The Nuclear Receptor Signaling Atlas (NURSA) is a co-operative agreement (DK097748) awarded by the National Institute of Diabetes, Digestive and Kidney Diseases to Baylor College of Medicine (BCM), with additional support from the National Institute of Child Health and Development. It supports an Informatics Hub as well as the competitive NURSA Data Source Project program, open annually and funded by subcontracts to individual investigators from BCM.

The mission of NURSA is to accrue, develop, and communicate information that advances our understanding of the roles of nuclear receptors (NRs) and coregulators in human physiology and disease. The NURSA website has been developed over the past decade into a comprehensive source of information about NRs and their co-regulators, ligands, and downstream transcriptional targets.

New features, enhancements and patches that have not yet been released to this public website may be reviewed at our Beta site. NURSA welcomes feedback on the production and Beta hub websites via its support@nursa.org.

We have created an animated, narrated tutorial describing concepts and models of nuclear receptor signaling. This tutorial has been peer-reviewed and published in Science's Signal Transduction Knowledge Environment (STKE). To cite the tutorial, please reference McKenna NJ and O'Malley BW (2005) An interactive course in nuclear receptor signaling: concepts and models. Sci STKE. 2005, tr22. Please click here to view the video tutorial. Please adjust the volume on your speakers before playing the tutorial.

Yes, the tutorials may be reused for non-commercial purposes provided that any such use includes a citation to the above referenced manuscript.

Click on the newsletter button above the menu, enter your first and last name, and then click sign up.

Data from external resources are refreshed every six months through an automated pipeline that extracts, transforms and loads data into the database. Some data, such as clinical trials, then undergo additional manual validation by NURSA curators. The exception is the Transcriptomine target gene search engine (see below), for which datasets are annotated and curated by NURSA staff and uploaded to Transcriptomine on a continuous basis.

Recommended browsers are Firefox 24+, Chrome 30+, Safari 5.1.9+, and IE9+.

This may be due to a browser issue. We recommend using the following browsers: Firefox 24+, Chrome 30+, Safari 5.1.9+, and IE9+. The NURSA hub uses responsive design, so the site will render slightly differently on smaller mobile devices than it would on larger laptop or desktop screens. It has been optimized for a 15" laptop. For larger or smaller screens on non-mobile devices, you may need to adjust the zoom within your web browser to maximize your user experience. If you are still experiencing display issues please contact us.

Click on the molecules box on the homepage or click on molecules in the menu to reach the all molecules page. The search text field is located on the all molecules page just below the breadcrumb trail. Type the molecule symbol or synonym in the search text field and then select the correct term from the autogenerated list. You will then be directed to the detail page for the selected molecule.

Please use the search text field located on the all molecules and molecule detail pages to find your molecule of interest. Type the molecule symbol or synonym in the search text field and then select the correct term from the autogenerated list. You will then be directed to the detail page for the selected molecule. If you still cannot find your molecule of interest please contact us.

Nuclear receptor coregulators are molecules that interact with nuclear receptors and are required by nuclear receptors for efficient activation or repression of gene expression. Note that coactivation or corepression are contextual properties that can often coexist in the same molecule, and for this reason NURSA does designate coregulators explicitly as coactivators or corepressors. Inclusion of a molecule in the NURSA coregulator database requires a peer-reviewed publication showing (a) interaction with a nuclear receptor and (b) loss-of-function or gain-of-function data demonstrating its contribution to the activity of a nuclear receptor in a standard cell biology assay.

The addition of the Drug section to the NURSA Molecule Pages is one of the new features of the website added to make it more accessible to clinicians and physicians in the nuclear receptor signaling field. With a few exceptions, the term ligand in the context of nuclear receptors is classically taken to refer to a molecule that interacts with and modulates that AF-2 function of the nuclear receptor ligand binding domain. Although many well-known drugs – tamoxifen and cortisol for example, are also classical nuclear receptor ligands, many do not meet this definition, and target other nuclear receptor functions. For this reason. although there are many overlaps, we have annotated Drugs and Ligands separately.

In the first two funding cycles of NURSA (2002-2012), only datasets funded by the NURSA Consortium were included in the NURSA database. In the current funding cycle (2012-2017) we removed the restriction that datasets on the NURSA website had to have been generated using NURSA funds, and we currently curate ‘omics-scale datasets associated with any published article, NURSA funded or otherwise.

According to CDISC, a dataset is defined as a collection of related data records, and an experiment is defined as a coordinated set of actions and observations designed to generate data with the ultimate goal of discovery or hypothesis testing. Each experiment generates numerous data points which we have called gene lists in the case of transcriptomic datasets or metabolite lists for metabolomic datasets.

A list of NURSA datasets can be found on the all datasets page. By clicking on the dataset name, you can view a dataset detail page which displays metadata about the dataset in the Overview section, the Experiment section, which includes tabs for a scatterplot and table view of the genes/metabolites and experimental conditions for the selected experiment and a Related Datasets section based on the regulatory molecule or biosample source for the experiments in the dataset.

By default the gene/metabolite scatterplots and table views only display data points with a minimum fold change value ≥1.5 and p-value ≤0.05. If no data points are displayed in the graph or shown in the table view, you can download the data points for the entire dataset by clicking the Download Dataset button in the Overview section.

Datasets are related based on the regulatory molecule or the biosample used in the experiment(s) for that dataset. At this time datasets are not ranked on their level of relatedness.

The Dataset Citation in the Dataset Detail page is the information needed to cite the dataset. To provide users of the site with context for the dataset, we provide information on the primary research article with which the dataset is associated in the the Associated Article page.

Experiment Names are assigned after careful, detailed consideration of the article associated with the dataset. Disambiguation of experiment names in NURSA transcriptomic datasets is an essential part of the user experience, for example when experiment names are viewed side by side in the gene list drop down on the dataset pages, or when fold changes are viewed side by side in Transcriptomine. Equally, removing redundancy in experiment names makes for a cleaner, less visually cluttered user interface. To achieve these twin aims in an elegant, consistent manner, we have made two important changes to Experiment Names:

Before we explain Experiment Names, a word on regulatory molecule nomenclature. Ligands and small molecules are indicated using a unique NURSA Short Symbol. All genes are shown with the format common symbol/approved symbol, e.g. TRα/THRA, unless these are the same (e.g. AR), in which case only one is shown. Common symbols are all upper case and approved symbols are case sensitive per species.

Experiment names comprise up four defined components: 1) core contrast; 2) ligand or regulatory small molecule concentration/dose and duration; 3) regulatory molecules on both sides of the core contrast; and 4) additional disambiguating annotations. These four components are combined to make experiment name unique within a specific dataset - see the Dataset Detail Page section for a discussion of the importance of visually disambiguating experiment names within a dataset.

1. Core Contrast

   The Core Contrast is of the form A vs B, defining the primary experimental perturbation and control:

   • 17βE2 vs Veh
   • ERα/ESR1 OE vs EV
   
   

   The OE above indicates “overexpressed”. See below for a full list of non-standard codes used in Experiment Names.

2. Ligand or regulatory small molecule concentration/dose and duration

   For datasets comparing the transcriptomes of ligands or regulatory small molecule administered at different concentrations/doses and/or durations, the core contrast is separated by a pipe | from the concentration/dose and duration information.

   • MePred vs Veh | 50 mg/kg body weight, 1 h
   • MePred vs Veh | 50 mg/kg body weight, 2 h
   • MePred vs Veh | 50 mg/kg body weight, 4 h
   
   
3. Regulatory molecules on both sides of the contrast

   Regulatory molecules that appear on both sides of a core contrast are indicated in parentheses after the core contrast (and the Concentration/Dose & Duration, if present), along with a code as if required.

   • Baz vs Veh (17βE2)
   • T1317 vs Veh (LXRα/NR1H3 OE)
   • 17βE2 vs Veh | 10 nM, 8 h (ERα/ESR1 WT OE)
   
   

   Multiple molecules are separated by a "+" sign.

   • Tam vs Veh (Test + CYP19A1 OE)
   
   

   In the case of datasets involving gene knockout experiments, control experiments are designated (WT) to indicate the wild type animal.

4. Additional information

   If additional disambiguating information is still required to make an Experiment Name unique within a dataset, this appears after a dash - at the end of the Experiment Name. This can be an abbreviated RNA Source, time point, gender, strain, or some other relevant annotation, and does not follow any set format.

   • Pred vs Veh - Patient 1
   • Pred vs Veh - Patient 2
   • Phenob vs Veh - Replicate 1
   • Phenob vs Veh - Replicate 2
   • Phenob vs Veh - Replicate 3
   • Acmphn vs Veh | 300 mg/kg body weight, 3 h - C57BL/6J
   • Acmphn vs Veh | 300 mg/kg body weight, 3 h - DBA/2J
   • RORα/Rora KO vs WT - Liver
   • RORα/Rora KO vs WT - WAT
   
   

   If you’re still a bit confused, NURSA curators always associated an Experiment Description with each experiment, which can be accessed in the Fold Change Detail Window for each fold change (in Transcriptomine), or in the Dataset Page.

   Below is a list of non-standard abbreviations in Experiment Names

   • DKO => Double knockout
   • EV => Empty vector
   • HET => Heterozygote
   • KD => Knockdown
   • KI => Knock-in
   • KO => Knockout
   • mg/kg bw =>milligram/kilogram body weight
   • Mut => Mutant
   • NC => Negative control
   • OE => Overexpressed
   • Veh => Vehicle
   • UI => Uninduced
   • WT => Wild type

I have a dataset I wish to contribute to NURSA, how do I do this?

The NURSA Hub is not intended to replace existing, standard public data repositories such as GEO, dbGaP, ENCODE or ProteomeXchange. If you would like to have your data integrated within the hub, please deposit your data within an appropriate repository and contact us with the datasets identifier information. If you have generated NR- or coregulator-relevant datasets for which no standard repository exists, please contact us.

How do I download the dataset metadata?

Dataset metadata can be harvested via the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) standard, version 2.0. The OAI-PMH is based on metadata harvesting and has a well-defined protocol (http://www.openarchives.org/OAI/openarchivesprotocol.html). It provides an application independent interoperability framework with two classes of participants: 1) Data Providers, which use OAI-PMH supported systems to expose metadata and 2) Service Providers, which harvest metadata via the OAI-PMH.

Transcriptomine is a search engine that queries across all fold changes in all NURSA transcriptomic datasets.

Experiment Names in Transcriptomine have the same basic format:

Condition x vs Condition y

The value in the Fold Change column reflects the ratio

Expression level under Condition x/Expression level in Condition y

A Fold Change value >1 corresponds to upregulation or induction of the gene in condition x relative to condition y. For example, a fold change of 4.32 for GREB1 in the experiment 17β-estradiol vs control indicates that 17β-estradiol induces GREB1 approximately 4 fold in that experiment.

A Fold Change value <1 corresponds to down-regulation or repression in condition x relative to condition y. For example, a fold change of - 2.5 fold of NCOA3 in 17β-estradiol vs control indicates that NCOA3 is downregulated 2.5 fold by 17β-estradiol in that experiment.

To identify datasets, we use a three-part Perl script with NCBI's eUtils to capture and download journal abstracts from studies investigating NR, NR ligand and coregulator-dependent gene expression specifically in the context of genome-wide technologies such as microarray and RNA-Seq (see Supplementary File S2 for a list of molecule terms). Fold changes were extracted preferentially from public high throughput database repositories containing full datasets (NCBI Gene Expression Omnibus and EBI ArrayExpress) or, when these were unavailable, were manually retrieved from investigator-curated gene lists published in journal articles and supplementary files.

For more information see: Ochsner SA, Watkins CM, McOwiti A, Xu X, Darlington YF, Dehart MD, Cooney AJ, Steffen DL, Becnel LB, McKenna NJ. (2012) Transcriptomine: a web resource for nuclear receptor signaling transcriptomes. Physiol Genomics 44, 853-63.

Investigator-provided normalized array feature expression intensities are retrieved from the series matrix file (GEO) or processed files (ArrayExpress). To calculate relative abundance gene expression for investigator-defined experimental contrasts in the statistical program R, we use the linear modeling functions from the Bioconductor limma analysis package. Initially, a linear model is fitted to a group-means parameterization design matrix defining each experimental variable. Subsequently, a contrast matrix is fitted that recapitulates the sample contrasts of interest as defined in the study producing fold change and significance values for each array feature present on the array. P-values obtained from Limma analysis are not corrected for multiple comparisons. In cases where a given gene is represented on an array by more than one probeset, data from individual probesets are generated separately: fold changes values are not pooled across array features. Finally, individual identifiers on the array are mapped to the current Entrez Gene ID and the appropriate approved gene symbol.

Note that the fold changes in the NURSA dataset may differ from those in the associated article. This is because articles rarely contain a description of how the fold changes were calculated from the normalized intensity values. As a result, NURSA curators are unable to exactly recapitulate this step. In the absence of this information, and in the interests of transparency and consistency,NURSA curator-calculated gene fold changes are generated solely by applying the industry standard procedure outlined above to the investigator-provided normalized array feature expression intensities in the original dataset deposition. While we do our best to minimize differences, and always cross-check the fold changes we generate with those of selected genes reported in the associated article, some small discrepancies are inevitable.

For more information see: Ochsner SA, Watkins CM, McOwiti A, Xu X, Darlington YF, Dehart MD, Cooney AJ, Steffen DL, Becnel LB, McKenna NJ. (2012) Transcriptomine: a web resource for nuclear receptor signaling transcriptomes. Physiol Genomics 44, 853-63 PubMed

For information on Affymetrix probeset naming conventions, see here

For more information see: Ochsner SA, Watkins CM, McOwiti A, Xu X, Darlington YF, Dehart MD, Cooney AJ, Steffen DL, Becnel LB, McKenna NJ. (2012) Transcriptomine: a web resource for nuclear receptor signaling transcriptomes. Physiol Genomics 44, 853-63.

Fold changes in TM are unlogged to facilitate their interpretation, especially for those investigators who may not have extensive experience interpreting logged values. For example, a fold change value of 2.0 in Transcriptomine is equal to a 2-fold increase in expression and a fold change value of -2.0 in Transcriptomine is equal to a 2-fold decrease in expression.

Fold changes are not cross-normalized across different experiments in Transcriptomine. Fold changes are inherently comparable across different datasets and do not require normalization. In any case, for many experiments authors do not deposit raw data in public repositories and so unprocessed values, which are required for cross-normalization, are not available.

In addition to basic annotations such as species (human, mouse or rat), gene fold change value, direction, significance and Regulatory Molecule (ligand, NR or coregulator), experiments were annotated for a number of variables known to be critical determinants of the transcriptional response to NRs and their ligands.

(i) Ligand concentrationThe transcriptional function of NRs is known to be closely linked to the amount of ligand available for binding, and receptor-ligand pairs have been postulated to act as rheostats which function to fine tune the transcriptional response in a given tissue. Moreover, many endocrine-disrupting chemicals (EDCs) such as bisphenol A are biologically active only at concentrations several orders of magnitude higher than natural agonists for the NR in question. As such, the ligand concentration at which regulation of a specific gene is observed is a critical experimental descriptor.

(ii) Duration of ligand exposure. Experimental systems yield distinct transcriptional profiles based on the length of time they are exposed to NR ligand. Rapid non-genomic signaling by 17β-estradiol, for example, elicits transcriptional responses within minutes that differ fundamentally from those elaborated after multiple hours of exposure, when the genomic effects of liganded estrogen receptors are manifest.

(iii) RNA Source. Tissue and cell line identity are important determinants of transcriptional and functional endpoints of NR signaling pathways. For example, the breast cancer drug Tamoxifen, while an estrogen receptor (ER) antagonist in the breast, has partial ER agonist activity in the uterus and ER agonist activity in the skeleton and induction or repression of the same gene by glucocorticoid receptor (GR) agonists has been shown to be a function of cell type. In order to align our RNA source annotations with published standards, we used the EFO Experimental Factor Ontology and the CL cell type ontology where possible.

For more information see: Ochsner SA, Watkins CM, McOwiti A, Xu X, Darlington YF, Dehart MD, Cooney AJ, Steffen DL, Becnel LB, McKenna NJ. (2012) Transcriptomine: a web resource for nuclear receptor signaling transcriptomes. Physiol Genomics 44, 853-63. Full Text

There are two possible explanations for this.

The first is that the default fold change and significance p-value cut-offs are too strict. While FC = 2 and p <0.05 are common cut-off values they do not serve everyone's purpose. Try relaxing these using the Other option in the pulldown. Selecting Any in the Significance field will retrieve all p-values, including fold changes from experiments with unreported p-values (Not reported), in which the original investigators did not report p-values. Note that Not reported p-values may actually be significant, but because no numerical value was provided in the publication, we are unable to determine whether they are or not.

The second possibility is simply that the database currently does not contain the data points you are seeking. We are currently making our way through curating a backlog of studies and Transcriptomine is updated on a continuous basis, so it is possible that data points corresponding to your query will be added to the Transcriptomine database in future.

To facilitate querying by our users, version 3.2.1 of Transcriptomine (May 2016) phased out querying using individual regulatory molecules and RNA Source, and introduced in their place menu-driven querying by Signaling Pathway and Tissue/Cell Line.

Signaling Pathway

Ligands are assigned to receptors using the International Union of Pharmacology (IUPHAR) Guide To Pharmacology (GtoP) mappings. Receptors and their mapped ligands are then organized into signaling pathways as shown below.

Nuclear receptor signaling pathways and their regulatory molecules in Transcriptomine v 3.2.1 (May 2016)

Top level Categories are in large font (e.g NR Pathways) followed by the Signaling Pathway is slightly smaller font (e.g. ERs & Estrogens). Beneath those are symbols representing individual regulatory molecules in the signaling pathway, again in the example below, 17βE2 = 17β-estradiol, 4HT = 4-hydroxytamoxifen, etc. For a full list of small molecule symbols and their corresponding full names, see this Table. Fold changes from experiments in which a regulatory molecule is a variable in the experiment are shown as blue (repression) or red (induction) circles. Click on a data point for additional information and a link to the full Fold Change Detail page.

The top level category in the Biosample view is Physiological System (in the example below, Female Reproductive), followed by Organ (in the example below, Mammary Gland, Uterus, etc). The bottom level values are individual tissues, cell lines, etc. Fold changes from the corresponding experiments are shown as blue (repression) or red (induction) circles. Click on a data point for additional information and a link to the full Fold Change Detail page. For a full Biosample hierarchy, click here.

Experiment Names are assigned after careful, detailed consideration of the article associated with the dataset. Disambiguation of experiment names in NURSA transcriptomic datasets is an essential part of the user experience, for example when experiment names are viewed side by side in the gene list drop down on the dataset pages, or when fold changes are viewed side by side in Transcriptomine. Equally, removing redundancy in experiment names makes for a cleaner, less visually cluttered user interface. To achieve these twin aims in an elegant, consistent manner, we have made two important changes to Experiment Names:

Before we explain Experiment Names, a word on regulatory molecule nomenclature. Ligands and small molecules are indicated using a unique NURSA Short Symbol. All genes are shown with the format common symbol/approved symbol, e.g. TRα/THRA, unless these are the same (e.g. AR), in which case only one is shown. Common symbols are all upper case and approved symbols are case sensitive per species.

Experiment names comprise up four defined components: 1) core contrast; 2) ligand or regulatory small molecule concentration/dose and duration; 3) regulatory molecules on both sides of the core contrast; and 4) additional disambiguating annotations. These four components are combined to make experiment name unique within a specific dataset - see the Dataset Detail Page section for a discussion of the importance of visually disambiguating experiment names within a dataset.

1. Core Contrast

   The Core Contrast is of the form A vs B, defining the primary experimental perturbation and control:

   • 17βE2 vs Veh
   • ERα/ESR1 OE vs EV
   
   

   The OE above indicates “overexpressed”. See below for a full list of non-standard codes used in Experiment Names.

2. Ligand or regulatory small molecule concentration/dose and duration

   For datasets comparing the transcriptomes of ligands or regulatory small molecule administered at different concentrations/doses and/or durations, the core contrast is separated by a pipe | from the concentration/dose and duration information.

   • MePred vs Veh | 50 mg/kg body weight, 1 h
   • MePred vs Veh | 50 mg/kg body weight, 2 h
   • MePred vs Veh | 50 mg/kg body weight, 4 h
   
   
3. Regulatory molecules on both sides of the contrast

   Regulatory molecules that appear on both sides of a core contrast are indicated in parentheses after the core contrast (and the Concentration/Dose & Duration, if present), along with a code as if required.

   • Baz vs Veh (17βE2)
   • T1317 vs Veh (LXRα/NR1H3 OE)
   • 17βE2 vs Veh | 10 nM, 8 h (ERα/ESR1 WT OE)
   
   

   Multiple molecules are separated by a "+" sign.

   • Tam vs Veh (Test + CYP19A1 OE)
   
   

   In the case of datasets involving gene knockout experiments, control experiments are designated (WT) to indicate the wild type animal.

4. Additional information

   If additional disambiguating information is still required to make an Experiment Name unique within a dataset, this appears after a dash - at the end of the Experiment Name. This can be an abbreviated RNA Source, time point, gender, strain, or some other relevant annotation, and does not follow any set format.

   • Pred vs Veh - Patient 1
   • Pred vs Veh - Patient 2
   • Phenob vs Veh - Replicate 1
   • Phenob vs Veh - Replicate 2
   • Phenob vs Veh - Replicate 3
   • Acmphn vs Veh | 300 mg/kg body weight, 3 h - C57BL/6J
   • Acmphn vs Veh | 300 mg/kg body weight, 3 h - DBA/2J
   • RORα/Rora KO vs WT - Liver
   • RORα/Rora KO vs WT - WAT
   
   

   If you’re still a bit confused, NURSA curators always associated an Experiment Description with each experiment, which can be accessed in the Fold Change Detail Window for each fold change (in Transcriptomine), or in the Dataset Page.

   Below is a list of non-standard abbreviations in Experiment Names

   • DKO => Double knockout
   • EV => Empty vector
   • HET => Heterozygote
   • KD => Knockdown
   • KI => Knock-in
   • KO => Knockout
   • mg/kg bw =>milligram/kilogram body weight
   • Mut => Mutant
   • NC => Negative control
   • OE => Overexpressed
   • Veh => Vehicle
   • UI => Uninduced
   • WT => Wild type

The default Transcriptomine single gene search looks across datasets from all species. Although the same gene often has the same symbol in the three species, differing only in capitalization, this is not always the case. In instances where they differ, a mixture of symbols will be seen in search results.

These experiments are typically one of a series of experiments which would make more sense when viewed side-by-side with the other experiments in the dataset. A future version of Transcriptomine will give the user the option to view fold changes from multiple experiments in a dataset side by side in a single scatterplot.

On occasion you will observe apparently conflicting information on regulation of a given gene by a given regulatory molecule. Many times this will be because the data points are from different datasets done by different laboratories using different reagents under different conditions, and the “noise” that arises from such discrepancies is a common phenomenon.

However, such discrepancy can arise within the same dataset. In these cases, there are several possible explanations, some biological, some technical.

(a) Differential regulation of a given transcript over time. If the two experiments involve a ligand, compare the Duration of Treatment value for each experiment by clicking on the fold change value to open the Fold Change Detail window.

(b) Differential regulation of a given by different ligand concentrations or doses. The effect of NR ligands has been shown to be concentration-dependent in certain contexts, at least in vitro. If the conflicting fold changes involve a ligand, check the ligand concentration or dose for each contrast by clicking on the fold change to open the Fold Change Detail window.

(c) Differential regulation of isoforms Alternative regulation of transcripts encoding different isoforms of the same gene has been demonstrated. In experiments where the raw data were available, the specific probeset ID (where applicable) is shown in the gene list. This links to a view of the UCSC Genome Browser showing the regions of the gene to which that probeset maps. If investigators did not deposit their raw data, but provided GenBank identifiers, these are displayed in the Identifier (Type) column, e.g. NM_014668 (GB).

(d) Tissue-specific regulation of a given gene. Regulation of gene expression is highly context-dependent, and there is abundant evidence for genes being regulated in different directions by the same signaling pathway in different cell types. The number of promoters regulated in a tissue-specific manner has been estimated at 20% of active promoters in the mouse (Barrera et al. (2008) Genome Research 18, 46-59) and the opposing effects of GR ligands on a given gene in different tissues have been documented (see Feltus et al. (2002) J Steroid Biochem Mol Biol 82, 55-63 and Badrinarayanan et al. (2006) Biochem Cell Biol. 84, 745-754).

It is not necessarily the case that all experiments in a given dataset were done at the same time, using the same instrumentation and by the same personnel. Variables such as these can impact the results of an experiment. Where discrepancies arise, it is a good idea, as a “sanity check”, to look at the regulation of other genes in a given experiment to view a particular fold change in context.

This is for one of two reasons:

1. If the gene list was retrieved from the published paper, the authors did not provide a p-value in the paper.
2. If the fold change was retrieved from a dataset deposited GEO, the dataset did not contain replicates.

Use of Transcriptomine is governed under a Creative Commons Attribution 3.0 license, which provides for sharing, adaptation and both non-commercial and commercial re-use, as long as the datasets in Transcriptomine are cited. This provides credit for the original authors of the dataset as well as to the Transcriptomine resource.

In order to strive for a positive and equitable experience for all users of Transcriptomine, queries are monitored. IP addresses regularly submitting queries that return an excessively large number of fold changes will be blocked. If you are seeking a large number of data points, please contact support@nursa.org so that your needs can be discussed further.

Citation of NURSA datasets is important so that authors receive full academic credit for their work in creating the dataset, especially when citing gene fold changes that are not discussed in the associated article. To cite a NURSA dataset click on the appropriate reference manager icon next to the download dataset citation field which can be found on Dataset Detail pages and in Transcriptomine in the Fold Change Detail window.

The information necessary to properly cite a dataset is: Authors, Year, Title, Repository and Identifier. NURSA currently supports downloading datasets into EndNote, Mendeley, Zotero, and Papers. As of the date of this version of the FAQs (March 2016) however, only Endnote supports Dataset as a reference style. In addition, few if any journals have guidelines for formatting dataset citations in their reference lists. This situation may change at any time, so we advise users to double-check their reference manager version settings and journal citation style guides for the most up to date information.

If your references are not appearing properly in your bibliography or reference, follow the steps in the diagram below - this need only be done once for each journal style.

EndNote

1. Click on the appropriate EndNote icon on the dataset page - this will download the citation into your library
2. In EndNote, click on Edit….Output Styles….Edit (your style of interest - in this example PLoS)
3. In the Journal Style window (1) click Templates (2) click Reference Types and (3) click Dataset.
4. Using the Insert Fields button in the top right of the Journal Style window, insert Investigators (Year) Title. Name of Database DOI Access Date.
5. Open the Style Manager in EndNote and check the box beside PLoS Copy, then select PLoS Copy as the EndNote Output Style in EndNote and Microsoft Word.

Citations should now appear in your bibliography in the following format:

Aenlle KK and Foster TC (2010) Age-dependence and time-course analysis of the 17β-estradiol (17βE2)-regulated transcriptome in mouse hippocampus. NURSA Datasets. dx.doi.org/10.1621/2xdFWjzGKz

Mendeley, Papers and Zotero do not currently support “Dataset” as a reference type. The dataset citation will therefore be imported in a generic format in these applications. Please refer to documentation in these applications to ensure that the citation displays in your reference list as it appears in the NURSA Dataset Detail page.

Stable constructed URLs pointing directly to Transcriptomine fold change results can be created using the rules outlined below. The geneSearchType parameter must be included in the constructed URL. All other query parameters are optional and are treated as the specified default if they are not included in the constructed URL.

The URL specification is able to handle special characters such as Greek symbols and any character that is not alphanumeric for the GO term, disease term, regulatory molecule, and tissue/cell line query parameters. Alternatively, these special characters can be provided as ASCII. For example, the URLs will be converted as follows:

• 17βE2 becomes 17%CE%B2E2
• Mammary gland, mammary carcinoma cells, MCF-7 becomes Mammary%20gland,%20mammary%20carcinoma%20cells,%20MCF-7

For an ASCII tutorial, please see the W3Schools site.

Use one or more of the parameters below to construct a Transcriptomine URL. Multiple parameters can be joined using “&”. Valid values for parameters are in italics. Example queries are shown after the list of parameters.

geneSearchType and species: The geneSearchType and species parameters limit query results to a specific approved gene symbol (or specific orthologs thereof); gene(s) that map to a specific Gene Ontology (GO) term; or gene(s) that map to an Online Mendelian Inheritance in Man (OMIM) disease name. Transcriptomine query URLs must contain only values specified by these standard controlled vocabularies or ontologies: aliases, synonyms and typographical errors or use of synonyms will not return results.

• To return fold changes for any gene: geneSearchType=any
• To return fold changes for all curated orthologs (currently human, mouse and rat) for a specific gene, provide the approved HGNC gene symbol as a value for geneSearchType. Combine this parameter with species and provide the case sensitive values Human, House Mouse and Norway Rat to return fold changes for the human, mouse or rat orthologs, respectively
• If geneSearchType=goTerm, provide GO (Gene Onotology) terminology name
• If geneSearchType=disease, provide OMIM (Online Mendelian Inheritance in Man) disease name

signalingPathway: The signalingPathway parameter allows the specification of a signaling pathway as an experimental variable in the query. Signaling pathway must be identified using the approved pathways listed below. See ‘When querying Transcriptomine, what is meant by Signaling Pathway and Biosample?’ for more information.

• All (default)
• AR+%26+Androgens
• CAR%2C+PXR+and+xeno-+%26+endobiotics
• COUP-TF+subfamily
• ERR+subfamily
• ERs+%26+Estrogens
• FXR+%26+bile+acids
• GR+%26+Glucocorticoids
• HNF4A+subfamily
• LXRs+%26+Oxysterols
• MR+%26+Mineralocorticoids
• NR4A+subfamily
• Others
• PPARs+%26+Fatty+acids
• PR+%26+Progestins
• RARs+%26+Retinoids
• ROR+subfamily
• RXRs+%26+Rexinoids
• TRs+%26+thyroid+hormone
• VDR+%26+vitamin+D3
• Other+NR+pathways

parentTissue, subTissue, & tissue: NURSA employs a hierarchical parent-subtissue-child vocabulary that allows users to restrict or broaden the range of biosamples as required. The parentTissue parameter restricts the query to one or more biosamples. If not specified, then the default parentTissue parameter is ‘All’. The biosample can be further restricted by using the subTissue parameter. The subTissue categories are dependent on the parentTissue. If not specified, then the default subTissue parameter is ‘All’. The tissue parameter restricts the biosample even further to a specific tissue or cell line. The parentTissue and subTissue parameters do not need to be included if the tissue parameter is entered. To view a list of biosamples currently curated within Transcriptomine, please click here.

significance: By default, Transcriptomine returns any fold change associated with a p- value of 0.05 or smaller. This statistical cut-off can be modified using the significance parameter and providing a numeric value between 0 and 1.

foldChange: By default, Transcriptomine returns any data point with a fold change value greater than or equal to |2.0| and less than or equal to |30.0|. This fold change range can be modified using the foldChangeMin and foldChangeMax parameters and providing a numeric value greater than 0.

direction: By default, Transcriptomine returns fold changes in either direction, up or down. To restrict fold changes to upregulated or downregulated, use the direction parameter and provide the case-sensitive values up or down, respectively.

1. Regulation of GREB1
   • https://nursa.org/nursa/transcriptomine/index.jsf?geneSearchType=gene&gene=GREB1&foldChangeMin=2&foldChangeMax=30&direction=any&significance=0.05&species=all
2. Regulation of Greb1 in mouse
   • https://nursa.org/nursa/transcriptomine/index.jsf?geneSearchType=gene&gene=GREB1&foldChangeMin=2&foldChangeMax=30&direction=any&significance=0.05&species=House Mouse
3. Regulation of GREB1 and direction up only
   • https://nursa.org/nursa/transcriptomine/index.jsf?geneSearchType=gene&gene=GREB1&foldChangeMin=2&foldChangeMax=30&direction=up&significance=0.05&species=all
4. Regulation of GREB1 in female reproductive tissues and cell lines
   • https://nursa.org/nursahttps://nursa.org/nursa/transcriptomine/index.jsf?geneSearchType=gene&gene=GREB1&foldChangeMin=2&foldChangeMax=30&direction=any&significance=0.05&parentTissue=Female+reproductive&subTissue=all&species=all
5. Regulation of GREB1 in the mammary gland (FC≥2, p &lt;0.05)
   • https://nursa.org/nursahttps://nursa.org/nursa/transcriptomine/index.jsf?geneSearchType=gene&gene=GREB1&foldChangeMin=2&foldChangeMax=30&direction=any&significance=0.05&parentTissue=Female+reproductive&subTissue=Mammary+gland&species=all
6. Regulation of genes that map to the Gene Ontology term "cell cycle phase transition"
   • https://www.nursa.org/nursa/transcriptomine/index.jsf?geneSearchType=goTerm&goTerm=cell cycle phase transition
7. Regulation of genes that map to the OMIM disease term "breast cancer"
   • https://nursa.org/nursa/transcriptomine/index.jsf?geneSearchType=disease&disease=BREAST+CANCER&foldChangeMin=2&foldChangeMax=30&direction=any&significance=0.05&species=all
8. Regulation of genes by the ARs & Androgens signaling pathway with a fold change minimum of 25 and maximum of 30
   • https://nursa.org/nursa/transcriptomine/index.jsf?geneSearchType=any&foldChangeMin=25&foldChangeMax=30&direction=any&significance=0.05&signalingPathway=AR+%26+Androgens&species=all

Some data points may be duplicated because their regulatory molecule belongs to more than one signaling pathway.

Yes, to retrieve data points please view the Transcriptomine API here. To retrieve dataset information, such as DOI, dataset name, dataset description, and release date, based on the regulatory molecule, please view the Dataset API here. All data exchange is in JSON.

Nuclear Receptor Signaling publishes primary research articles, reviews, methods and dataset reports in all mechanistic, functional and pathological aspects of nuclear receptor signaling. The Editors-in-Chief welcome submissions representing the broad range of disciplines in this field, including cell biology, biochemistry, physiology, chemistry, pharmacology and informatics, as well as clinical and translational studies. The intent of this journal is to act as a unique, pan-disciplinary home for innovative and insightful research in nuclear receptor signaling.

Please click here for instructions on how to submit a manuscript to the NRS.

Click on the reagents box on the homepage or click on reagents in the menu to reach the all reagents page. The search text field is located on the all reagents page just below the breadcrumb trail. Type the reagent name in the search text field and then select the correct term from the autogenerated list. You will then be directed to the detail page for the selected reagent.

Please use the search text field located on the all reagents and reagent detail pages to find your reagent of interest. Type the reagent name in the search text field and then select the correct term from the autogenerated list. You will then be directed to the detail page for the selected reagent. If you still cannot find your reagent of interest please contact us.

Click on the clinical box on the homepage or click on clinical in the menu to reach the all clinical page. The search text field is located on the all clinical page just below the breadcrumb trail. Type the clinical trial, disease or drug name in the search text field and then select the correct term from the autogenerated list. You will then be directed to the detail page for the selected clinical trial, disease or drug.

Please use the search text field located on the all clinical and clinical detail pages to find your clinical trial, disease or drug of interest. Type the clinical trial, disease or drug name in the search text field and then select the correct term from the autogenerated list. You will then be directed to the detail page for the selected clinical trial, disease or drug. If you still cannot find your clinical trial, disease or drug of interest, please contact us.

Clinical trials' data was mined to extract protocols for which there was a validated gene-disease or gene-drug association. Through this extraction methodology, any trial that mapped to a disease or condition that has a known association with a nuclear receptor or coregulator, or that utilized a drug targeting a nuclear receptor or coregulator was included. Extracted records were manually reviewed by NURSA curators. If you find any clinical trial that you believe should not be included on the website or that is missing, please contact us.

NURSA provides funding for pilot and feasibility (P&F) awards as part of its NURSA Data Source Projects (NDSP) arm. These awards are made possible largely through support from the NIDDK and also NICHD. The NURSA Bioinformatics Hub facilitates the P&F awards through the RFP notifications and online applications on its web portal (www.nursa.org). As RFPs are released, the NDSPs will be openly competed and peer reviewed. Each funding cycle will have one or two focus areas, which are selected by members of the NIDDK and a steering committee. Please view our NDSP RFP page to learn more about upcoming, current and past RFPs. To learn more about funded NDSP projects, please see our NURSA-funded Projects page.

We expect to fund 3-4 NDSPs (for 1 year with additional years dependent on progress and availability of funds).

Total costs for each NDSP cannot exceed $150,000/year for 1-2 years.

One year with an additional year dependent on progress and availability of funds.

One funding cycle is provided each year.

Interested investigators should e-mail a letter of intent containing a short (quarter page) description of the project and a statement of intent to apply, to Ronald Margolis, NIDDK NURSA Project Scientist and/or Neil McKenna, NURSA co-Principal Investigator prior to the deadline for letter of intent.

A new NDSP cycle is typically announced around January. Please check our NDSP RFP page often for updated information, follow us on Twitter or sign up for our newsletter by clicking on the newsletter button above the menu.

NDSP investigators must utilize the Hub for submitting proposals, distributing information about NURSA-funded reagents and sharing NURSA-funded datasets. It is expected that funded investigators must freely share NURSA-funded reagents with other researchers upon request, but NDSP investigators may charge a nominal fee for reimbursing their expenses for reagent processing and shipping. Further, NDSP PIs will serve as members of the NURSA Steering Committee, with bimonthly meetings.

All investigators including new and early stage investigators are encouraged to apply. Because NDSPs are intended to explore and impact in 1-2 years key issues within the NR research community, successful applicants must demonstrate that they have other existing sources of support.

Details for how to submit a proposal in response to the NDSP announcement, including a template for preparation of the application, will be made available to investigators who submit an approved letter of intent. Applications will be short (6 pages maximum) to allow investigators time to meet the submission deadline.