Community database that collects and integrates the gene expression information in MGI with a primary emphasis on endogenous gene expression during mouse development. The data in GXD are obtained from the literature, from individual laboratories, and from large-scale data providers. All data are annotated and reviewed by GXD curators. GXD stores and integrates different types of expression data (RNA in situ hybridization; Immunohistochemistry; in situ reporter (knock in); RT-PCR; Northern and Western blots; and RNase and Nuclease s1 protection assays) and makes these data freely available in formats appropriate for comprehensive analysis. There is particular emphasis on endogenous gene expression during mouse development. GXD also maintains an index of the literature examining gene expression in the embryonic mouse. It is comprehensive and up-to-date, containing all pertinent journal articles from 1993 to the present and articles from major developmental journals from 1990 to the present. GXD stores primary data from different types of expression assays and by integrating these data, as data accumulate, GXD provides increasingly complete information about the expression profiles of transcripts and proteins in different mouse strains and mutants. GXD describes expression patterns using an extensive, hierarchically-structured dictionary of anatomical terms. In this way, expression results from assays with differing spatial resolution are recorded in a standardized and integrated manner and expression patterns can be queried at different levels of detail. The records are complemented with digitized images of the original expression data. The Anatomical Dictionary for Mouse Development has been developed by our Edinburgh colleagues, as part of the joint Mouse Gene Expression Information Resource project. GXD places the gene expression data in the larger biological context by establishing and maintaining interconnections with many other resources. Integration with MGD enables a combined analysis of genotype, sequence, expression, and phenotype data. Links to PubMed, Online Mendelian Inheritance in Man (OMIM), sequence databases, and databases from other species further enhance the utility of GXD. GXD accepts both published and unpublished data.

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.

[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.

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.

This resource of the Human Protein Atlas focuses on the expression profiles in human tissues of genes both on the mRNA and protein level. The protein expression data from 44 normal human tissue types is derived from antibody-based protein profiling using conventional and multiplex immunohistochemistry. All underlying images of immunohistochemistry stained normal tissues are available together with knowledge-based annotation of protein expression levels. The protein data covers 15302 genes (76%) for which there are available antibodies. The mRNA expression data is derived from deep sequencing of RNA (RNA-seq) from 40 different normal tissue types. More information about the specific content and the generation and analysis of the data in the resource can be found on the Methods Summary. Learn about:

protein localization in tissues at a single-cell level if a gene is enriched in a particular tissue (specificity) which genes have a similar expression profile across tissues (expression cluster)

THIS RESOURCE IS NO LONGER IN SERVICE, documented on October 30, 2012. A database that displays the observed frequencies of individual 5' end SAGE tags and previously unknown transcription start sites in the promoter regions, introns and intergenic regions of known genes. 5'SAGE will be useful for analyzing promoter regions and start site variation in different tissues, and is freely available.

A high-quality, barley gene reference transcript dataset (BaRTv1.0, Rapazote-Flores et al. 2019), was used to quantify gene and transcript abundances from 22 RNA-seq experiments, covering 843 separate samples. Using the abundance data we developed a Barley Expression Database (EoRNA* – Expression of RNA) to underpin a visualisation tool that displays comparative gene and transcript abundance data on demand as transcripts per million (TPM) across all samples and all the genes. EoRNA provides gene and transcript models for all of the transcripts contained in BaRTV1.0, and these can be conveniently identified through either BaRT or HORVU gene names, or by direct BLAST of query sequences. Browsing the quantification data reveals cultivar, tissue and condition specific gene expression and shows changes in the proportions of individual transcripts that have arisen via alternative splicing. TPM values can be easily extracted to allow users to determine the statistical significance of observed transcript abundance variation among samples or perform meta analyses on multiple RNA-seq experiments. * Eòrna is the Scottish Gaelic word for Barley

This resource provides comprehensive spatial profiling of the Brain, including overview of protein expression in the mammalian brain based on integration of data from human, pig and mouse. Transcriptomics data combined with affinity-based protein in situ localization down to single cell detail is available in this brain-centric sub atlas of the Human Protein Atlas. The data presented are for human genes and their one-to-one orthologues in pig and mouse. Gene summary pages provide the hierarchical expression landscape form 13 main regions of the brain to individual nuclei and subfields for every protein coding gene. For selected proteins, high content images are available to explore the cellular and subcellular protein distribution. In addition, the Brain resource contains lists of genes with elevated expression in one or a group of regions to help the user identify unique protein expression profiles linked to physiology and function. More information about the specific content and the generation and analysis of the data in this resource can be found on the Methods Summary. Learn about:

Expression levels for all human proteins in regions and subregions of the human brain Expression levels for all proteins with human orthologs in regions and subregions of the pig and mouse brain Brain enriched genes with higher expression in any of the regions of the brain compared to peripheral organs Regional enriched genes with higher expression in a single or few regions of the brain Cell-type and cell-compartment distribution of selected proteins in the human and mouse brain Differences in gene expression between mammalian species

Additional information: In addition to the data provided in the brain resource there is also data on human retina and single cell data containing information on protein expression in human neuronal and non-neuronal cell-types in the central nervous system.

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.

TCGA RNA-seq V2 Level3 data were downloaded from TCGA Genomic Data Commons Data Portal (https://gdc-portal.nci.nih.gov), consisting of 11,303 samples in 34 cancer projects (33 cancer types). Nine cancer types that do not have corresponding non-tumour samples were filtered out, and the analysis was focused on tumour versus non-tumour comparison. 24 cancer types were used in this meta-analysis: BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, THCA, THYM, UCEC (https://gdc-portal.nci.nih.gov). The nine filtered cancer types were ACC, DLBC, LAML, LGG, MESO, OV, TGCT, UCS and UVM. To extract expression values from TCGA RNA-seq data, we used genomic coordinates to retrieve UCSC Transcript IDs that correspond to the identifiers in TCGA RNA-seq V2 Level3 data (isoform level). The GAF (General Annotation Format) file was used to map the coordinate to UCSC Transcript ID, and it was downloaded form https://tcga-data.nci.nih.gov/docs/GAF/GAF.hg19.June2011.bundle/outputs/TCGA.hg19.June2011.gaf. This file contains genomic annotations shared by all TCGA projects. More details of the GAF file format can be found at https://tcga-data.nci.nih.gov/docs/GAF/GAF3.0/GAF_v3_file_description.docx. We filtered out any coding exons overlapping UCSC Transcript IDs to eliminate expression value of coding genes and evaluate lncRNA expression.We could find the expression values of 443 pcRNAs and 203 tapRNAs in TCGA data, as many of non-coding regions are not yet fully annotated in the TCGA RNA-seq V2 Level3 data. The expression value of pcRNAs and tapRNAs were extracted and clustered by un-supervised Pearson correlation method (Supplementary Figure 18A). The expression values of tapRNA-associated coding genes were also extracted and used to generate the heat-map (Supplementary Figure 18B), which shows the similar pattern of expression with tapRNAs across the cancer types.To show that tapRNAs and associated coding genes have similar expression profiles in cancers we generated a Spearman's Rank-Order Correlation heatmap (Figure 6A) between tapRNAs and their associated coding genes based on the TCGA RNA-seq data. We used the MatLab function corr to calculate the Spearman's rho. This function takes two matrices X (197-by-8,850 expression profiling matrix of tapRNA) and Y (197-by-8,850 expression profiling matrix of tapRNA-assocated coding gene) and returns an 8,850-by-8,850 matrix containing the pairwise correlation coefficient between each pair of 8,850 columns (TCGA cancer samples in Supplementary Figure 18A and B). Thus, the rank-order correlation matrix that we computed from the matrices of expression profiling data (Supplementary Figure S18A and B) allowed us to compare the correlation between two column vectors i.e. cancer samples. This function also returns a matrix of p-values for testing the hypothesis of no correlation against the alternative that there is a nonzero correlation. Each element of a matrix of p-values is the p value for the corresponding element of Spearman's rho. The p-values for Spearman's rho are calculated using large-sample approximations. To check significance level of correlation between tapRNA and its associated coding gene, the diagonal of the p-value matrix was extracted and used. The median is 1.31x10-11 and the mean is 1.03x10-4 with standard deviation 0.0029.To identify cancer-specific tapRNAs, we considered not only the global expression pattern of a given tapRNA in each cancer type, but also expression pattern of specific sub-group that is significantly distinct, to take into account cancer sample heterogeneity. Thus, two conditions were applied: (1) average expression level of a tapRNA in a given cancer type is in top 10% or bottom 10% and (2) a tapRNA has at least 10% of samples in a given cancer type that are significantly up-regulated (Z-score > 2) or down-regulated (Z-score < -2).

THIS RESOURCE IS NO LONGER IN SERVICE, documented on March 19, 2012. Due to budgetary constraints, the National Center for Biotechnology Information (NCBI) has discontinued support for the NCBI GENSAT database, and it has been removed from the Entrez System. The Gene Expression Nervous System Atlas (GENSAT) project involves the large-scale creation of transgenic mouse lines expressing green fluorescent protein (GFP) reporter or Cre recombinase under control of the BAC promoter in specific neural and glial cell populations. BAC expression data for all the lines generated (over 1300 lines) are available in online, searchable databases (www.gensat.org and the Database of GENSAT BAC-Cre driver lines). If you have any specific questions, please feel free to contact us at info_at_ncbi.nlm.nih.gov The GENSAT project aims to map the expression of genes in the central nervous system of the mouse, using both in situ hybridization and transgenic mouse techniques. Search criteria include gene names, gene symbols, gene aliases and synonyms, mouse ages, and imaging protocols. Mouse ages are restricted to E10.5 (embryonic day 10.5), E15.5 (embryonic day 15.5), P7 (postnatal day 7), and Adult (adult). The project focuses on two techniques * Evaluation of unmodified mice lines for expression of a given gene using radiolabelled riboprobes and in-situ hybridization. * Creation of transgenic mice lines containing a BAC construct that expresses a marker gene in the same environment as the native gene

Database of long noncoding RNA expression that integrates annotated expression data from various sources in human and mouse. The database contains both microarray and in situ hybridization data, and supplies a rich tapestry of ancillary information for featured ncRNAs, including evolutionary conservation, secondary structure evidence, genomic context links and antisense relationships.

Gene expression database system in compliance with MIAME, which is a standard that the MGED Society has developed for comparing and data produced in microarray experiments at different laboratories worldwide. It serves as a public repository for a wide range of high-throughput experimental data in gene expression research, including microarray-based experiments measuring mRNA, serial analysis of gene expression (SAGE tags), and mass spectrometry proteomic data.

Single-cell RNA-seq studies profile thousands of cells in developmental processes. Current databases for human single-cell expression atlas only provide search and visualize functions for a selected gene in specific cell types or subpopulations. These databases are limited to technical properties or visualization of single-cell RNA-seq data without considering the biological relations of their collected cell groups. Here, we developed a database to investigate single-cell gene expression profiling during different developmental pathways (SCDevDB). In this database, we collected 10 human single-cell RNA-seq datasets, split these datasets into 176 developmental cell groups, and constructed 24 different developmental pathways. SCDevDB allows users to search the expression profiles of the interested genes across different developmental pathways. It also provides lists of differentially expressed genes during each developmental pathway, T-distributed stochastic neighbor embedding maps showing the relationships between developmental stages based on these differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis results of these differentially expressed genes. This database is freely available at https://scdevdb.deepomics.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. rOGED is a searchable database containing ovarian gene sequences, data, and images of ovarian genes expressed in rats. The site also provides customized gene expression profiles in the rat oviduct and uterus or mouse ovary. The database was constructed from total RNA isolated from intact ovaries, granulosa cells, or residual ovarian tissues collected from immature PMSG/hCG-treated rats at 0 (no PMSG), 12, and 48 h post-PMSG, as well as 6 and 12 h post-hCG. The total RNA was used for DNA microarray analysis using Affymetrix Rat Expression Arrays 230A and B. The microarray data was compiled and utilized for display of individual gene expression profiles through specially developed software. The final rOGED provides immediate analysis of temporal gene expression profiles for over 28,000 genes in intact ovaries, granulosa cells, and residual ovarian tissue during follicular growth and the preovulatory period. The accuracy of the rOGED was validated against the gene profiles for over 100 known genes. The utility of the rOGED was demonstrated by identifying seven genes which have not been described in the rat periovulatory ovary. The rOGED can be used as an instant reference for ovarian gene expression profiles, as well as reliable resource for identifying important yet, to date, unknown ovarian genes. Category: Microarray Data and other Gene Expression Databases

This dataset contains all the Seurat objects that were used for generating all the figures in Pal et al. 2021 (https://doi.org/10.15252/embj.2020107333). All the Seurat objects were created under R v3.6.1 using the Seurat package v3.1.1. The detailed information of each object is listed in a table in Chen et al. 2021.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 08, 2011. This database contains gene expression data for various physiological and pathological processes in mouse brain. All the data have been obtained by adaptor-tagged competitive PCR, an advanced version of quantitative PCR. Brain Gene Expression Database (BGED) contains gene expression data for various physiological and pathological processes in mouse brain. All the data have been obtained by adaptor-tagged competitive PCR, an advanced version of quantitative PCR. Manual Download 1. Data retrieval Gene expression data can be retrieved either by ID numbers or by keywords representing functional annotations from this page. The ID numbers include GenBank, RefSeq, SwissProt, Gene Ontology, and BED (our own ID). The keyword search is based either on definition in GenBank, SwissProt and RefSeq, functional annotation of SwissProt database, or Gene Ontology terms. 2. Gene expression pattern display * Display of multiple gene expression patterns. Expression patterns of multiple genes selected by the keyword search can be displayed from the result page of the keyword search. * Gene expression pattern similarity search This function is available on the information page of each gene accessed through BED ID (in-house ID).

Results of transcript sequencing for AtT-20FlpIn cells. mRNA was isolated from AtT-20FlpIn cells using standard procedures, next generation sequencing was performed by Macrogen (https://dna.macrogen.com/). A report ourtlining the workflow and data analysis methods is available from the Authors by request.

Deposited data is in an Excel file, which includes the gene symbol, transcript ID from the reference mouse genome, protein ID and transcript abundance. The AtT-20FlpIn cells were generated by Dr Santiago, and have been used as the 'wild type' cells for generating cell lines stably expressing GPCR and ion channels for most of the molecular pharmacology projects in the Molecular Pharmacodynamics group.

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.

This compound data set comprises the following information from the The Cancer Genome Atlas:

• RNA-Seq counts for 60483 genes across 11093 samples
• HuEx 1.0 ST gene expression data for 18632 genes across 1211 samples
• clinical indicators for 11160 patients

All gene expression data is annotated across ENSEMBL, ENTREZ and symbols. Samples are annotated by TCGA barcodes.

To read the data set into R (requires 6 GB of RAM) use:

tcga <- readRDS("tcga.rds")