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1,155 results for “loci”
Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas with data for 196 markers
<p>Dataset for Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas with data for 196 markers. This file is associated with a dissertation on lodging resistance called Quantitative Trait Loci Associated with Lodging, Stem Strength, Yield, and Other Important Agronomic Traits in Dry Field Peas.</p>
gRNAs, cdna, and code used in High-throughput tagging of endogenous loci for rapid characterization of protein function
<p>Contains code and sequences used to analyze NGS reads sequencing junctions between tagged genes and linker sequences.</p>
Final filtered loci dataset for Rostroraja eglanteria
<p>Skates are benthic elasmobranchs that are highly vulnerable to incidental fishery bycatch, are discarded at sea, and are poorly accounted for in catch records. Many aspects of skate life history, such as population structure, are not well understood. Without this knowledge, their continued indiscriminate harvest may have deleterious effects on scientific, conservation, and management efforts. The Clearnose Skate (<i>Rostroraja eglanteria</i>) is seasonally migratory and widely distributed in the coastal waters of the eastern United States and in the northeastern Gulf of Mexico. This study used molecular techniques to assess the population structure of Clearnose Skate so that it may serve as a valuable biological reference point for further research and management. Specimens were collected from 2014 to 2019 by fisheries-independent surveys, and examined on broad and small geographic scales. High-throughput genotyping-by-sequencing was used to identify single nucleotide polymorphisms (SNPs), resulting in two datasets; one consisting of 8,914 loci (outlier and neutral) and the other comprised of 30 outlier loci. Results from all analyses and using both datasets indicated large and significant genetic differences between specimens from the Gulf of Mexico and specimens from the U.S East Coast. Using the outlier dataset, small but significant differences were also found among specimens from the U.S. East Coast with a subtle break near the North Carolina and South Carolina border<sub>. </sub>These genetic differences were spatially autocorrelated, indicating a genetic gradient consistent with an isolation-by-distance model. The genetic differentiation between the Gulf of Mexico and the U.S. East Coast is likely due to the physical barriers such as Florida and the Gulf Stream current, while the subtle structure along the U.S. East Coast is likely due to recent isolation caused by reduced dispersal and local temperature preferences.</p>
Data from: Phylogenomic analyses of 539 highly informative loci dates a fully resolved time tree for the major clades of living turtles (Testudines)
[No abstract entered]
Hundreds of nuclear and plastid loci yield novel insights into orchid relationships
<p>Multiple Sequence DNA alignments of the Angiosperm353 target enrichment loci and their corresponding ML gene tree phylogenies</p>
Data from: Isolation and characterization of fifteen polymorphic microsatellite loci for the citrus mealybug, Planococcus citri (Hemiptera: Pseudococcidae), and cross-amplification in two other mealybug species
[No abstract filled]
Data from: Characterization of microsatellite loci for the Gulf Coast waterdog (Necturus beyeri) using paired-end Illumina shotgun sequencing and cross-amplification in other Necturus
[No abstract filled]
Supplementary material 1 from: Hirano K, Kakui K (2022) A new brackish tanaidacean, Sinelobus kisui sp. nov. (Crustacea, Peracarida, Tanaidacea), from Japan, with a key to Sinelobus species and barcode information from two loci. Zoosystematics and Evolution 98(2): 245-256. https://doi.org/10.3897/zse.98.84818
Table S1
FIGURE 4 in A broadly sampled 3-loci plastid phylogeny of Atraphaxis (Polygoneae, Polygonoideae, Polygonaceae) reveals new taxa: III. A. kuvaevii and сryptic species in A. pungens from Southern Siberia and Northern Mongolia
FIGURE 4. Holotype of Atraphaxis kuvaevii Yurtseva sp. nov.:
FSJ Genotype FInal All Loci
<p>Microsatellite genotype data of Florida Scrub-Jay at Kennedy Space Center</p>
FIGURE 2 in Development of microsatellite loci and population genetics in the bumblebee catfish species Pseudopimelodus atricaudus and Pseudopimelodus magnus (Siluriformes: Pseudopimelodidae)
FIGURE 2 | Population structure suggested by STRUCTURE (A-C) and the Discriminant Analysis of the Principal Components (D, E) for Pseudopimelodus magnus (A, B, D), and Pseudopimelodus atricaudus (A, C, E). S1: sector 1 of the middle Cauca River; S4, S5, and S8: sectors 4, 5 and 8 of the lower Cauca River, respectively.
FIGURE 1 in Microsatellite loci development for three catfish species from northwestern South America
FIGURE 1 | Frequency of identified microsatellite loci and potentially amplifiable loci (PAL) obtained from 454 FLX sequencing for Ageneiosus pardalis, Pimelodus grosskopfii, and Sorubim cuspicaudus.
Relevant loci from genomic assembly, gene model, and interproscan annotation, for PKZILLA-B1 from B-type P. parvum strain RCC3426
<h2><strong>Version 1.0 of the <em>Prymnesium parvum</em> RCC3426 PKZILLA-B1 gene annotation (PKZILLA-B1.gff)</strong></h2> <p>Version 1.0 of the <em>P. parvum</em> RCC3426 gene annotation (PKZILLA-B1.gff), consisting of expert guided <em>ab initio</em> gene prediction of just the PKZILLA-B1 gene model on its selected loci from the draft RCC3426 genome assembly. Interproscan annotations of PKS domains are also included (see <code>interproscan-parallel.zip</code>)</p> <p>See the manuscript "Materials and Methods" section for full details.</p> <p><strong>seqkit stat statistics:</strong></p> <p>file format type num_seqs sum_len min_len avg_len max_len<br>./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.gene.fa.gz FASTA DNA 1 <strong>118,049 </strong> 118,049 118,049 118,049<br>./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.mRNA.fa.gz FASTA DNA 1 <strong>116,940</strong> 116,940 116,940 116,940<br>./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.pep.fa.gz FASTA Protein 1 <strong>38,980</strong> 38,980 38,980 38,980<br>./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.CDS.fa.gz FASTA DNA 1 116,940 116,940 116,940 116,940<br>./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indCDSnt.fa.gz FASTA DNA 16 116,940 1,140 7,308.8 14,895<br>./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indCDSpep.fa.gz FASTA Protein 16 38,980 380 2,436.3 4,965<br>./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indExon.fa.gz FASTA DNA 16 116,940 1,140 7,308.8 14,895<br>./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indIntron.fa.gz FASTA DNA 30 2,218 63 73.9 101</p> <p><strong>seqkit sum checksums:</strong></p> <p>seqkit.v0.1_DLS_k0_48447ccbb39a48a389fc726e3647a9a4 ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.CDS.fa.gz<br>seqkit.v0.1_DLS_k0_1366665c674b96f4e302f8afab53fa4e ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.gene.fa.gz<br>seqkit.v0.1_DLS_k0_101122a59afec1b41ca7f9d1a4186b52 ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indCDSnt.fa.gz<br>seqkit.v0.1_PLS_k0_404d6657b58d22da550585c3bfa06f8d ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indCDSpep.fa.gz<br>seqkit.v0.1_DLS_k0_101122a59afec1b41ca7f9d1a4186b52 ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indExon.fa.gz<br>seqkit.v0.1_DLS_k0_af7f0b217bd14bf6dff5dbca157c5cbb ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indIntron.fa.gz<br>seqkit.v0.1_DLS_k0_48447ccbb39a48a389fc726e3647a9a4 ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.mRNA.fa.gz<br>seqkit.v0.1_PLS_k0_9f280bb95c4ee9afe7a1f28975de338d ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.pep.fa.gz</p> <p><strong>seqkit sum -g checksums:</strong></p> <p>seqkit.v0.1_DLS_k0_48447ccbb39a48a389fc726e3647a9a4 ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.CDS.fa.gz<br>seqkit.v0.1_DLS_k0_1366665c674b96f4e302f8afab53fa4e ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.gene.fa.gz<br>seqkit.v0.1_DLS_k0_101122a59afec1b41ca7f9d1a4186b52 ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indCDSnt.fa.gz<br>seqkit.v0.1_PLS_k0_fced14c2e2e98c5464f20ac91aab1bde ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indCDSpep.fa.gz<br>seqkit.v0.1_DLS_k0_101122a59afec1b41ca7f9d1a4186b52 ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indExon.fa.gz<br>seqkit.v0.1_DLS_k0_af7f0b217bd14bf6dff5dbca157c5cbb ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indIntron.fa.gz<br>seqkit.v0.1_DLS_k0_48447ccbb39a48a389fc726e3647a9a4 ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.mRNA.fa.gz<br>seqkit.v0.1_PLS_k0_8eb393988e68fdfa1fe5a435eefe353b ./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.pep.fa.gz</p> <p><strong>openssl md5 checksums:</strong></p> <p>MD5(./PKZILLA-B1.gff)= b7389f45965e2b078597ceeaa2ad4d67<br>MD5(./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.CDS.fa.gz)= 7f576dbd61bd60ba1955ce46b5077e85<br>MD5(./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.gene.fa.gz)= 7dcae701dea30fbfa4508c0f01438168<br>MD5(./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indCDSnt.fa.gz)= 0fae261356f4a20629d20fccaad00d1c<br>MD5(./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indCDSpep.fa.gz)= d0081800e314b9f99c0fa759d4e80187<br>MD5(./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indExon.fa.gz)= 79b6b942b61fcab1f14682f38e80cf41<br>MD5(./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.indIntron.fa.gz)= c2055a64ebcf332c7c6bb28c57762c94<br>MD5(./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.mRNA.fa.gz)= 7f576dbd61bd60ba1955ce46b5077e85<br>MD5(./PKZILLA-B1.gff__PKZILLA-1-on-B.loci.nt.rename.consensus.fa.pep.fa.gz)= 2f01d8cf130b343076e1d9197e6041ab</p> <p>MD5(./interproscan-parallel.zip)= 595ec69a0d77e8c96c7aa29ea913532b</p>
Figure 3 from: Li Y, Lin L-S, Gao T-X (2018) Genetic signatures of polymorphic microsatellite loci in the Ambiguous silver pomfret, Pampus argenteus (Teleostei, Stromateidae). ZooKeys 810: 139-151. https://doi.org/10.3897/zookeys.805.25602
Figure 3 3D-FCA showing relationships among six populations of P.argenteus based on seven microsatellite loci. Abbreviations: SO: Sonmiani Bay, OR: Ormara, PS: Pasni, KW: Kuwait, TW: Taiwan, XM: Xiamen.
Figure 4 from: Li Y, Lin L-S, Gao T-X (2018) Genetic signatures of polymorphic microsatellite loci in the Ambiguous silver pomfret, Pampus argenteus (Teleostei, Stromateidae). ZooKeys 810: 139-151. https://doi.org/10.3897/zookeys.805.25602
Figure 4 Results of the STRUCTURE analysis from seven microsatellite loci in P.argenteus (K = 2). Vertical lines are proportional to the probability of individual membership in the simulated cluster. Abbreviations: SO: Sonmiani Bay, OR: Ormara, PS: Pasni, KW: Kuwait, TW: Taiwan, XM: Xiamen.
Figure 2 from: Li Y, Lin L-S, Gao T-X (2018) Genetic signatures of polymorphic microsatellite loci in the Ambiguous silver pomfret, Pampus argenteus (Teleostei, Stromateidae). ZooKeys 810: 139-151. https://doi.org/10.3897/zookeys.805.25602
Figure 2 The UPGMA tree based on (δμ)2 genetic distance of six P.argenteus populations. Abbreviations: SO: Sonmiani Bay, OR: Ormara, PS: Pasni, KW: Kuwait, TW: Taiwan, XM: Xiamen.
Linked collectors and determiners for: Fungal 28S Ribosomal RNA (LSU) RefSeq Targeted Loci Project..
Natural history specimen data linked to collectors and determiners held within, "Fungal 28S Ribosomal RNA (LSU) RefSeq Targeted Loci Project.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/bf3f09bd-3af6-45be-a2c4-bd5c285cab8a">https://bionomia.net/dataset/bf3f09bd-3af6-45be-a2c4-bd5c285cab8a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/bf3f09bd-3af6-45be-a2c4-bd5c285cab8a">https://gbif.org/dataset/bf3f09bd-3af6-45be-a2c4-bd5c285cab8a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Fungal Internal Transcribed Spacer RNA (ITS) RefSeq Targeted Loci Project.
Natural history specimen data linked to collectors and determiners held within, "Fungal Internal Transcribed Spacer RNA (ITS) RefSeq Targeted Loci Project". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9baace02-10fe-4972-b3ec-2cb647c55194">https://bionomia.net/dataset/9baace02-10fe-4972-b3ec-2cb647c55194</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9baace02-10fe-4972-b3ec-2cb647c55194">https://gbif.org/dataset/9baace02-10fe-4972-b3ec-2cb647c55194</a>. Formatted as a Frictionless Data package.
Data from: Genetic mapping of novel loci affecting canine blood phenotypes
Since the publication of the dog genome and the construction of high-quality genome- wide SNP arrays, thousands of dogs have been genotyped for disease studies. For many of these dogs, additional clinical phenotypes are available, such as hematological and clinical chemistry results collected during routine veterinary care. Little is known about the genetic basis of variation in blood phenotypes, but this variation may play an important role in the etiology and progression of many diseases. From a cohort of dogs that had been previously genotyped on a semi-custom Illumina CanineHD array for various genome-wide association studies (GWAS) at Cornell University Hospital for Animals, we chose 353 clinically healthy, adult dogs for our analysis of clinical pathologic test results (14 hematological tests and 25 clinical chemistry tests). After correcting for age, body weight and sex, genetic associations were identified for amylase, segmented neutrophils, urea nitrogen, glucose, and mean corpuscular hemoglobin. Additionally, a strong genetic association (P = 8.1×10-13) was evident between a region of canine chromosome 13 (CFA13) and alanine aminotransferase (ALT), explaining 23% of the variation in ALT levels. This region of CFA13 encompasses the GPT gene that encodes the transferase. Dogs homozygous for the derived allele exhibit lower ALT activity, making increased ALT activity a less useful marker of hepatic injury in these individuals. Overall, these associations provide a roadmap for identifying causal variants that could improve interpretation of clinical blood tests and understanding of genetic risk factors associated with diseases such as canine diabetes and anemia, and demonstrate the utility of holistic phenotyping of dogs genotyped for disease mapping studies.
Data from: Paths to selection on life history loci in different natural environments across the native range of Arabidopsis thaliana
Selection on quantitative trait loci (QTL) may vary among natural environments due to differences in the genetic architecture of traits, environment-specific allelic effects or changes in the direction and magnitude of selection on specific traits. To dissect the environmental differences in selection on life history QTL across climatic regions, we grew a panel of interconnected recombinant inbred lines (RILs) of Arabidopsis thaliana in four field sites across its native European range. For each environment, we mapped QTL for growth, reproductive timing and development. Several QTL were pleiotropic across environments, three colocalizing with known functional polymorphisms in flowering time genes (CRY2, FRI and MAF2-5), but major QTL differed across field sites, showing conditional neutrality. We used structural equation models to trace selection paths from QTL to lifetime fitness in each environment. Only three QTL directly affected fruit number, measuring fitness. Most QTL had an indirect effect on fitness through their effect on bolting time or leaf length. Influence of life history traits on fitness differed dramatically across sites, resulting in different patterns of selection on reproductive timing and underlying QTL. In two oceanic field sites with high prereproductive mortality, QTL alleles contributing to early reproduction resulted in greater fruit production, conferring selective advantage, whereas alleles contributing to later reproduction resulted in larger size and higher fitness in a continental site. This demonstrates how environmental variation leads to change in both QTL effect sizes and direction of selection on traits, justifying the persistence of allelic polymorphism at life history QTL across the species range.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.