The MassGIS General Reference Map contains a variety of features, all from the MassGIS database. The map was designed by MassGIS staff in ESRI's ArcMap 10.x software and was cached (pre-rendered) into tile layers for the Web using ArcGIS Server 10.x. The caching process greatly speeds the display of all basemap features. The tile layers are hosted at MassGIS' ArcGIS Online organizational account.For full details see http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/online-mapping/massgis-basemap.html.

Pre-built Symphony reference objects that can be downloaded and used to map new query datasets.

The Symphony algorithm is used to perform reference mapping to these atlases.

• Preprint: https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2
• Usage: https://github.com/immunogenomics/symphony

References available for download:

1. 10x PBMCs Atlas (pbmcs_10x_reference.rds)
2. Pancreatic Islet Cells Atlas (pancreas_plate-based_reference.rds)
3. Fetal Liver Hematopoiesis Atlas (fetal_liver_reference_3p.rds)
4. Healthy Fetal Kidney Atlas (kidney_healthy_fetal_reference.rds)
5. T cell CITE-seq atlas (tbru_ref.rds)
6. Cross-tissue Fibroblast Atlas (see here)
7. Cross-tissue Inflammatory Immune Atlas (here)
8. Tabula Muris Senis (FACS) Atlas (TMS_facs_reference.rds)

To read in a reference into R, one may simply execute: reference = readRDS('path/to/reference_name.rds')

Note: To be able to map query datasets into the reference UMAP coordinates, you must also download the corresponding 'uwot_model' file and set the reference$save_uwot_path.

We have developed ProjecTILs, a computational approach to project new data sets into a reference map of T cells, enabling their direct comparison in a stable, annotated system of coordinates. Because new cells are embedded in the same space of the reference, ProjecTILs enables the classification of query cells into annotated, discrete states, but also over a continuous space of intermediate states. By comparing multiple samples over the same map, and across alternative embeddings, the method allows exploring the effect of cellular perturbations (e.g. as the result of therapy or genetic engineering) and identifying genetic programs significantly altered in the query compared to a control set or to the reference map. We illustrate the projection of several data sets from recent publications over two cross-study murine T cell reference atlases: the first describing tumor-infiltrating T lymphocytes (TILs), the second characterizing acute and chronic viral infection.To construct the reference TIL atlas, we obtained single-cell gene expression matrices from the following GEO entries: GSE124691, GSE116390, GSE121478, GSE86028; and entry E-MTAB-7919 from Array-Express. Data from GSE124691 contained samples from tumor and from tumor-draining lymph nodes, and were therefore treated as two separate datasets. For the TIL projection examples (OVA Tet+, miR-155 KO and Regnase-KO), we obtained the gene expression counts from entries GSE122713, GSE121478 and GSE137015, respectively.Prior to dataset integration, single-cell data from individual studies were filtered using TILPRED-1.0 (https://github.com/carmonalab/TILPRED), which removes cells not enriched in T cell markers (e.g. Cd2, Cd3d, Cd3e, Cd3g, Cd4, Cd8a, Cd8b1) and cells enriched in non T cell genes (e.g. Spi1, Fcer1g, Csf1r, Cd19). Dataset integration was performed using STACAS (https://github.com/carmonalab/STACAS), a batch-correction algorithm based on Seurat 3. For the TIL reference map, we specified 600 variable genes per dataset, excluding cell cycling genes, mitochondrial, ribosomal and non-coding genes, as well as genes expressed in less than 0.1% or more than 90% of the cells of a given dataset. For integration, a total of 800 variable genes were derived as the intersection of the 600 variable genes of individual datasets, prioritizing genes found in multiple datasets and, in case of draws, those derived from the largest datasets. We determined pairwise dataset anchors using STACAS with default parameters, and filtered anchors using an anchor score threshold of 0.8. Integration was performed using the IntegrateData function in Seurat3, providing the anchor set determined by STACAS, and a custom integration tree to initiate alignment from the largest and most heterogeneous datasets.Next, we performed unsupervised clustering of the integrated cell embeddings using the Shared Nearest Neighbor (SNN) clustering method implemented in Seurat 3 with parameters {resolution=0.6, reduction=”umap”, k.param=20}. We then manually annotated individual clusters (merging clusters when necessary) based on several criteria: i) average expression of key marker genes in individual clusters; ii) gradients of gene expression over the UMAP representation of the reference map; iii) gene-set enrichment analysis to determine over- and under- expressed genes per cluster using MAST. In order to have access to predictive methods for UMAP, we recomputed PCA and UMAP embeddings independently of Seurat3 using respectively the prcomp function from basic R package “stats”, and the “umap” R package (https://github.com/tkonopka/umap).

Pre-built Symphonypy reference objects that can be downloaded and used to map new query datasets. The same data as in https://zenodo.org/record/5090425, but for Python port for Symphony.

The Symphony algorithm is used to perform reference mapping to these atlases.

• Paper: https://www.nature.com/articles/s41467-021-25957-x
• Usage: https://github.com/potulabe/symphonypy

References available for download:

1. 10x PBMCs Atlas (pbmcs_10x_reference.h5ad)
2. Pancreatic Islet Cells Atlas (pancreas_plate-based_reference.h5ad)
3. Fetal Liver Hematopoiesis Atlas (fetal_liver_reference_3p.h5ad)
4. Healthy Fetal Kidney Atlas (kidney_healthy_fetal_reference.h5ad)
5. T cell CITE-seq atlas (tbru_ref.h5ad)
6. Cross-tissue Inflammatory Immune Atlas (zhang_reference.h5ad)
7. Tabula Muris Senis (FACS) Atlas (TMS_facs_reference.h5ad)

These data were compiled for the use of training natural feature machine learning (GeoAI) detection and delineation. The natural feature classes include the Geographic Names Information System (GNIS) feature types Basins, Bays, Bends, Craters, Gaps, Guts, Islands, Lakes, Ridges and Valleys, and are an areal representation of those GNIS point features. Features were produced using heads-up digitizing from 2018 to 2019 by Dr. Sam Arundel's team at the U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA, and Dr. Wenwen Li's team in the School of Geographical Sciences at Arizona State University, Tempe, Arizona, USA.

Guide to Publicly Available Demographic Data This data source guide is a reference tool describing data important to workforce professionals. We created the guide because multiple federal and state organizations provide data relevant to workforce professionals; and skillful data use requires understanding: the sources of data how often it is collected, for what years it is available, and a link to the data release dates the geographic level of analysis (state, county, etc.) the variables included in the data how to access and use the data

Whole genome sequencing enables us to ask fundamental questions about the genetic basis of adaptation, population structure, and epigenetic mechanisms, but usually requires a suitable reference genome for making sense of the sequence data. While the availability of reference genomes has significantly improvement in both taxonomic coverage and overall quality, this poses a challenge for researchers in determining which reference genome best suits their data. Here we compare the use of two different reference genomes for the three-spined stickleback (Gasterosteus aculeatus), one novel genome from a European individual and the published reference genome of a North American individual. Specifically, we investigate the impact of using a local reference versus one generated from a differentiated population on several commonly used metrics in population genomics. Through mapping genome resequencing data of 60 sticklebacks from across Europe and North America, we confirmed genome quality is an important factor in choosing a reference genome. A local reference genome did offer increased mapping efficiency and genotyping accuracy, likely stemming from the higher similarity in genome sequence and synteny. Despite comparable distributions of the metrics generated across the genome using SNP data (i.e., π, Tajima's D, and FST), window-based statistics using different references resulted in different outlier genes and enriched gene functions. In contrast, the marker-based analysis utilising DNA methylation distributions had a considerably higher overlap in outlier genes and functions when using different reference genomes. Overall, our results highlight how using a local reference genome can increase the resolution of genome scans when multiple similar-quality reference genomes are available. Such results have implications in the detection of signatures of selection.

Part of VMREFTAB, the set of Reference Tables for the VICMAP suite of products.

Because plumage coloration is frequently involved in sexual selection, for both male and female mate choice, birds with aberrant plumage should have fewer mating opportunities and thus lower reproductive output. Here we report an Eastern Bluebird (Sialia sialis) female with a brown phenotype that raised a brood of four chicks to fledging. The brown female and her mate were only related to their social offspring to the second degree and one of the offspring was a half-sibling. We propose four family tree scenarios and discuss their implications (e.g., extra-pair paternity, conspecific brood parasitism), but regardless of the tree, the brown female was able to find a mate, which may have been facilitated by the bottleneck created by the severe snowstorms in February 2021., These data are a result of low-coverage whole genome sequencing from individual Eastern bluebirds (Sialia sialis). We isolated genetic material from blood samples of bluebirds collected in northeast Arkansas, USA. We filtered the raw reads and mapped them to an existing reference (GenBank accession GCA_009812075.1) to call single nucleotide polymorphisms (SNPs). We then used the SNPs in a relatedness analysis to assess the familial relationship among samples. We also assembled the mitochondrial gene nad2 from the filtered genomic data. We used the nad2 sequences to assess maternal lineages among our samples., , # Data from: Genomic data reveal unexpected relatedness between a brown female Eastern Bluebird and her brood

These data include files related to the phylogenetic and relatedness analysis among seven samples of Eastern bluebirds (Sialia sialis). Phylogenetic files include an alignment of the mitochondrial nad2 gene and a resulting neighbor-joining distance tree generated from the APE package in R. The phylogeny shows the relationships among the different samples, most notably the maternal lineages present among the juvenile individuals. The file involved in the relatedness analysis is a Bash shell script to map nuclear genomic data from seven individual bluebirds to a reference and call single nucleotide polymorphisms (SNPs). The SNPs were used in an analysis to estimate relatedness among the individuals.

Description of the data and file structure

.fasta files and .tre files can both be viewed in a standard text editor.

nad2_alignment.fasta: A FASTA-formatted file containin...

Statistics of mapping ratio to reference genome.

Gene ontology mapping to DH pahang reference genome v2 using TrEMBL database

Result of read mapping to the reference genome sequences of Japanese eel.

Mapping and reference level information for significant mortality risk factors highlighted in this study. Category, field type, names used, and the unit information is provided. (XLSX)

Reference map

Statistics of tag mapping against reference gene and genome sequence of the chicken.

This map is suitable only for the ArcGIS Online Map Viewer. Because of viewer-specific effects, it will not render as designed in Map Viewer Classic or ArcGIS Pro.This map uses a combination of blend modes and effects with imagery, hillshade, and reference layers, to create a muted landcover-tinted natural appearance. It is appropriate for reference mapping, physical/environmental geography themes, or thematic maps where a muted representation of the natural environment provides helpful context.

Statistics of tag mapping against reference gene and genome sequence of maize.

Patients characteristics and results of self-reference mapping and ablation.

Background

The TU Wien flood mapping algorithm is a Sentinel-1-based workflow using Bayes Inference at the pixel level. The algorithm is currently deployed in global operations under the Copernicus GFM project and have been shown to work generally well. However, the current approach has overestimation issues related to imperfect no-flood probability modeling. In a recent study, we proposed and compared an Exponential Filter derived from no-flood references versus the original Harmonic Model. We have conducted experiments on seven study sites for flooded and no-flood scenarios. A full description and discussion are found in the paper: Assessment of Time-Series-Derived No-Flood Reference for SAR-based Bayesian Flood Mapping.

Methodology

• We generated no-flood references using the Exponential Filter at various T-parameter values and the original Harmonic Model as a baseline.
• Flood maps were generated using the Bayes Inference-based SAR Flood mapping algorithm implemented in Python using the Yeoda software package. Flood maps using the various no-flood references for all available Sentinel-1 image acquisitions for a selected relative orbit per study site.
• Each flood map is compared with the reference CEMS Rapid Mapping or Sentinel Asia reference dataset to generate validation/confusion maps.

Technical details

• Datasets are stored in GeoTiff format using LZW Compression.
• Files are compressed in two bundles: 1) flood maps, 2) false positive count maps, and 3) validation results.
• Files are organized and tiled following the T3 Equi7Grid tilling system at 20m x 20m resolution.
  • Folder structure: dataset/map product>(continental)subgrid>tile>files.
  • The study covers the following study sites:
    • EU E039N027T: Scotland
    • AS E054N015T3: Vietnam
    • EU E054N006T3: Greece
    • EU E051N012T3: Slovenia
    • AS E024N027T3: India
    • OC E057N117T3: Philippines
    • EU E057N024T3: Latvia
• Files are named following the Yeoda file naming convention.
• Summary Accuracy Assessment Metrics are in CSV format.

Datasets:

• Flood: flood maps generated using different parameterizations of no-flood reference.
• FP_Count: false positive count maps.
• Validation results include:
  • Confusion maps were generated from the difference between the flood maps and the rasterized CEMS Rapid Mapping reference or Sentinel Asia datasets. Summary Accuracy Assessment Metrics in CSV format.
  • ERA5-LAND daily aggregates in CSV format.
  • Root Mean Square Error time-series analysis in CSV format.
  • False Positive Rate time-series analysis in CSV format.
• *Due to storage constraints, no flood reference is available upon request.

This dataset describes the Release File structure of SNOMED CT, referred to as Release Format 2 (RF2). The US Edition of SNOMED CT is the official source of SNOMED CT for use in US healthcare systems. The US Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT.

A Simple Map Reference set is used to represent one-to-one maps between SNOMED CT concepts and codes in another terminology, classification or code system.