These data contain Genbank Accession numbers for DNA amplified and sequenced from a subset of Environmental Samplers that were positive for Ceratocystis lukuohia and Austropuccinia by quantitative PCR (qPCR)

Amplicon sequence variants from benthic and zooplankton samples generated using COI primers

생물 유전정보 중 DNA 바코드 관련 내용으로 그에 대한 정의와 국외 및 국내 연구동향, DNA 바코드의 필요성에 대한 내용 설명 입니다.

NIH Genetic sequence database; an annotated collection of all publicly available DNA sequences.

Data were collected in August and September 2015 for analysis of bacteria communities of the Grand Calumet River and associated shorelines. Water samples were collected on three occasions corresponding to one rain-related (wet) events and two non-rain (dry) events. Water samples were collected in the Grand Calumet River, at the mouth of the river, at offshore locations around the peninsular impoundment and at shoreline locations: Jeorse Park (East Chicago, Indiana), Whihala (Whiting, Indiana), and 63rd Street (Chicago, Illinois) beaches. Samples were collected in triplicate, and water was filtered at the USGS Lake Michigan Ecological Research Station. After DNA extraction, samples were analyzed using 16S rRNA sequencing using Illumina sequencing. Taxonomic identification was assigned for communities in each sample, at multiple taxonomic levels (Kingdom, Phylum, Class, Order, Family, Genus). Water was also analyzed for E. coli bacteria and turbidity, at the USGS laboratory. Hydrological conditions corresponding to the days of sample collection were obtained from publicly available USGS information (NWIS-National Water Information System). The coordinate file includes information regarding sampling locations and their corresponding latitude and longitudes. The Data file include E. coli densities, laboratory turbidity measurements, and NWIS hydrological data. The raw metagenomic data can be accessed at the NCBI repository under the biproject accession PRJNA541325: https://www.ncbi.nlm.nih.gov/sra/PRJNA541325

This dataset contains sample information about the origin of the Desmognathus wrighti and Plethodon jordani salamanders collected from Great Smoky Mountains National Park and sequenced, and their corresponding SNP genotype data. These data were used to examine patterns of genomic structure of these two salamander species within Great Smoky Mountains National Park. The raw sequence data are archived in the GenBank Bioproject PRJNA1062342 at: https://www.ncbi.nlm.nih.gov/bioproject/

The data release details the samples, methods, and raw data used to generate high-quality genome assemblies for greater sage-grouse (Centrocercus urophasianus), white-tailed ptarmigan (Lagopus leucura), and trumpeter swan (Cygnus buccinator). The raw data have been deposited in the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI), the authoritative repository for public biological sequence data, and are not included in this data release. Instead, the accessions that link to those data via the NCBI portal (www.ncbi.nlm.nih.gov) are provided herein. The release consists of a single file, sample.metadata.txt, which maps NCBI accessions to the samples sequenced and the different types of sequencing performed to generate the assemblies and annotate their gene features.

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species’ conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse (Centrocercus urophasianus) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., fine-scale) with low genetic diversity relative to regional units (i.e., coarse-scale), further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity for hierarchically nested populations. We developed a spatial data set to display genetic diversity values and conservation concern information from a Genetic Warning System (GWS) for population monitoring of greater sage-grouse, as described in Zimmerman et al. (2022). Here we added the genetic diversity estimates (allelic richness and expected heterozygosity) and GWS information as attributes to the relevant fine-scale (level 2) and coarse-scale (level 13) previously developed hierarchically nested population clusters (O’Donnell et al. 2019, O’Donnell et al. 2022). The GWS incorporates population trend decline watches and warnings from the Targeted Annual Warning System (TAWS) for greater sage-grouse as reported in Coates et al. (2021) to further refine degree of conservation concern.

Genetic variation is a well-known indicator of population fitness yet is not typically included in monitoring programs for sensitive species. Additionally, most programs monitor populations at one scale, which can lead to potential mismatches with ecological processes critical to species’ conservation. Recently developed methods generating hierarchically nested population units (i.e., clusters of varying scales) for greater sage-grouse (Centrocercus urophasianus) have identified population trend declines across spatiotemporal scales to help managers target areas for conservation. The same clusters used as a proxy for spatial scale can alert managers to local units (i.e., fine-scale) with low genetic diversity relative to regional units (i.e., coarse-scale), further facilitating identification of management targets. We developed a genetic warning system utilizing previously developed hierarchical population units to identify management-relevant areas with low genetic diversity within the greater sage-grouse range. Within this warning system we characterized conservation concern thresholds based on values of genetic diversity for hierarchically nested populations. We developed a spatial data set to display genetic diversity values and conservation concern information from a Genetic Warning System (GWS) for population monitoring of greater sage-grouse, as described in Zimmerman et al. (2022). Here we added the genetic diversity estimates (allelic richness and expected heterozygosity) and GWS information as attributes to the relevant fine-scale (level 2) and coarse-scale (level 13) previously developed hierarchically nested population clusters (O’Donnell et al. 2019, O’Donnell et al. 2022). The GWS incorporates population trend decline watches and warnings from the Targeted Annual Warning System (TAWS) for greater sage-grouse as reported in Coates et al. (2021) to further refine degree of conservation concern.

Biological indicator taxa have long been used for integrative assessments of water quality, particularly benthic invertebrate groups such as arthropods. While standardized protocols have been developed to calculate 'biological index' scores based on the abundances of these taxa, such systems are challenging to implement at large scales due to the sampling effort required, taxonomic expertise needed, and the need for repeated sampling to reliably discriminate sites. Many of the same taxa detected by traditional surveys can also be detected by genetic analysis of environmental DNA (eDNA), potentially allowing for an alternative formulation of biological indexes that might be faster and more economical to produce. The current data were produced to evaluate eDNA-derived biological indexes at sites within the Potomac River watershed of the eastern United States, specifically within units of the National Park Service for which previous biological assessment data were available. This data release consists of five files: 1. sample.metadata.txt, which contains sampling metadata and identifiers linking to sample-derived sequence data that has been deposited in the Sequence Read Archive of the National Center for Biotechnology Information (NCBI). This database is authoritative and comprehensive for sharing high-throughput sequence data produced with public funds. All accessions listed in the file can be searched to retrieve sample and sequence information at www.ncbi.nlm.nih.gov. 2. cox1.references.fasta, which contains reference sequences of the cytochrome c oxidase 1gene of arthropods (typically abbreviated cox1 or COI), identified from regional checklists. The file is a text file in FASTA format. 3. mt16S.references.fasta, which contains reference sequences of the mitochondrial 16S ribosomal RNA (mt16S) gene of arthropods identified from regional checklists. The file is a text file in FASTA format. 4. first.stage.counts.txt, which is a tab-delimited table of counts of sequences that are attributed to each taxon from each sample for the first stage of the study. Whether the taxon attribution is from the mt16S or cox1 locus is also indicated. 5. second.stage.counts.txt, which is a tab-delimited table of counts of sequences that are attributed to each taxon from each sample for the second stage of the study. Whether the taxon attribution is from the mt16S or cox1 locus is also indicated.