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3,696 results for “Reference”
txtools use cases omic references and processed data
<p><strong>Dataset</strong></p> <p>This dataset entry is meant to be downloaded programmatically while rendering the txtools_useCases.Rmd notebooks, to facilitate their replication using the provided genomic references. Processed data is also provided to show ready-to-use examples of data processed by txtools.</p> <p><strong>Abstract</strong></p> <p>We present txtools, an R package that enables the processing, analysis, and visualization of RNA-seq data at the nucleotide-level resolution, seamlessly integrating alignments to the genome with transcriptomic representation. txtools’ main inputs are BAM files and a transcriptome annotation, and the main output is a table, capturing mismatches, deletions, and the number of reads beginning and ending at each nucleotide in the transcriptomic space. txtools further facilitates downstream visualization and analyses. We showcase, using examples from the epitranscriptomic field, how a few calls to txtools functions can yield insightful and ready-to-publish results. txtools is of broad utility also in the context of structural mapping and RNA:protein interaction mapping. By providing a simple and intuitive framework, we believe that txtools will be a useful and convenient tool and pave the path for future discovery. txtools is available for installation from its GitHub repository at <a href="https://github.com/AngelCampos/txtools">https://github.com/AngelCampos/txtools</a>. </p>
Fig.ç4.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3983), scanning electron micrographs. A, Mouth cone, lateral view; B, introvert, lateral view. Abbreviations: oo, outer oral styles; sc, scalids; sp, spinoscalids; tr, trichoscalids. Digits a er the labels refer to introvert ring numbers. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç4.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3983), scanning electron micrographs. A, Mouth cone, lateral view; B, introvert, lateral view. Abbreviations: oo, outer oral styles; sc, scalids; sp, spinoscalids; tr, trichoscalids. Digits a er the labels refer to introvert ring numbers.
Reference genome choice and filtering thresholds jointly influence phylogenomic analyses
<p>Molecular phylogenies are a cornerstone of modern comparative biology and are commonly employed to investigate a range of biological phenomena, such as diversification rates, patterns in trait evolution, biogeography, and community assembly. Recent work has demonstrated that significant biases may be introduced into downstream phylogenetic analyses from processing genomic data; however, it remains unclear whether there are interactions among bioinformatic parameters or biases introduced through the choice of reference genome for sequence alignment and variant-calling. We address these knowledge gaps by employing a combination of simulated and empirical data sets to investigate to what extent the choice of reference genome in upstream bioinformatic processing of genomic data influences phylogenetic inference, as well as the way that reference genome choice interacts with bioinformatic filtering choices and phylogenetic inference method. We demonstrate that more stringent minor allele filters bias inferred trees away from the true species tree topology, and that these biased trees tend to be more imbalanced and have a higher center of gravity than the true trees. We find the greatest topological accuracy when filtering sites for minor allele count > 3–4 in our 51-taxa data sets, while tree center of gravity was closest to the true value when filtering for sites with minor allele count > 1-2. In contrast, filtering for missing data increased accuracy in the inferred topologies; however, this effect was small in comparison to the effect of minor allele filters and may be undesirable due to a subsequent mutation spectrum distortion. The bias introduced by these filters differs based on the reference genome used in short read alignment, providing further support that choosing a reference genome for alignment is an important bioinformatic decision with implications for downstream analyses. These results demonstrate that attributes of the study system and dataset (and their interaction) add important nuance for how best to assemble and filter short read genomic data for phylogenetic inference.</p>
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
refering rawdata and code of "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)"
<p>Corresponding raw data and codes that produce all relative video and imaging results for real-time monitoring the physicochemical phenomena of microfluidics, microorganism's group, and chemical reactions, supporting and verifying the research article "Ultrahigh-throughput single-pixel complex-field microscopy with frequency-comb acousto-optic coherent encoding (FACE)".</p>
Dataset accompanying "A new species of Mesolepis from the Late Carboniferous of Scotland, with especial reference to Mesolepis wardi Young"
<p>This dataset accompanies the manuscript "A new species of <em>Mesolepis </em>from the Late Carboniferous of Scotland, with especial reference to <em>Mesolepis wardi </em>Young" (<span><a href="https://doi.org/10.1017/S1755691024000094" target="_blank" rel="noopener">https://doi.org/10.1017/S1755691024000094</a>)</span> and comprises the following items: </p> <p>- <em>Mesolepis arabellae</em> GLAHM 163398/1 (part) raw data (TIFF stack, zipped)</p> <p>- <em>Mesolepis arabellae</em> GLAHM 163398/1 (part) .mcs file</p> <p><em>- Mesolepis arabellae</em> GLAHM 163398/1 (part) .ply files (zipped)</p> <p>- <em>Mesolepis arabellae</em> GLAHM 163398/2 (counterpart) raw data (TIFF stack, zipped)</p> <p><em>- Mesolepis arabellae</em> GLAHM 163398/2 (counterpart).mcs file</p> <p>- <em>Mesolepis arabellae</em> GLAHM 163398/2 (counterpart) .ply files (zipped)</p> <p>- <em>Mesolepis arabellae </em>GLAHM 163398/2 (fin region) raw data (TIFF stack, zipped)</p> <p><em>- Mesolepis arabellae</em> GLAHM 163398/2 (fin region) .mcs file</p> <p>- <em>Mesolepis arabellae </em>GLAHM 163398/2 (fin region) .ply file</p>
LD reference panel for UKB-PPP cis-pQTL
<p>NEWLOC_protein.txt includes information for 2954 cis-pQTL regions.</p> <p>Each row name in the file follows the format:</p> <p>[GeneSymbol].[GeneSymbol]_[UniProtID]_[OlinkID]_v[Version]_[PanelName]_[Number].[Chromosome]:[StartPosition]-[EndPosition];</p> <p>For example, "A1BG.A1BG_P04217_OID30771_v1_Inflammation_II_2.19:57856549-59864858"<br><br></p> <p>In the "LD.path", it includes LD files, eigenvectors, and eigen matrixes for all local regions, end by "_LDSVD.rda"</p> <p>In the "bim.path", it includes bim files for local regions, which helps to clean the summary statistics data and check if there are multiallelic or duplicated SNPs</p> <p><br><strong><br></strong></p>
Reference Sequence Library Resources - Maine-eDNA
<p>The following files and resources are associated with the Maine-eDNA Reference Library Research Group - aiming to create reference sequence library resources for researchers part of Maine-eDNA or otherwise interested in leveraging eDNA tools for research in the Gulf of Maine.</p> <p>These include:</p> <ul> <li> <p>RoughWorkflow.zip</p> </li> <ul> <li> <p>contains a NCBI scraping script to build reference databases based on an input species list, a configuration file for the script, and genbankr version - most useful for shorter species lists (time-intensive)</p> </li> </ul> <li> <p>12S_REFDB.fasta</p> </li> <ul> <li> <p>A DADA2-compliant reference library for 12S sequences, built with the RoughWorkflow based on the GNRMaineSpecies_May2024 species list</p> </li> </ul> <li> <p>COI_REFDB.fasta</p> </li> <ul> <li> <p>A DADA2-compliant reference library for COI sequences, built with the RoughWorkflow based on the GNRMaineSpecies_May2024 species list</p> </li> </ul> <li> <p>GitHub Repo - referee - <a href="https://github.com/BigelowLab/referee">https://github.com/BigelowLab/referee</a></p> </li> <ul> <li> <p>Scripts for building reference databases for the Maine-eDNA project through downloading GenBank - this workflow is suggested especially for large species lists</p> </li> </ul> <li> <p>GitHub Repo - refdbtools - <a href="https://github.com/BigelowLab/refdbtools">https://github.com/BigelowLab/refdbtools</a> </p> </li> <ul> <li> <p>R language package to assist in making eDNA reference databases</p> </li> </ul> <li> <p>SpeciesListMeta_Shareable.xlsx</p> </li> <ul> <li> <p>Describes the sources from which the GNRMaineSpecies_May2024.csv and GNRMaineTaxonomiedSpecies_May2024.csv species lists were compiled - sources not associated with a link were found as separate files and are hosted elsewhere. Species list sources are courtesy of public datasets, Maine-eDNA researchers, and collaborators</p> </li> </ul> <li> <p>GNRMaineSpecies_May2024.csv</p> </li> <ul> <li> <p>A Maine (and surrounding area) species list ran through taxize’s gnr_resolve to resolve species names and fill out taxonomy (full results)</p> </li> </ul> <li> <p>GNRMaineTaxonomiedSpecies_May2024.csv</p> </li> <ul> <li> <p>A Maine (and surrounding area) species list ran through taxize’s gnr_resolve to resolve species names and fill out taxonomy (only species results that could be resolved with taxonomy)</p> </li> </ul> </ul> <p> </p> <p>Contact Beth Y. Davis - bethy.davis4@gmail.com for questions</p> <p> </p> <p>###</p> <p>Changelog:</p> <p>May 16, 2023 (Version 1.0) - Initial upload</p> <p>July 11, 2023 (Version 1.1) - Did additional cleaning to the MaineSpeciesList_Clean file and uploaded the new version - July2023_SpeciesList</p> <p>May 20, 2024 (Version v3) - Additional cleaning to correct deduplication errors and ran taxize's gnr_resolve to resolve names and fill out taxonomy. The version update adds two files, GNRMaineSpecies_May2024.csv containing the full result of gnr_resolve, and GNRMaineTaxonomiedSpecies_May2024.csv contains only those species from the original list that could be resolved with taxonomy. The SpeciesListMeta_Shareable.csv has not been updated but is still an accurate tracker of the sources from which the species names were gained from.</p> <p>November 27, 2024 (Version 4.0) - Updated Zenodo description and added the RoughWorkflow R files, 12S and COI files</p>
reference genome used for scRNA-seq mapping with CellRanger in the method spatial-scERA
<p>The modified <em>Drosophila </em>melanogaster (dm6) reference genome used for the mapping with CellRanger in the method paper about spatial-scERA</p> <p>The genome is composed of the original genome from EnsembleMetazo website (BDGP6.46.110). An addition of 26 chromosomes (one for the plasmid construct and 25 for the tested enhancer sequences) is also present to allow for the mapping of mRNAs comming from our constructs. </p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ni-based alloy Inconel IN718
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of Ni-based alloy Inconel IN718 between room temperature and 800 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ti-6Al-4V
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of titanium alloy Ti-6Al-4V between room temperature and 400 °C in an additively manufactured variant (laser-based directed energy deposition with powder as feedstock, DED-LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of austenitic stainless steel AISI 316L
<p><span>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of austenitic stainless steel AISI 316L between room temperature and 900 °C in an additively manufactured variant (laser powder bed fusion, PBF</span><span>‑</span><span>LB/M) and from a conventional process route (hot rolled sheet). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</span></p>
TCR-DeepInsight Reference Datasets
<p>Reference single-cell TCR immune profiling datasets for the TCR-DeepInsight analysis. We have excluded two controlled-access datasets (1) AML dataset from Abbas et al. 2021 (EGAS00001004894) and (2) Kawasaki disease dataset from Wang et al., 2021 (OEP001162).</p> <ul> <li>human_gex_reference_v2.h5ad: Processed H5AD file for transcriptome features from the single-cell TCR immune profiling datasets. <strong><em>anndata.read_h5ad<br>For raw count H5AD files, please check the additional repo here: <a href="https://zenodo.org/records/17405143">https://zenodo.org/records/17405143</a></em></strong></li> <li>human_tcr_reference_v2.h5ad: Processed H5AD file for unique TCR clonotypes single-cell TCR immune profiling dataset. <strong><em>anndata.read_h5ad</em></strong></li> <li>Yi_2023_Ankylosing_Spondylitis.h5ad: Processed H5AD file from Yi <em>et al</em>., 2023 (GSE216885). <strong><em>anndata.read_h5ad</em></strong></li> <li>GSE272993_cd8_nn_labeled_FINAL.fl_tcr.match_v2_5.transfered.h5ad: Processed H5AD file from Wang et al., 2024 (GSE272993). <strong><em>anndata.read_h5ad</em></strong><em><strong><br><br></strong></em></li> <li>human_bulk_tcr_reference.parquet: PARQUET file for bulk TCRβ sequencing. <strong><em>pandas.read_parquet</em></strong> <ul> <li>human_bulk_tcr_reference.cd4.parquet: PARQUET file for bulk TCRβ sequencing for CD4-sorted T cells<br>human_bulk_tcr_reference.mait.parquet: PARQUET file for bulk TCRβ sequencing for MAIT cells<br>human_bulk_tcr_reference.treg.parquet: PARQUET file for bulk TCRβ sequencing for Treg cells<br>human_bulk_tcr_reference.cd8.parquet: PARQUET file for bulk TCRβ sequencing for CD8-sorted T cells<br><em><br></em></li> </ul> </li> <li>Yi_2023_Ankylosing_Spondylitis.h5ad. <strong><em>anndata.read_h5ad<br></em></strong>Processed dataset from K. Yi et al. Analysis of Single‐Cell Transcriptome and Surface Protein Expression in Ankylosing Spondylitis Identifies OX40 ‐Positive and Glucocorticoid‐Induced Tumor Necrosis Factor Receptor–Positive Pathogenic Th17 Cells. <em>Arthritis & Rheumatology</em> <strong>75</strong>, 1176–1186 (2023).</li> <li>GSE272993_cd8_nn_labeled_FINAL.fl_tcr.match_v2_5.transfered.h5ad. <strong><em>anndata.read_h5ad<br></em></strong>Processed dataset from K. Wang et al. Combination anti-PD-1 and anti-CTLA-4 therapy generates waves of clonal responses that include progenitor-exhausted CD8+ T cells. <em>Cancer Cell</em>, S1535610824003064 (2024).</li> </ul> <ul> <li>human_gex_reference_v2.scatlasvae.ckpt: pretrained weight state dict from scAtlasVAE model for human_gex_reference_v2.h5ad. <strong><em>torch.load</em></strong></li> <li>human_bert_pseudosequence.tcr_v2.ckpt: pretrained weight state dict from BERT for human_tcr_reference_v2.h5ad. <em><strong>torch.load</strong><br></em></li> <li><em>human_bert_pseudosequence_pca.tcr_v2.pkl: pretrained PCA weight for </em>human_tcr_reference_v2.h5ad. <em><strong>pickle.load</strong></em></li> </ul>
BAM Reference Data: Creep of Single-Crystal Ni-Based Superalloy CMSX-6
<p>This publication provides comprehensive metadata and test results of constant force creep tests according to <br>DIN EN ISO 204:2019-4 on the single crystal Ni-based superalloy CMSX-6 at T = 980 °C and initial stresses<br>between 140 MPa and 230 MPa. The tests were carried out in an accredited test laboratory using calibrated <br>measuring equipment. The data were audited and are BAM reference data.</p>
Reference Data for EMASE and GBRS
<p>The files in this repository support the analysis of genetically diverse samples (e.g., DO and CC mice) with Expectation-Maximization algorithm for Allele Specific Expression: <a href="https://github.com/churchill-lab/emase">EMASE</a>, and Genome reconstruction By RNA-Sequencing <a href="https://github.com/churchill-lab/GBRS">GBRS</a>, and the generation of reference files required for running these tools.</p> <p>These files are used in the The Jackson Laboratory <a href="https://github.com/TheJacksonLaboratory/cs-nf-pipelines/">Computational Sciences Nextflow pipelines</a>. Pipeline specific documentation is in the repository wiki for running <a href="https://github.com/TheJacksonLaboratory/cs-nf-pipelines/wiki/EMASE-Pipeline-ReadMe">EMASE</a> and/or <a href="https://github.com/TheJacksonLaboratory/cs-nf-pipelines/wiki/GBRS-Pipeline-ReadMe">GBRS</a>.</p>
How to cite & reference in Chicago (footnote) style
<p>This video shows how to cite and reference a book and a journal article in Chicago style, using style guide Cite Them Right.</p>
Reference data and analysis software for "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane"
<p>Reference data set for the single molecule co-tracking analysis presented in "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane". Corresponding author for further inquiries:</p> <p>Prof. Dr. Jacob Piehler</p> <p>University of Osnabrück, Department of Biology/Chemistry, Division of Biophysics, Barbarastr. 11, 49076 Osnabrück, Germany</p> <p>https://www.biophysik.uni-osnabrueck.de/</p>
Fig. 5 in A New Genus and Two New Species of the Family Aulopidae (Aulopiformes), Commonly Referred to as Aulopus, Flagfins, Sergeant Bakers or Threadsails, in Australasian Waters
Fig. 5. Species of Leptaulopus gen. nov.: A) L. damasi, NSMT-P115220, neotype, 297 mm SL, Kagoshima Prefecture, Japan (photo by H. Motomura); B) L. damasi, CSIRO H 651–10, 170 mm SL, Marion Reef, Queensland, Australia (photo courtesy of CSIRO); C) L. erythrozonatus sp. nov., NMNZ P.042518, holotype, 271 mm SL, Bay of Plenty, New Zealand; and, D) new genus and species (after Shimizu and Yamakawa 1989), Norfolk Ridge, New Zealand.
Fig. 4 in A New Genus and Two New Species of the Family Aulopidae (Aulopiformes), Commonly Referred to as Aulopus, Flagfins, Sergeant Bakers or Threadsails, in Australasian Waters
Fig. 4. Collection localities for specimens examined of Hime spp. Hime pyrhistion sp. nov. (circles): holotype black with red centre, paratypes red and other material blue; Hime japonica (yellow stars).
Fig. 2 in A New Genus and Two New Species of the Family Aulopidae (Aulopiformes), Commonly Referred to as Aulopus, Flagfins, Sergeant Bakers or Threadsails, in Australasian Waters
Fig. 2. Nasal flap: A) Hime pyrhistion sp. nov. NMNZ P.054047, holotype; B) Leptaulopus damasi NSMT-P. 115220, neotype; and, C) Leptaulopus erythrozonatus sp. nov. NMNZ P.042518, holotype.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.