specific immunity was examined in a pre-clinical model of metastatic breast cancer. Mice bearing established 4T1 mouse mammary carcinoma treated with pan-isoform specific TGF xce xb2 neutralizing antibody 1D11 showed significantly improved control of the irradiated tumor and non-irradiated metastases but no effect in the absence of RT. Notably whole tumor transcriptional analysis demonstrated the selective upregulation of genes associated with immune-mediated rejection only in tumors of mice treated with RT+TGF xce xb2 blockade. Mice treated with RT+TGF xce xb2 blockade exhibited cross-priming of CD8+ T cells producing IFN xce xb3 in response to three tumor-specific antigens in tumor-draining lymph nodes which was not evident for single modality treatment. Analysis of the immune infiltrate in mouse tumors showed a significant increase in CD4+ and CD8+ T cells only in mice treated with the combination of RT+TGF xce xb2 blockade. Depletion of CD4+ or CD8+ T cells abrogated the therapeutic benefit of RT+TGF xce xb2 blockade. These data identify TGF xce xb2 as a master inhibitor of the ability of RT to generate an in situ tumor vaccine which supports testing inhibition of TGF xce xb2 during radiotherapy to promote therapeutically effective anti-tumor immunity. We used genome-wide microarray to depict main biological processes responsibles for the therapeutic benefit of the combination ofTGF-beta blockade and local radiotherapy. To gain a more comprehensice protrait of the effects of RT and TGFbeta blockade on gene expressionin tumors we collected 4T1 tumors 4 days after completion of RT. Three tumors from each group were then subjected to RNA extraction and hybridization on affymetrix array.

Background Microarray technologies are emerging as a promising tool for genomic studies. The challenge now is how to analyze the resulting large amounts of data. Clustering techniques have been widely applied in analyzing microarray gene-expression data. However, normal mixture model-based cluster analysis has not been widely used for such data, although it has a solid probabilistic foundation. Here, we introduce and illustrate its use in detecting differentially expressed genes. In particular, we do not cluster gene-expression patterns but a summary statistic, the t-statistic. Results The method is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle-ear infection. Three clusters were found, two of which contain more than 95% genes with almost no altered gene-expression levels, whereas the third one has 30 genes with more or less differential gene-expression levels. Conclusions Our results indicate that model-based clustering of t-statistics (and possibly other summary statistics) can be a useful statistical tool to exploit differential gene expression for microarray data.

Gene expression datasets.

This data package contains expression profiles for proteins in normal and cancer tissues. It also contains data on sequence based RNA levels in human tissue and cell line.

We assembled a compendium of 30 primary HNC gene expression datasets with accompanying clinical data, representing the largest such resource for HNC. This resource was specifically built to identify genes associated with two outcome variables: patient survival and lymph node metastasis (LNM) status. Meta-analyses were applied to uniformly preprocessed gene expression data, as in our PRECOG resource (Gentles et al, Nat Med, 2016). Briefly, datasets were quality controlled, normalized, log transformed, and standardized to calculate gene expression profiles. Clinical data were manually curated and included survival and LNM status as well as variables relevant to HNC prognosis, such as tumor grade, tumor subanatomic location, and HPV status. The resulting 30 cleaned studies included 2,134 HNC tumors. 1,666 patients (across 17 cohorts) had survival outcome data and 1,490 patients (21 cohorts) had LNM status. Fully processed datasets are provided here as a resource to enable efficient meta-analyses of gene expression data in head and neck cancer.

a-e Different superscripts within a row indicate significant difference (P<0.05).The highest expression value for each gene is highlighted in bold font. A/O: ratio between average expression of certain gene in tissue with specific expression of that gene and expression in other tissues. EM: Extracellular matrix; ER: Endoplasmic reticulum.Gene expression values of selected genes.

The global gene expression analysis market is anticipated to reach a valuation of 2121.6 million USD by 2033, expanding at a CAGR of 5.2%. Key drivers fueling market growth include the rising prevalence of chronic diseases, advancements in molecular biology techniques, and increasing investments in research and development. The emergence of personalized medicine and the growing demand for accurate disease diagnostics are further bolstering market demand. Segment-wise, the research application holds a dominant share in the market due to the extensive use of gene expression analysis in basic scientific research and drug discovery. Furthermore, the DNA microarray segment accounts for a significant market share owing to its cost-effectiveness and well-established protocols. Regionally, North America leads the market, followed by Europe and Asia Pacific. The presence of major industry players, robust research infrastructure, and favorable government funding contribute to the region's dominance. Growing awareness of personalized medicine in emerging economies is expected to drive market growth in the Asia Pacific region.

This dataset contains the terms of the vocabulary used in the Comparative Toxicogenomics Database (CTD) to describe the activity of genes inferred to have an interaction with a chemical or disease. The dataset contains different types of standardized identifications for the gene to provide a cross-platform compatibility making able to identify the gene and its characteristics in major scientific databases.

Most of the research studies developed applying microarray technology to the characterization of different pathological states of any disease may fail in reaching statistically significant results. This is largely due to the small repertoire of analysed samples, and to the limitation in the number of states or pathologies usually addressed. Moreover, the influence of potential deviations on the gene expression quantification is usually disregarded. In spite of the continuous changes in omic sciences, reflected for instance in the emergence of new Next-Generation Sequencing-related technologies, the existing availability of a vast amount of gene expression microarray datasets should be properly exploited. Therefore, this work proposes a novel methodological approach involving the integration of several heterogeneous skin cancer series, and a later multiclass classifier design. This approach is thus a way to provide the clinicians with an intelligent diagnosis support tool based on the use of a robust set of selected biomarkers, which simultaneously distinguishes among different cancer-related skin states. To achieve this, a multi-platform combination of microarray datasets from Affymetrix and Illumina manufacturers was carried out. This integration is expected to strengthen the statistical robustness of the study as well as the finding of highly-reliable skin cancer biomarkers. Specifically, the designed operation pipeline has allowed the identification of a small subset of 17 differentially expressed genes (DEGs) from which to distinguish among 7 involved skin states. These genes were obtained from the assessment of a number of potential batch effects on the gene expression data. The biological interpretation of these genes was inspected in the specific literature to understand their underlying information in relation to skin cancer. Finally, in order to assess their possible effectiveness in cancer diagnosis, a cross-validation Support Vector Machines (SVM)-based classification including feature ranking was performed. The accuracy attained exceeded the 92% in overall recognition of the 7 different cancer-related skin states. The proposed integration scheme is expected to allow the co-integration with other state-of-the-art technologies such as RNA-seq.