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12 results for “modern samples”
Evolutionary history of the Galápagos Rail revealed by ancient mitogenomes and modern samples
<p>Beast v. 2.6.3 input (<em>.xml</em>) files and output (<em>.log</em> and <em>.trees</em>) files for phylogenetic analyses of rails, used to determined the evolutionary history of the Galápagos Rail <em>Laterallus spilonota</em>. There are two main datasets: coding sequences of the mitochondrial genome ('mtCDS'), partitioned per codon position, and a two mitochondrial/one nuclear marker dataset ('2mt1nc'). For each of the datasets, separate runs have been made in which the fossil calibration of Rallidae is applied to the stem of the present-day family ('calRallidaeStem') or the crown node ('calRallidaeCrown), and finally all runs have been replicated with three different starting seeds ('seed_NNNNNNNNN', with the different seeds 123456789, 456789123, and 789123456).</p> <p>We provide raw output (<em>.log</em> and <em>.raw.trees</em>) as well as maximum clade credibility ('mcc') trees (<em>.mcc.trees</em>), calculated after discarding 10% of the trees as burn-in, using median ('heights_median') or mean ('heights_mean') node heights as estimated node age.</p> <p>The runs used for Table 1 (and Figure 2) in the accompanying paper are:</p> <ul> <li>Dataset mtCDS, Rallidae calibration of stem: seed 123456789</li> <li>Dataset mtCDS, Rallidae calibration of crown: seed 456789123 </li> <li>Dataset 2mt1nc, Rallidae calibration of stem: seed 789123456</li> <li>Dataset 2mt1nc, Rallidae calibration of crown: seed 123456789</li> </ul> <p>This version of the data includes <em>Pellornis mikkelseni</em> among the fossils making up the calibration distribution for crown Gruiformes. In a previous version of this data deposit, that data point was represented by <em>Messelornis cristata </em>(see accompanying paper).</p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture.</p> <p>Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1 and <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4, as described in <a href="http://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. The files were compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>import json site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
Pond diatom communities from the McMurdo Sound region of Antarctica, assessed from modern samples (2012-2016) and from historical samples collected during Shackleton’s Nimrod Expedition (1908-1909)
We performed a field survey to characterize diatom communities living in the benthic microbial mats of ponds across the McMurdo Sound region of Antarctica. Samples were collected during the Austral summers of 2012-13, 2013-14, and 2015-16 from the McMurdo Dry Valleys, Cape Royds, and from Hut Ridge near McMurdo Station. We also characterized pond diatom communities in samples collected on various dates from 1908-1909 during Ernest Shackleton’s Nimrod Expedition to compare against modern samples. Historical samples were taken from Cape Royds and the Stranded Moraines, and were analyzed from slides stored at the Natural History Museum, London. This data package includes relative abundance data for diatom species found in these modern and historical samples.
Diatom relative abundances for LakePulse lakes (major taxa and full dataset) modern and pre-industrial samples
<p>Raw diatom relative abundance data and major species relative abundances used in: Griffiths K., Duda M. P., Antoniades D., Smol J. P., Gregory-Eaves I. 2024. Diatom species responses along gradients of dissolved inorganic carbon, total phosphorus, and lake depth from lakes across Canada. Journal of Phycology</p>
Sponge silicon isotope analyses from Triassic–Jurassic boundary samples (Levanto and Malpaso, Perú) and individual spicule analyses on modern sponges (analyzed via SIMS)
Open the record for dataset details and reuse information.
Supplementary Data - Haplotype-based inference of recent effective population size in modern and ancient DNA samples
<p>This repository contains the simulated data analyzed in our manuscript titled "Haplotype-based Inference of Recent Effective Population Size in Modern and Ancient DNA Samples".The data is split into 7 datasets. </p><p><strong>- demographies.tar.gz</strong>: the simulated demographic models.<br><strong>- modern_data.tar.gz:</strong> simulated SNP-araray data. The genotypes were simulated under 4 demographic histories (see demographies.tar.gz) for 256 samples and under 10 different random seeds (Replicate 1-10). Additionally, Replicates 11 and 12 include simulations at larger sample sizes. Note that the results at lower sample sizes can be obtained by keeping the first N samples of the simulated files.<br><strong>- true_ibd.tar.gz :</strong> This dataset contains the IBD segments from the simulated modern_data (ground truth from ARGON simulator).<br><strong>- ancient_data.tar.gz</strong> dataset contains simulated aDNA data. The dataset contains data simulated at different coverages (MISSING_$M, where $M = exp(-coverage)), sample sizes, demographic models, and random seeds. Split into three parts. See below for instructions on how to reconstruct the dataset. <br><strong>- structure.tar.gz</strong> and<strong> admixture.tar.gz :</strong> contain the data simulated under more complex demographic histories involving 2 isolated populations (structure) or a single population undergoing a recent admixture event (admixtuer.tar.gz). The manuscript provides more details about the demographic histories.<br>- <strong>imputed.tar.gz</strong> dataset contains simulated imputed aDNA data. Each region was simulated independently. Each folder corresponds to a chromosome arm and contains:<br> - data.anc_array.npy: list of the SNPs included in the analysis (simulating a 1240k array)<br> - data.glimpse.vcf.gz: phased ancient population data as phased by GLIMPSE v1<br> - data.glimpse.vcf.gz.csi: index for the above VCF file<br> - data.imputed.vcf.gz: imputed ancient population data (unphased, including dosages and genotype posteriors)<br> - data.imputed.vcf.gz.csi: index file for the above VCF<br> - data.map.gz: genetic map for GLIMPSE (tab-separated format: pos chr cM)<br> - data.ref.tsv.gz: file used for calculating genotype likelihoods with BCFtools mpileup command to use as input for GLIMPSE (format: chromosome position ref_allele,alt_allele)<br> - data.ref.tsv.gz.tbi: index for the above file<br> - data.ref.vcf.gz: simulated sequencing data from the reference panel<br> - data.ref.vcf.gz.csi: index file for the above VCF<br> - data.target_ground_truth.vcf.gz: simulated sequencing data for the ancient population (ground truth)<br> - datalist.txt: list of genotype likelihood files for each target individual (to be used for merging into a single file)<br> - data.temp.map.gz: genetic map for data creation with msprime simulator (format: chr position rate(cM/Mb) cM)<br> - data.tree -> msprime simulator output containing all samples, both reference panel samples and ancient population samples<br> - data.vcf.gz: VCF containing ground truth sequencing data, phased genotypes for reference panel samples and for ancient population samples<br> - dataref.fa.fai: index for reference fasta file used during reads creation</p><p> </p><p>Note that some of the datasets have been split into multiple parts, for example admixed.tar.gz has been split into three different parts admixed.tar.gz-part-aa, admixed.tar.gz-part-ab, and admixed.tar.gz-part-ac<br>You can get the data by typing:</p><p>cat admixed.tar.gz.part-* > admixed.tar.gz</p><p><br> </p>
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Data from: High-throughput SNP genotyping of historical and modern samples of five bird species via sequence capture of ultraconserved elements
Sample availability limits population genetics research on many species, especially taxa from regions with high diversity. However, many such species are well represented in museum collections assembled before the molecular era. Development of techniques to recover genetic data from these invaluable specimens will benefit biodiversity science. Using a mixture of freshly preserved and historical tissue samples, and a sequence capture probe set targeting >5000 loci, we produced high-confidence genotype calls on thousands of single nucleotide polymorphisms (SNPs) in each of five South-East Asian bird species and their close relatives (N = 27–43). On average, 66.2% of the reads mapped to the pseudo-reference genome of each species. Of these mapped reads, an average of 52.7% was identified as PCR or optical duplicates. We achieved deeper effective sequencing for historical samples (122.7×) compared to modern samples (23.5×). The number of nucleotide sites with at least 8× sequencing depth was high, with averages ranging from 0.89 × 106 bp (Arachnothera, modern samples) to 1.98 × 106 bp (Stachyris, modern samples). Linear regression revealed that the amount of sequence data obtained from each historical sample (represented by per cent of the pseudo-reference genome recovered with ≥8× sequencing depth) was positively and significantly (P ≤ 0.013) related to how recently the sample was collected. We observed characteristic post-mortem damage in the DNA of historical samples. However, we were able to reduce the error rate significantly by truncating ends of reads during read mapping (local alignment) and conducting stringent SNP and genotype filtering.
Data from: High-throughput SNP genotyping of historical and modern samples of five bird species via sequence capture of ultraconserved elements
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Use of a Modern Breath Sampling System (the Pneumopipe® Combined With an Array of E-nose Sensors) for the Prediction of Treatment Response in Persistent Asthmatic Children
ClinicalTrials.gov study NCT04326530. IPD Sharing: NO. Countries: 0. Publications: 1.
The lead isotope data of unalloyed copper artifacts of the Shang and Eastern Zhou dynasties, and copper ore samples of modern copper deposits in Huili.
<p>The lead isotope data of unalloyed copper artifacts of the Shang and Eastern Zhou dynasties, and copper ore samples of modern copper deposits in Huili. And the results of four runs for the SRM981 determination and published values.</p>
Modern sedimentation patterns in Lake Issyk Kul (Kyrgyzstan), derived from surface sediment and inlet stream samples
<p>Dataset of surface samples from Lake Issyk Kul, Kyrgyzstan</p>
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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.