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Supplementary Figure 2 from A Serial Analysis of Gene Expression (SAGE) Database Analysis of Chemosensitivity

sagecodes/sage-rag-chroma-db-20250912-224438 dataset hosted on Hugging Face and contributed by the HF Datasets community

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9, 2022. The SAGE Genie website provides highly intuitive, visual displays of human and mouse gene expression, based on a unique analytical process (Boon et al.) that reliably matches SAGE tags, 10 or 17 nucleotides in length, to known genes. Recently, with the construction and incorporation of a reference database of SNP-associated alternative tags into SAGE Genie (Silva et al.), the interpretation of tag to human gene mapping has been enhanced. Human SAGE Genie Tools The SAGE Anatomic Viewer visually displays the relative expression of a given gene in normal and malignant tissues of the human body. In addition, there is access to: o The Digital Northern, which shows the relative expression of the gene in each library. o The Ludwig Transcript (LT) Viewer, which visually represents a particular transcript with up to four possible virtual SAGE tag locations (starting from the 3' end) and locations of internally primed or alternatively polyadenylated transcripts. o Expanded access to brain, hES cells, and breast cell subtypes. * The SAGE Digital Gene Expression Displayer distinguishes significant differences in gene expression profiles between two pools of SAGE libraries. * The SAGE Experimental Viewer provides DGED results for pre-set pairs of libraries, one under control and the other under experimental conditions. * The SAGE Absolute Level Lister lists all SAGE libraries and links to the distribution of transcript expression levels in any given library. * The SAGE Library Finder tool searches for one or more tissue-specific libraries from the SAGE collection. * SAGE Genie Downloads provide files of genes, tags, datasets, mappings, and more. * SAGE Genomics Finder tool searches for genomics info for tags. * DKView tool for viewing Digital Karyotyping data. * DK Microbe tool searches for exact matches of 17 base-pair digital karyotyping tags in bacterial and viral genomes. * SAGE Tag Extraction tool to extract tags from sequence files. Mouse SAGE Genie Tools * The mSAGE Expression Matrix visually displays the relative expression of a given gene through stages of mouse development. In addition, there is access to: o The Digital Northern, which shows the relative expression of the gene in each library. o The Ludwig Transcript (LT) Viewer, which visually represents a particular transcript with up to four possible virtual SAGE tag locations (starting from the 3' end) and locations of internally primed or alternatively polyadenylated transcripts. * The mSAGE Absolute Level Lister lists all mouse SAGE libraries, organized by either tissue (normal or malignant) or developmental stage, and links to the distribution of transcript expression levels in any given library. * The mSAGE Digital Gene Expression Displayer distinguishes significant differences in gene expression profiles between two pools of mouse SAGE libraries. * The mSAGE Experimental Viewer automatically sets up the DGED with mouse libraries, prepared as stand alone experiments, for gene expression comparison. * The mSAGE Library Finder tool searches for one or more tissue-specific mouse libraries from the SAGE collection. * mSAGE Genie Downloads provide files of mouse genes, tags, datasets, mappings, and more. * SAGE Genomics Finder tool searches for genomics info for tags. * SAGE Tag Extraction tool to extract tags from sequence files. Notes: The majority of human libraries are short whereas the majority of mouse libraries are long. Several mouse developmental libraries are prepared by the SAGELite method. SAGELite is an extension of the long Sage protocol that includes a PCR-based amplification stage to allow the use of 10ng to 100ng of total RNA to produce a Sage library. However, it has been observed that a significant bias incurred by this amplification process. SAGE Genie was produced as part of the NCI CGAP SAGE project with collaborators from Johns Hopkins University, the Ludwig Institute for Cancer Research, Sao Paulo Branch, the University of Texas MD Anderson Cancer Center, and the Genome Sciences Center of the BCCRC.

SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) atte ...

This CED spatial web service (ESRI ArcGIS Online Hosted Feature Layer) is a optimized quick visualization and unique value source for optimized CED web startup. The CED provides Sagebrush biome spatial representations and attribute information of conservation efforts entered into the Conservation Efforts Database (https://conservationefforts.org) by various data providers. This spatial web service is made of point and polygon layers and non-spatial tables. Feature records are group with their respective spatial feature type layers (point, polygon). The two spatial layers have identical attribute fields.Read only access to this data is ONLY available via an interactive web map on the Conservation Efforts Database website or authorized websites. Users who are interested in more access can directly contact the data providers by using the contact information available through the CED interactive map's pop-up/identify feature.The spatially explicit, web-based Conservation Efforts Database is capable of (1) allowing multiple-users to enter data from different locations, (2) uploading and storing documents, (3) linking conservation actions to one or more threats (one-to-many relationships), (4) reporting functions that would allow summaries of the conservation actions at multiple scales (e.g., management zones, populations, or priority areas for conservation), and (5) accounting for actions at multiple scales from small easements to statewide planning efforts.The sagebrush ecosystem is the largest ecosystem type in the continental U.S., providing habitat for more than 350 associated fish and wildlife species. In recognition of the need to conserve a healthy sagebrush ecosystem to provide for the long-term conservation of its inhabitants, the US Fish and Wildlife Service (Service) and United States Geological Survey (USGS) developed the Conservation Efforts Database version 2.0.0 (CED). The purpose of the CED is to efficiently capture the unprecedented level of conservation plans and actions being implemented throughout the sagebrush ecosystem and designed to capture actions not only for its most famous resident, the greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) but for the other species that rely on sagebrush habitats. Understanding the distribution and type of conservation actions happening across the landscape will allow visualization and quantification of the extent to which threats are being addressed.The purpose of this CED spatial web service (ESRI ArcGIS Online Hosted Feature Layer) is to provide CED data for authorized web sites or authorized users.

Sage-Grouse habitat areas divided into proposed management categories within Nevada and California project study boundaries.MANAGEMENT CATEGORY DETERMINATION The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was determined from a statewide resource selection function model and first categorized into 4 classes: high, moderate, low, and non-habitat. The standard deviations (SD) from a normal distribution of RSF values created from a set of validation points (10% of the entire telemetry dataset) were used to categorize habitat ‘quality’ classes. High quality habitat comprised pixels with RSF values < 0.5 SD, Moderate > 0.5 and < 1.0 SD, Low < 1.0 and > 1.5, Non-Habitat > 1.5 SD. Proposed Habitat Management Categories were then defined and calculated as follows.1) Core habitat: Defined as the intersection between all suitable habitat (high, moderate, and low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality falling outside the 85% SUI and all non-habitat falling within the 85% SUI. 3) General habitat: Defined as moderate and low quality habitat falling outside the 85% SUI. 4) Non habitat. Defined as non-habitat falling outside the 85% SUI. SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis.” Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis. Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. “An individual-based quantitative approach for delineating core areas of animal space use.” Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas for the Bi-State management area.

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.

This dataset contains two predictive lek (breeding site) persistence raster layers covering the U.S. greater sage-grouse distribution. In the United States, locations where males display and breed with females (i.e., leks) are often monitored annually by state wildlife agencies, providing valuable information on the persistence of birds in the surrounding areas. A U.S. range-wide lek database was recently compiled for greater sage-Grouse (O’Donnell et al. 2021), providing a standardized source of information to build statistical models to evaluate environmental characteristics associated with lek persistence. The compiled lek database classified a subset of leks as being either active (leks currently used for breeding activities) or inactive (leks no longer used for breeding activities) based on count data collected over a 20-year monitoring period. We fit the outcome of a lek being active or inactive as a function of environmental predictors characterizing surrounding conditions in a logistic regression model. Covariates included sagebrush cover, pinyon-juniper cover, topography, precipitation, point and line disturbance densities, and landscape configuration metrics. We included the Bureau of Land Management habitat assessment areas (termed mid-scales) as regional random effects in the form of random intercepts and random slopes (for a subset of covariates). The final model included 13 covariates. We predicted conditional probabilities of lek persistence across the U.S. occupied range using the covariate layers and regional mid-scales, which we make available here as a 30-meter resolution continuous raster dataset. The predictions were conditional because they were specific to each mid-scale factor level (i.e., pixel predictions were influenced by the regional mid-scale polygon they fell within via the associated mid-scale intercept and random slope deviations). We applied sensitivity thresholds (capturing percentage of leks correctly classified as active) to the continuous probability layer to bin persistence probabilities into high, medium, low, and marginal areas of persistence, which we make available here as a 30-m categorical raster dataset.

(a)ELSD indicated expression level in SAGE database.(b)‘Yes’ indicating at least one putative MoAP1 binding site was identified in the promoter region of the gene while ‘No’ stand for non putative MoAP1 binding site was identified in the promoter region.

This shapefile represents proposed management categories (Core, Priority, General, and Non-Habitat) derived from the intersection of habitat suitability categories and lek space use. Habitat suitability categories were derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California formed from the multiplicative product of the spring, summer, and winter HSI surfaces. Summary of steps to create Management Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons. Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and season using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. For each season, subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell. The three seasonal HSI rasters were then multiplied to create a composite annual HSI. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). SPACE USE INDEX CALCULATION: Updated lek coordinates and associated trend count data were obtained from the 2015 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/20/2015). Leks count data from the California side of the Buffalo-Skedaddle and Modoc PMU's that contributed to the overall space-use model were obtained from the Western Association of Fish and Wildlife Agencies (WAFWA), and included count data up to 2014. We used NDOW data for border leks (n = 12), and WAFWA data for those fully in California and not consistently surveyed by NDOW. We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years (through the 2014 breeding season). Pending leks comprised leks without consistent breeding activity during the prior 3 - 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’, or newly discovered leks with at least 2 males. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2011 - 2015) for the number of male grouse (or NDOW classified 'pseudo-males' if males were not clearly identified but likely) attending each lek was calculated. Compared to the 2014 input lek dataset, 36 leks switched from pending to inactive, and 74 new leks were added for 2015 (which included pending ‘new’ leks with one year of counts. A total of 917 leks were used for space use index calculation in 2015 compared to 878 leks in 2014. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2011 - 2015) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was re-scaled between zero and one by dividing by the maximum pixel value. The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 - 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was re-scaled between zero and one by dividing by the maximum cell value. A Spatial Use Index (SUI) was calculated by taking the average of the lek utilization distribution and non-linear distance-to-lek rasters in ArcGIS, and re-scaled between zero and one by dividing by the maximum cell value. The volume of the SUI at cumulative at specific isopleths was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the Nevada state boundary. MANAGEMENT CATEGORIES: The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was categorized into 4 classes: High, Moderate, Low, and Non-Habitat as described above, and intersected with the space use index to form the following management categories . 1) Core habitat: Defined as the intersection between all suitable habitat (High, Moderate, and Low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality habitat

SAGE-Var is a follow-up to the SAGE and SAGE-SMC Legacy programs. The SAGE-Var program obtained 4 epochs of photometry at 3.6 and 4.5 microns covering the bar of the Large Magellanic Cloud (LMC) and the central region of the Small Magellanic Cloud (SMC) in order to probe the variability of extremely red sources missed by variability surveys conducted at shorter wavelengths, and to provide additional epochs of observation for known variables. The 6 total epochs of observations probe infrared variability on 15 different timescales ranging from 20 days to 5 years.

THIS RESOURCE IS NO LONGER IN SERVICE, documented September 6, 2016. The Unified Library Database, or UniLib, takes a library-level view of the EST and SAGE libraries present in NCBI's dbEST, UniGene and SAGEmap resources. This database was initially developed by NCBI in order to track and annotate libraries being generated by NCI's CGAP project. The query bar of the UniLib Library browser provides the most friendly way to navigate through these libraries. When matches to the Library browser query are returned as summaries, full library records can be retrieved through the linked Record retriever.

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on March 24, 2014. A resource for gene expression studies, storing highly curated MIAME-compliant studies (i.e. experiments) employing a variety of technologies such as filter arrays, 2-channel microarrays, Affymetrix chips, SAGE, MPSS and RT-PCR. Data were available for querying and downloading based on the MGED ontology, publications or genes. Both public and private studies (the latter viewable only by users having appropriate logins and permissions) were available from this website. Specific details on protocols, biomaterials, study designs, etc., are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format. RAD is part of a more general Genomics Unified Schema (http://gusdb.org), which includes a richly annotated gene index (http://allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. NOTE: Due to changes in technology and funding, the RAD website is no longer available. RAD as a schema is still very much active and incorporated in the GUS (Genomics Unified Schema) database system used by CBIL (EuPathDB, Beta Cell Genomics) and others. The schema for RAD can be viewed along with the other GUS namespaces through our Schema Browser.

To accelerate the process of tumor antigen discovery, we generated a publicly available Human Potential Tumor Associated Antigen database (HPtaa) with pTAAs identified by insilico computing. 3518 potential targets have been included in the database, which is freely available to academic users. It successfully screened out 41 of 82 known Cancer-Testis antigens, 6 of 18 differentiation antigen, 2 of 2 oncofetal antigen, and 7 of 12 FDA approved cancer markers that have Gene ID, therefore will provide a good platform for identification of cancer target genes. This database utilizes expression data from various expression platforms, including carefully chosen publicly available microarray expression data, GEO SAGE data, Unigene expression data. In addition, other relevant databases required for TAA discovery such as CGAP, CCDS, gene ontology database etc, were also incorporated. In order to integrate different expression platforms together, various strategies and algorithms have been developed. Known tumor antigens are gathered from literature and serve as training sets. A total tumor specificity penalty was computed from positive clue penalty for differential expression in human cancers, the corresponding differential ratio, and normal tissue restriction penalty for each gene. We hope this database will help with the process of cancer immunome identification, thus help with improving the diagnosis and treatment of human carcinomas.

Invasive annual grasses (IAGs) present a persistent challenge for the ecological management of rangelands, particularly the imperiled sagebrush biome in western North America. Cheatgrass (Bromus tectorum), medusahead (Taeniatherum caput-medusae), and Ventenata spp. are spreading across sagebrush rangelands and already occupy at least 200,000 kilometers squared (km sq.) of the intermountain west. The loss and degradation of native plant communities caused by IAGs threatens the persistence of sagebrush obligate species such as the Greater Sage-grouse (Centrocercus urophasianus) and pygmy rabbit (Brachylagus idahoensis). IAGs convert sagebrush landscapes to monocultures of non-native grasslands that substantially increase the risk of wildfire and degrade important ecosystem services including forage production and quality, soil stability, and carbon sequestration. As a result, the economic consequences of IAGs are substantial. Successful management of IAG invasions depends on extensive and accurate geospatial data that is accessible and interpretable by those charged with managing landscapes across the sagebrush biome. The past decade has seen a rapid growth in these products, yet researchers and managers both report a persistent research-implementation gap between the availability of products and their application. To address this problem, we first conducted a systematic literature review to inventory spatial products released over the past decade that map cheatgrass, medusahead, and Ventenata within the western U.S. at regional and national scales. We then developed a series of informational data resources to guide land managers in understanding and selecting the best available spatial data for their management needs. This Excel-readable .xlsx file version database product represents a searchable, filterable, and sortable collection of summary information for each IAG spatial data product, published from January 2010 to February 2021, we summarized as part of our review. An additional, machine-readable .csv file version of the database is also available for users.

Rat Strain

The Spitzer Space Telescope Survey of the Large Magellanic Cloud Legacy Project Surveying the Agents of a Galaxy's Evolution (SAGE) traces the life cycle of observable matter that drives the evolution of a galaxy's appearance. SAGE has revealed over 6 million sources including ~150,000 evolved stars, ~50,000 young stellar objects and the diffuse interstellar medium with column densities > 1.2×1021 cm -2. The data will provide fundamental insights into the physical processes of the interstellar medium, the formation of new stars and the injection of mass by evolved stars and their relationships on the galaxy-wide scale of the Large Magellanic Cloud.In comparison to the catalog, the archive has more source fluxes (fewer nulled wavelengths) and some more sources but these additions have more uncertainty associated with them. For the catalog, the S/N must be greater than [6, 6, 6, 10] for IRAC bands [3.6um], [4.5um], [5.8um] and [8.0um] for the flux to appear in the corresponding wavelength column. Whereas for the archive, the S/N must be greater than [5, 5, 5, 5] for IRAC bands [3.6um], [4.5um], [5.8um] and [8.0um]. For the catalog, sources with neighbors within a 2" radius are excluded (culled). For the archive, sources within a 0.5" radius are excluded. The difference in criteria for the catalog and archive creation is more complex and described in Section 5.2 of the SAGE Data Delivery Document.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. This database is intended to serve as a learning tool to obtain curated information for the design of microarray targets to scan collecting duct tissues (human, rat, mouse). The database focuses on regulatory and transporter proteins expressed in the collecting duct, but when collecting duct proteins are a member of a larger family of proteins, common additional members of the family are included even if they have not been demonstrated to be expressed in the collecting duct. An Internet-accessible database has been devised for major collecting duct proteins involved in transport and regulation of cellular processes. The individual proteins included in this database are those culled from literature searches and from previously published studies involving cDNA arrays and serial analysis of gene expression (SAGE). Design of microarray targets for the study of kidney collecting duct tissues is facilitated by the database, which includes links to curated base pair and amino acid sequence data, relevant literature, and related databases. Use of the database is illustrated by a search for water channel proteins, aquaporins, and by a subsequent search for vasopressin receptors. Links are shown to the literature and to sequence data for human, rat, and mouse, as well as to relevant web-based resources. Extension of the database is dynamic and is done through a maintenance interface. This permits creation of new categories, updating of existing entries, and addition of new ones. CDDB is a database that organizes lists of genes found in collecting duct tissues from three mammalian species: human, rat, and mouse. Proteins are divided into categories by family relationships and functional classification, and each category is assigned a section in the database. Each section includes links to the literature and to sequence information for genes, proteins, expressed sequence tags, and related information. The user can peruse a section or use a search engine at the bottom of the web page to search the database for a name or abbreviation or for a link to a sequence. Each entry in the database includes links to relevant papers in the kidney and collecting duct literature. It uses links to PubMed to generate MEDLINE searches for retrieval of references. In addition, each entry includes links to curated sequence data available in LocusLink. Individual links are made to sequence and protein data for human, rat, and mouse. Links are then added as curated sequences become available for proteins identified in the renal collecting duct and for proteins identified in kidney and similar in function or homologous to proteins identified in the collecting duct.

This dataset contains:

• 84912 images collected from our camera (a 6MP Fisheye Sky facing camera with 192 degrees horizontal and vertical angle view). Each image is a JPEG image, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 2048x2048, components 3. Images were recorded on October–December 2021. The dataset is clusterized with images belonging to 20 different clusters/classes. We used PCA_MLE plus SOM to produce such clusterization. Files Cluster_*.zip
• 11 model checkpoints from the training process. Files checkpoint*.pth and log.txt
• Output feature vectors and their corresponding file names resulting from inference . File features.zip
• The clusterization produced on WSISEG-Database. File README.txt