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.

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.

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

This is the GitHub repository for the single cell RNA sequencing data analysis for the human manuscript. The following essential libraries are required for script execution: Seurat scReportoire ggplot2 dplyr ggridges ggrepel ComplexHeatmap Linked File: -------------------------------------- This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. Provided below are descriptions of the linked datasets: 1. Gene Expression Omnibus (GEO) ID: GSE229626 - Title: Gene expression profile at single cell level of human T cells stimulated via antibodies against the T Cell Receptor (TCR) - Description: This submission contains the matrix.mtx, barcodes.tsv, and genes.tsv files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. - Submission type: Private. In order to gain access to the repository, you must use a "reviewer token"(https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html). 2. Sequence read archive (SRA) repository - Title: Gene expression profile at single cell level of human T cells stimulated via antibodies against the T Cell Receptor (TCR) - Description: This submission contains the "raw sequencing" or .fastq.gz files, which are tab delimited text files. - Submission type: Private. In order to gain access to the repository, you must use a "reviewer token" (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html). Please note that since the GSE submission is private, the raw data deposited at SRA may not be accessible until the embargo on GSE229626 has been lifted. Installation and Instructions -------------------------------------- The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation: > Ensure you have R version 4.1.2 or higher for compatibility. > Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code. The following code can be used to set working directory in R: > setwd(directory) Steps: 1. Download the "Human_code_April2023.R" and "Install_Packages.R" R scripts, and the processed data from GSE229626. 2. Open "R-Studios"(https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R. 3. Set your working directory to where the following files are located: - Human_code_April2023.R - Install_Packages.R 4. Open the file titled Install_Packages.R and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies. 5. Open the Human_code_April2023.R R script and execute commands as necessary.

This proposal addressed the theme of “impact of oil spills on public health†. Specifically, the proposal addressed the general hypothesis, which is: upon oil/dispersant respiratory exposure there will be a higher carcinogenic potential of lung tissue.

To test this hypothesis, we profiled and confirmed the existence of molecular signatures of carcinogenesis through RNA-seq analysis of a mouse model treated with instilled oil/dispersants. We exposed the wild-type C57BL/6 (B6) mice to BP crude oil, dispersant 9500, dispersant 9527, oil + 9527, oil + 9500 and H2O (as control) using intratracheal instillation method for 2 weeks. We then performed RNA-seq analysis of the lung tissue from the mice to identify differentially expressed (DEx) genes (DEGs) in the treated mice vs. the control mice. These DEGs were functionally annotated to search for GO terms and pathways related to carcinogenesis.

For each treatment group, 3 male and 3 female mice were used. Therefore, we generated RNA-seq data for a total of 36 animals (6 animals/group x 6 treatment group).

We have submitted the RNA-seq data to NCBI's GEO (Gene Expression Omnibus) online database (https://www.ncbi.nlm.nih.gov/geo/). The dataset is now assigned a GEO series number GSE137204.

In the GEO website, RNA-seq data are organized under three types: the metadata, the processed data and the raw data files. The metadata describes the treatment group and other information related to a sample. The processed data files contain raw counts of sequencing reads for transcripts. The raw data files are the raw fastq data files generated in the RNA-seq experiments.

This dataset supports the publication: Liu, Yao-Zhong; Charles A. Miller; Yan Zhuang; Sudurika S. Mukhopadhyay; Shigeki Saito; Edward B. Overton; and Gilbert F. Morris. 2020. The Impact of the Deepwater Horizon Oil Spill upon Lung Health—Mouse Model-Based RNA-Seq Analyses. International Journal of Environmental Research and Public Health, 17(15), 5466. doi:10.3390/ijerph17155466

[NOTICE: This data set has been deprecated. Please see our new version of the data (and additional data sets) here: https://osf.io/mhk93 ]"A causal role of mutations in genes encoding for multiple general transcription factors in neurodevelopmental disorders including autism suggested that alterations at the global level of gene expression regulation might also relate to disease risk in sporadic cases of autism. This premise can be tested by evaluating for global changes in the overall distribution of gene expression levels. For instance, in mice, we recently showed that variability in hippocampal-dependent behaviors was associated with variability in the pattern of the overall distribution of gene expression levels, as assessed by variance in the distribution of gene expression levels in the hippocampus. We hypothesized that a similar change in the variance in gene expression levels might be found in children with autism. Gene expression microarrays covering greater than 47,000 unique RNA transcripts were done on purified RNA from peripheral blood lymphocytes of children with autism (n=82) and controls (n=64). The variance in the distribution of gene expression levels from each microarray was compared between groups of children. Also tested was whether a risk factor for autism, increased paternal age, was associated with variance in the overall distribution of gene expression levels. A decrease in the variance in the distribution of gene expression levels in peripheral blood lymphocytes (PBL) was associated with the diagnosis of autism and a risk factor for autism, increased paternal age. Traditional approaches to microarray analysis of gene expression suggested a possible mechanism for decreased variance in gene expression. Gene expression pathways involved in transcriptional regulation were down-regulated in the blood of children with autism and children of older fathers. Thus, results from global and gene specific approaches to studying microarray data were complimentary and supported the hypothesis that alterations at the global level of gene expression regulation are related to autism and increased paternal age. Regulation of transcription, thus, represents a possible point of convergence for multiple etiologies of autism and other neurodevelopmental disorders."http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25507We have included gene-expression data, the outcome (class) being predicted, and any clinical covariates. When gene-expression data were processed in multiple batches, we have provided batch information. Each data set is organized into a file set, where each contains all pertinent files for an individual dataset. The gene expression files have been normalized using both the SCAN and UPC methods using the SCAN.UPC package in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html). We summarized the data at the gene level using the BrainArray resource (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/20.0.0/ensg.asp). We used Ensembl identifiers. The class, clinical, and batch data were hand curated to ensure consistency ("tidy data" formatting). In addition, the data files have been formatted to be imported easily into the ML-Flex machine learning package (http://mlflex.sourceforge.net/).

1Pooled LPS-challenged hosts: MOS+VIRG (antibiotic) groups; The complete raw data have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/projects/geo (accession no. GSE28959).2+: up-regulated genes by LPS; −: down-regulated genes by LPS.

Table of Contents

Main Description File Descriptions Linked Files Installation and Instructions

1. Main Description

This is the Zenodo repository for the manuscript titled "A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.". The code included in the file titled marengo_code_for_paper_jan_2023.R was used to generate the figures from the single-cell RNA sequencing data. The following libraries are required for script execution:

Seurat scReportoire ggplot2 stringr dplyr ggridges ggrepel ComplexHeatmap

File Descriptions

The code can be downloaded and opened in RStudios. The "marengo_code_for_paper_jan_2023.R" contains all the code needed to reproduce the figues in the paper The "Marengo_newID_March242023.rds" file is available at the following address: https://zenodo.org/badge/DOI/10.5281/zenodo.7566113.svg (Zenodo DOI: 10.5281/zenodo.7566113). The "all_res_deg_for_heat_updated_march2023.txt" file contains the unfiltered results from DGE anlaysis, also used to create the heatmap with DGE and volcano plots. The "genes_for_heatmap_fig5F.xlsx" contains the genes included in the heatmap in figure 5F.

Linked Files

This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. The "Rdata" or "Rds" file was deposited in Zenodo. Provided below are descriptions of the linked datasets:

Gene Expression Omnibus (GEO) ID: GSE223311(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223311)

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the "matrix.mtx", "barcodes.tsv", and "genes.tsv" files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Sequence read archive (SRA) repository ID: SRX19088718 and SRX19088719

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the raw sequencing or .fastq.gz files, which are tab delimited text files. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Zenodo DOI: 10.5281/zenodo.7566113(https://zenodo.org/record/7566113#.ZCcmvC2cbrJ)

Title: A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity. Description: This submission contains the "Rdata" or ".Rds" file, which is an R object file. This is a necessary file to use the code. Submission type: Restricted Acess. In order to gain access to the repository, you must contact the author.

Installation and Instructions

The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation:

Ensure you have R version 4.1.2 or higher for compatibility.

Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code.

1. Download the *"Rdata" or ".Rds" file from Zenodo (https://zenodo.org/record/7566113#.ZCcmvC2cbrJ) (Zenodo DOI: 10.5281/zenodo.7566113).
2. Open R-Studios (https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R.
3. Set your working directory to where the following files are located:

marengo_code_for_paper_jan_2023.R Install_Packages.R Marengo_newID_March242023.rds genes_for_heatmap_fig5F.xlsx all_res_deg_for_heat_updated_march2023.txt

You can use the following code to set the working directory in R:

setwd(directory)

1. Open the file titled "Install_Packages.R" and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies in order to set up an environment where the code in "marengo_code_for_paper_jan_2023.R" can be executed.
2. Once the "Install_Packages.R" script has been successfully executed, re-start R-Studios or your IDE of choice.
3. Open the file "marengo_code_for_paper_jan_2023.R" file in R-studios or your IDE of choice.
4. Execute commands in the file titled "marengo_code_for_paper_jan_2023.R" in R-Studios or your IDE of choice to generate the plots.

RNA-seq on K562 cells treated by CRISPR interference targeting STAT2.For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODE_Data_Use_Policy_for_External_Users_03-07-14.pdf

Array Manufacturer: Agilent, Distribution: custom-commercial, Technology: in situ oligonucleotide, We tiled the entire DMD gene, in both sense and antisense directions, using the web-based Agilent eArray database, Version 4.5 (Agilent Technologies), with 60-mer oligos every 66 bp of repeat-masked genome sequence. We defined probe sets for both orientations, encompassing the DMD exons, promoters, introns, predicted MiRNA (identified by PromiRII) and conserved non-coding sequences (CNSs) identified within dystrophin introns using the VISTA programme (http://genome.lbl.gov/vista/index.shtml). Two specific sets of probes were designed to cover, in both directions, the cDNA sequences of a group of control genes (Supplementary Table S1) identified in the Gene Expression Omnibus (GEO) database http://www.ncbi.nlm.nih.gov/geo/) and expressed equally in both normal and dystrophic muscles. Each probe set was opportunely distributed and replicated several times in order to obtain two 4x44k microarrays, referred to as DMD GEx Sense and DMD GEx Antisense, respectively, able to detect transcripts in the same and opposite directions as that of DMD gene transcription.

This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our “just-in-time” analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to “prune” the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF “N-specificity” index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs—CRF4, SNZ, CDF1, HHO5/6, and PHL1—validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and 15NO3− uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal “transcriptional logic” for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine. External links: 1. DFG: http://cs.nyu.edu/~mirowski/pub/GRN_Krouk_Mirowski_GenomeBiology.zip, 2. GEO dataset: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500

We report the expression of microbial metabolite tranporters in mucosal biopsies from children with GVHD RNA-Seq of GI mucosal tissue in children with GVHD and without GVHD

This template is for recording genome data from the NimbleGen platform. This template was taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics. Using these templates will mean easier submission to GEO/ArrayExpress and greater consistency of data in SEEK.

Sequences from this study are available at the NCBI GEO under accession series GSE131846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE131846

Based on Bacteroides thetaiotaomicron gene BT_0095, a putative uncharacterized protein As seen in gene expression experiments (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2231), It appears to be upregulated in the presence of host or vs when in culture .

Macrophages play a key role in ozone-induced lung injury by regulating both the initiation and resolution of inflammation. These distinct activities are mediated by pro-inflammatory and anti-inflammatory/pro-resolution macrophages which sequentially accumulate in injured tissues. Macrophage activation is dependent, in part, on intracellular metabolism. Herein, we used RNA-sequencing (seq) to identify signaling pathways regulating macrophage immunometabolic activity following exposure of mice to ozone (0.8 ppm, 3 hr) or air control. Analysis of lung macrophages using an Agilent Seahorse showed that inhalation of ozone increased macrophage glycolytic activity and oxidative phosphorylation at 24 and 72 hr post exposure. An increase in the percentage of macrophages in the S phase of the cell cycle was observed 24 hr post ozone. RNA-seq revealed significant enrichment of pathways involved in innate immune signaling and cytokine production among differentially expressed genes at both 24 and 72 hr after ozone, while pathways involved in cell cycle regulation were upregulated at 24 hr and intracellular metabolism at 72 hr. An interaction network analysis identified tumor suppressor 53 (TP53), E2F family of transcription factors (E2Fs), Cyclin Dependent Kinase Inhibitor 1A (CDKN1a/p21), and Cyclin D1 (CCND1) as upstream regulators of cell cycle pathways at 24 hr and TP53, nuclear receptor subfamily 4 group a member 1 (NR4A1/Nur77), and estrogen receptor alpha (ESR1/ERα) as central upstream regulators of mitochondrial respiration pathways at 72 hr. These results highlight the complex interaction between cell cycle, intracellular metabolism, and macrophage activation which may be important in the initiation and resolution of inflammation following ozone exposure. Methods Total RNA was extracted as described above from 3 mice/treatment group. In a pilot study, we found that 3 mice were sufficient to identify a significant difference in Ptgs2 gene expression by qPCR at α = 0.05 and power = 80%. RNA integrity numbers (RINs) were confirmed to be ≥ 8.8 using a 2100 Bioanalyzer Instrument (Agilent, Santa Clara, CA). cDNA libraries were prepared using mouse TruSeq® Stranded Total RNA Library Prep kit (illumina, San Diego, CA) and quantified using a KAPA Library Quantification kit (Roche, Pleasanton, CA). cDNA libraries were sequenced (75 bp single-ended, ~35-44M reads per sample) on an Illumina NextSeq instrument. Raw reads in FastQ files were trimmed using Trimmomatic-0.39 (Bolger et al. 2014) and quality control of trimmed files performed using FastQC. Salmon was used to align reads in mapping-based mode with selective alignment against a decoy-aware transcriptome generated from mouse transcriptome GENCODE Release M23 (GRCm38.p6). Estimated counts per transcript were generated using the gcBias flag and normalized to transcript length to correct for potential changes in gene length across samples from differential isoform usage (Love et al. 2016; Patro et al. 2017). Transcript level quantitation data were aggregated to the gene-level using tximport (Soneson et al. 2015). Differential gene expression analysis was performed with air exposed mice as controls using DESeq2 with corrections for differences in library size (Love et al. 2014) in R version 4.0.3. Significantly enriched canonical pathways and upstream regulators were identified with Ingenuity IPA Version 65367011 (QIAGEN Inc, https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/) using a right-tailed Fisher’s Exact Test (Krämer et al. 2014). A less stringent criteria (fold change > 1.3 and experimental false discovery rate [padj] < 0.05) was used to augment the number of genes included in the pathway analysis (Bennett et al. 2024). Data were deposited NCBI’s Gene Expression Omnibus (Edgar et al. 2002) and are accessible through GEO Series accession number GSE237594 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237594).

NCBI Gene Expression Omnibus accession numbers GSE49047 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49047).

Data for EPA collaboration with Case Western Reserve Univ, Erin Cohn, et al. 'Pervasive environmental chemicals impair oligodendrocyte development', DOI https://doi.org/10.1038/s41593-024-01599-2. Primary screening results are available in Supplementary Table 1 and will be included in a future public release of the EPA ToxCast database. RNA-seq datasets generated in this study have been deposited in Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo) under accession code GSE244500. This dataset is associated with the following publication: Cohn, E., B. Clayton, M. Madhavan, K. Lee, S. Yacoub, Y. Fedorov , M. Scavuzzo, K. Paul-Friedman, T. Shafer, and P. Tesar. Pervasive environmental chemicals impair oligodendrocyte development. Nature Neuroscience. Nature Portfolio, Berlin, GERMANY, 27(5): 836-845, (2024).

A virtual database of annotations made by 50 database providers (April 2014) - and growing (see below), that map data to publication information. All NIF Data Federation sources can be part of this virtual database as long as they indicate the publications that correspond to data records. The format that NIF accepts is the PubMed Identifier, category or type of data that is being linked to, and a data record identifier. A subset of this data is passed to NCBI, as LinkOuts (links at the bottom of PubMed abstracts), however due to NCBI policies the full data records are not currently associated with PubMed records. Database providers can use this mechanism to link to other NCBI databases including gene and protein, however these are not included in the current data set at this time. (To view databases available for linking see, http://www.ncbi.nlm.nih.gov/books/NBK3807/#files.Databases_Available_for_Linking ) The categories that NIF uses have been standardized to the following types: * Resource: Registry * Resource: Software * Reagent: Plasmid * Reagent: Antibodies * Data: Clinical Trials * Data: Gene Expression * Data: Drugs * Data: Taxonomy * Data: Images * Data: Animal Model * Data: Microarray * Data: Brain connectivity * Data: Volumetric observation * Data: Value observation * Data: Activation Foci * Data: Neuronal properties * Data: Neuronal reconstruction * Data: Chemosensory receptor * Data: Electrophysiology * Data: Computational model * Data: Brain anatomy * Data: Gene annotation * Data: Disease annotation * Data: Cell Model * Data: Chemical * Data: Pathways For more information refer to Create a LinkOut file, http://neuinfo.org/nif_components/disco/interoperation.shtm Participating resources ( http://disco.neuinfo.org/webportal/discoLinkoutServiceSummary.do?id=4 ): * Addgene http://www.addgene.org/pgvec1 * Animal Imaging Database http://aidb.crbs.ucsd.edu * Antibody Registry http://www.neuinfo.org/products/antibodyregistry/ * Avian Brain Circuitry Database http://www.behav.org/abcd/abcd.php * BAMS Connectivity http://brancusi.usc.edu/ * Beta Cell Biology Consortium http://www.betacell.org/ * bioDBcore http://biodbcore.org/ * BioGRID http://thebiogrid.org/ * BioNumbers http://bionumbers.hms.harvard.edu/ * Brain Architecture Management System http://brancusi.usc.edu/bkms/ * Brede Database http://hendrix.imm.dtu.dk/services/jerne/brede/ * Cell Centered Database http://ccdb.ucsd.edu * CellML Model Repository http://www.cellml.org/models * CHEBI http://www.ebi.ac.uk/chebi/ * Clinical Trials Network (CTN) Data Share http://www.ctndatashare.org/ * Comparative Toxicogenomics Database http://ctdbase.org/ * Coriell Cell Repositories http://ccr.coriell.org/ * CRCNS - Collaborative Research in Computational Neuroscience - Data sharing http://crcns.org * Drug Related Gene Database https://confluence.crbs.ucsd.edu/display/NIF/DRG * DrugBank http://www.drugbank.ca/ * FLYBASE http://flybase.org/ * Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/ * Gene Ontology Tools http://www.geneontology.org/GO.tools.shtml * Gene Weaver http://www.GeneWeaver.org * GeneDB http://www.genedb.org/Homepage * Glomerular Activity Response Archive http://gara.bio.uci.edu * GO http://www.geneontology.org/ * Internet Brain Volume Database http://www.cma.mgh.harvard.edu/ibvd/ * ModelDB http://senselab.med.yale.edu/modeldb/ * Mouse Genome Informatics Transgenes ftp://ftp.informatics.jax.org/pub/reports/MGI_PhenotypicAllele.rpt * NCBI Taxonomy Browser http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html * NeuroMorpho.Org http://neuromorpho.org/neuroMorpho * NeuronDB http://senselab.med.yale.edu/neurondb * SciCrunch Registry http://neuinfo.org/nif/nifgwt.html?tab=registry * NIF Registry Automated Crawl Data http://lucene1.neuinfo.org/nif_resource/current/ * NITRC http://www.nitrc.org/ * Nuclear Receptor Signaling Atlas http://www.nursa.org * Olfactory Receptor DataBase http://senselab.med.yale.edu/ordb/ * OMIM http://omim.org * OpenfMRI http://openfmri.org * PeptideAtlas http://www.peptideatlas.org * RGD http://rgd.mcw.edu * SFARI Gene: AutDB https://gene.sfari.org/autdb/Welcome.do * SumsDB http://sumsdb.wustl.edu/sums/ * Temporal-Lobe: Hippocampal - Parahippocampal Neuroanatomy of the Rat http://www.temporal-lobe.com/ * The Cell: An Image Library http://www.cellimagelibrary.org/ * Visiome Platform http://platform.visiome.neuroinf.jp/ * WormBase http://www.wormbase.org * YPED http://medicine.yale.edu/keck/nida/yped.aspx * ZFIN http://zfin.org

Integration of in silico and in vitro approaches to design and conduct transcriptomic drug screening in patient-derived neural cells, in order to survey novel pathologies and points of intervention in schizophrenia.