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11,177 results for “identify”
Using big sequencing data to identify chronic SARS-Coronavirus-2 infections
<p>This dataset supports the "Using big sequencing data to identify chronic SARS-Coronavirus-2 infections" publication in Nature Communications. </p><p>It contains the following files and folders:<br> </p><ul><li>Readme.txt - detailed information on all folders and files.</li><li>Supplementary Dataset 1</li><li>Supplementary Dataset 2</li><li>Supplementary Dataset 3</li><li>Supplementary Dataset 4</li><li>Supplementary Dataset 5</li><li>Supplementary Dataset 6</li><li>Supplementary Dataset 7</li></ul>
Data from: The twenty amino acids are identified by unique numbers assigned to the uracil, cytosine, adenine, and guanine found in the three base positions of the sixty-four messenger RNA genetic codons
<p>A codon's three bases consist of any combination of uracil, cytosine, adenine, or guanine and these encode the twenty amino acids. When the codon's first two bases are given specific values, and those values are multiplied, then the third base of the codon is used during translation only when the product is greater than three. Here we show that those values plus more variables within the ribosomal decoding site results in specific flow values for each of the twenty amino acid groups. These results are demonstrated in a flow chart showing the unidirectional flow which is expected during the translation process. All twenty amino acids can be represented by numbers that describe their relationship to each other and to the decoding site. We anticipate our findings will increase discussion about using a number system to better understand the translation process.</p>
Data from: Molecular docking and dynamics studies to identify novel active compounds targeting potential breast cancer receptor proteins from an indigenous herb Euphorbia thymifolia Linn
<p>Breast cancer has become most prevalent disease and their incidence has doubled in Indian scenario. Targeted therapy with the novel compounds derived from plants could be the promising approach for the development of drugs. <em>Euphorbia thymifolia</em> L is a widely growing tropical herb which has been reported for its various ethnopharmacological properties, including anticancer properties. The aim of the present study was to identify the active phytocompounds present in the methanolic extract using an <em>I</em><em>n-silico</em> approach. The methanolic extract of <em>E. thymifolia</em> (ME.ET) was subjected to GC-MS analysis and the identified compounds were docked with potential protein targets implicated in breast cancer such as ERK1, AKT, EGFR/HER2, ER, MELK, PLK1, PTK6. Compounds with good docking score were further subjected to dynamics study to understand the protein ligand binding stability, ligand pathway calculation, molecular mechanics energies combined with Poisson-Boltzmann (MM/PBSA) calculation using Schrodinger suite. Out of 219 unique phytocompounds subjected to docking, two compounds namely, 3,6,9,12-tetraoxatetradecane-1,14-diyl dibenzoate (TTDB) and succinic acid, 2-(dimethylamino)ethyl 4-isopropylphenyl ester (SADPE) showed good docking score. Molecular dynamics study showed high affinity and low binding energy for TTDB with HER2, ERK1 and SADPE with ER. Hence this is the first study to identify and report active compounds from <em>E.thymifolia</em> linn. Further <em>invitro</em> and <em>invivo</em> anticancer studies can be performed to confirm these results and understand the molecular mechanism by which TTDB and SADPE exhibit anticancer activity against breast cancer.</p>
Supplementary material 1 from: Walter HE, Cádiz-Véliz A, Meriño BM, Villalobos-Barrantes HM, Guerrero PC (2024) Taxonomic dissection based on molecular evidence of the Eriosyce curvispina complex (Cactaceae): identifying nine endemic species from Central Chile. PhytoKeys 237: 117-139. https://doi.org/10.3897/phytokeys.237.107403
New accessions of taxa used in the phylogenetic analyses, including their laboratory code, population locality, and GenBank numbers
GMPipe: A gene mining pipeline for identifying olfactory receptor repertoires and other multicopy gene families from genomes
<p>This repository contains the code and tutorial for the GMPipe algorithm. GMPipe is a gene mining pipeline designed to maximize the identification of multicopy gene family homologs in genome assemblies (for intronless genes) or lists of protein sequences. GMPipe has been validated for mining olfactory receptor genes from vertebrate genomes. This algorithm uses custom ingroup and outgroup sequence lists to distinguish between members of a gene family of interest from close off-target relatives. Overall, GMPipe uses HMMER as an initial screening step, paired with phylogenetic clustering to test whether passing sequences are more closly related to "ingroup" than "outgroup" sequences. The result is a pipeline with a low false negative rate, with minimal impact on the false positive rate, especially when paired with additional family-specific filtering steps.</p>
Supplementary material 2 from: Jones TS, Culham A, Pickles BJ, David J (2024) Can gardeners identify 'future invaders'? NeoBiota 91: 125-144. https://doi.org/10.3897/neobiota.91.110560
Full list of reported taxa (cultivars not included) along with a unique ID for each gardener (survey participants)
A Comprehensive Dataset of Classified Citations with Identifiers from English Wikipedia (2024)
<p><strong>2024 (new!)</strong></p> <p>This is a dataset of 44.766.800 (+9.2%) citations extracted from the English Wikipedia February 2024 dump (<a href="https://dumps.wikimedia.org/enwiki/20240220/">https://dumps.wikimedia.org/enwiki/20240220/</a>).</p> <p>The same extraction and template harmonization pipeline was used as the year before. The published dataset fields are like in the previous dataset. A classification label is assigned to each citation (either 'news', 'book', 'journal' or 'other)' by the deterministic rule-based classifier that analyses available identifiers (see code documentation for details), revealing the following citation subgroups:</p> <ol> <li>The total number of news: 10.958.151 (+9.4%)</li> <li>The total number of books:* 3.277.629 (+8.6%)</li> <li>The total number of journals*: 2.248.748 (+8.7%)</li> </ol> <p>* Please note that these numbers do not represent the overall number of book and journal citations, we count only citations with DOI, PMID, PMC and ISBN identifiers assigned by authors (prior to the lookup process that augments citations with missing identifiers). </p> <p>This dataset is not equipped with identifiers located via the lookup process (no 'acquired_ID_list' field). If there is interest in such an augmented version, see the source code for instructions or contact authors for assistance with this task. </p> <p><strong>2023</strong></p> <p>This is a dataset of 40.664.485 citations extracted from the English Wikipedia February 2023 dump (<a href="https://dumps.wikimedia.org/enwiki/20230220/">https://dumps.wikimedia.org/enwiki/20230220/</a>).</p> <p>Version 1: en_citations.zip is a dataset of extracted citations </p> <p>Version 2: en_final.zip is the same dataset with classified citations augmented with identifiers </p> <p>The fields are as follows:</p> <ul> <li>type_of_citation - Wikipedia template type used to define the citation, e.g., 'cite journal', 'cite news', etc.</li> <li>page_title - title of the Wikipedia article from which the citation was extracted.</li> <li>Title - source title, e.g., title of the book, newspaper article, etc.</li> <li>URL - link to the source, e.g., webpage where news article was published, description of the book at the publisher's website, online library webpage, etc.</li> <li>tld - top link domain extracted from the URL, e.g., 'bbc' for https://www.bbc.co.uk/... </li> <li>Authors - list of article or book authors, if available.</li> <li>ID_list - list of publication identifiers mentioned in the citation, e.g., DOI, ISBN, etc.</li> <li>citations - citation text as used in Wikipedia code</li> <li>actual_label - 'book', 'journal', 'news', or 'other' label assigned based on the analysis of citation identifiers or top link domain. </li> <li>acquired_ID_list - identifiers located via Google Books and Crossref APIs for citations which are likely to refer to books or journals, i.e., defined using 'cite book', 'cite journal', 'cite encyclopedia', and 'cite proceedings' templates.</li> </ul> <ol> <li>The total number of news: 9.926.598</li> <li>The total number of books: 2.994.601</li> <li>The total number of journals: 2.052.172</li> <li>Augmented with IDs via lookup 929.601 (out of 2.445.913 book, journal, encyclopedia, and proceedings template citations not classified as books or journals via given identifiers). </li> </ol> <p>The source code to extract citations can be found here: <strong><a href="https://github.com/albatros13/wikicite">https://github.com/albatros13/wikicite</a>. </strong></p> <p>The code is a fork of the earlier project on Wikipedia citation extraction: <a href="https://github.com/Harshdeep1996/cite-classifications-wiki">https://github.com/Harshdeep1996/cite-classifications-wiki</a>.</p> <p> </p>
W2 light curve, W1 - W2 color change and color-magnitude diagrams for all identified NEOWISE variables of ATLASGAL sources
<p>Figures of the W2 light curve, W1 - W2 color change and color-magnitude relationship for different types of variability. The ZIP file contains six subsections, including linear, sin, and sin+linear light curves of the secular type, as well as burst, drop, and irregular light curves of the stochastic type. The evolutionary stage, source name and corresponding fitting parameters are given above each figure.</p> <p>The W2 light curves for all identified variables were presented using the mean Modified Julian Date (MJD) and the mean magnitude of each epoch. It should be noted that the full data for these light curve plots can be found in Table A1 of the published article.</p> <div> <div> </div> <div> </div> <div> <div> <div> <div> </div> </div> </div> </div> </div>
Fig. 10 in Identifying PCR primers to facilitate molecular phylogenetics in Caddisflies (Trichoptera)
Fig. 10. Results of maximum likelihood analysis in PAUP* (Swofford, 2002) using sequences from all 8 species. Bootstrap values are labeled for each node.
Figs 1–9 in Identifying PCR primers to facilitate molecular phylogenetics in Caddisflies (Trichoptera)
Figs 1–9. Results of maximum likelihood analysis in PAUP* (Swofford, 2002). 1. 16s rRNA. 2. 18s rRNA. 3. CAD. 4. COI. 5. COII. 6. EF-1. 7. IDH. 8. POL-II. 9. RPS2. All trees were rooted with A. deflata except for RPS2 for which no A. deflata sequence was
Representative data accompanying the manuscript: Four-dimensional quantitative analysis of cell plate development in Arabidopsis using lattice light sheet microscopy identifies robust transition points between growth phases
<p>Representative data accompanying the manuscript: Sinclair R, Wang M, Jawaid MZ, Longkumer T, Aaron J, Rossetti B, Wait E, McDonald K, Cox D, Heddleston J, Wilkop T, Drakakaki G. (2024). <em>Four-dimensional quantitative analysis of cell plate development in Arabidopsis using lattice light sheet microscopy identifies robust transition points between growth phases.</em> J Exp Bot. 2024 Mar 4: erae091. doi: 10.1093/jxb/erae091.</p> <p>The data show YFP–RABA2a dynamics in dividing cells of Arabidopsis root tips using lattice light sheet microscopy. Treatments with or without Endosidin 7, a cytokinesis-specific callose deposition inhibitor, are shown.</p> <p>Data: </p> <p>22.3 YFP-RABA2A. </p> <p>23.9 YFP-RABA2A ES7 </p>
Supplemental package of a study on Microservices versus Monoliths: Identifying Challenges and Proposing Practical Recommendations
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Systematic analysis identifies CDKN2A as a prognostic biomarker for hepatocellular carcinoma
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De-identified Covid-19 Data (Portugal)
Open the record for dataset details and reuse information.
Data for: Identifying the knowledge and capacity gaps in Southeast Asian insect conservation
<p>Datasets used in the paper "Identifying the knowledge and capacity gaps in Southeast Asian insect conservation".</p> <p>Curated datasets are provided in <em>Curated_Datasets.zip</em> and consists of (1) Insect occurrence, (2) primary literature, (3) authorship, (4) funding, and (5) Twitter data. Refer to <em>README_curated_datasets.txt</em> for more information on each dataset and their term definitions.</p> <p>Raw insect occurrence data from GBIF are provided in <em>GBIF_Raw_Insect_Occurrences.zip</em>. Refer to <em>README_GBIF_Raw_Insect_Occurrences.txt</em> for more information on each dataset and their term definitions.</p> <p>Data and models used in the authors' spaCy classifier analysis are provided in <em>spaCy_Data.zip</em> and <em>spaCy_Model_Best.zip</em>. Refer to <em>README_spacy.txt</em> for more information on each file and term definitions of the datasets used.</p> <p>Accompanying codes used to produce the figures and conduct the analyses are available in Github (https://github.com/teelabntu/SEA_Insect_Conservation).</p>
Integrative genome-wide analyses identify novel loci associated with kidney stones and provide insights into its genetic architecture
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High light-inducible (HLI) proteins identified in three Paulinella isolates.
<p>Description of files:</p> <p><strong>Paulinella_chromatophora_CCAC0185_HLI_genes.gff3</strong></p> <p>HLI gene models in the <em>Paulinella chromatophora</em> CCAC0185 genome.</p> <p> </p> <p><strong>Paulinella_chromatophora_CCAC0185_HLI_genes.pep.faa</strong></p> <p>Proteins derived from the HLI gene models in the <em>Paulinella chromatophora</em> CCAC0185 genome.</p> <p> </p> <p><strong>Paulinella_chromatophora_CCAC0185_HLI_genes.unaligned_transcripts.gff3</strong></p> <p>HLI gene models in the <em>Paulinella chromatophora</em> CCAC0185 assembled transcripts.</p> <p> </p> <p><strong>Paulinella_chromatophora_CCAC0185_HLI_genes.unaligned_transcripts.pep.faa</strong></p> <p>Proteins derived from the HLI gene models in the <em>Paulinella chromatophora</em> CCAC0185 assembled transcripts.</p> <p> </p> <p><strong>Paulinella_micropora_KR01_HLI_genes.gff3</strong></p> <p>HLI gene models in the <em>Paulinella micropora</em> KR01 genome.</p> <p> </p> <p><strong>Paulinella_micropora_KR01_HLI_genes.pep.faa</strong></p> <p>Proteins derived from the HLI gene models in the <em>Paulinella micropora</em> KR01 genome.</p> <p> </p> <p><strong>Paulinella_micropora_MYN1_HLI_genes.gff3</strong></p> <p>HLI gene models in the <em>Paulinella micropora</em> MYN1 genome.</p> <p> </p> <p><strong>Paulinella_micropora_MYN1_HLI_genes.pep.faa</strong></p> <p>Proteins derived from the HLI gene models in the <em>Paulinella micropora</em> MYN1 genome.</p>
BridgeDb: pathway identifier mapping database derived from Wikidata
<p>First release of a BridgeDb pathway identifier mapping database. Currently supports Wikidata and WikiPathways identifiers. CCZero.</p> <pre><code>[INFO]: Database finished. INFO: old database is Wikidata 1.0.0 (build: 20211211) INFO: new database is Wikidata 1.0.0 (build: 20211211) INFO: Number of ids in Wd (Wikidata): 905 (unchanged) INFO: Number of ids in Wp (WikiPathways): 900 (unchanged) INFO: new size is 2 Mb (changed +0.0%) INFO: total number of identifiers is 1805 INFO: total number of mappings is 1810 </code></pre> <p> </p>
Profiling of mature stage human breastmilk cells identifies host-defense lactocyte sub-populations
<p>Breastmilk is chock-full of nutrients, immunological factors, and cells that aid infant development. Maternal cells are the least studied breastmilk component, and their unique properties are difficult to identify using traditional techniques. Here, we characterized the cells in mature stage breastmilk from healthy donors at the protein, gene, and transcriptome levels. Holistic analysis of flow cytometry, qPCR, and single cell RNA sequencing data identified the predominant cell population as epithelial with smaller populations of macrophages and T cells. Two percent of epithelial cells expressed four stem cell markers: SOX2, TRA-1-60, NANOG, and SSEA4. Furthermore, milk contained six distinct epithelial lactocyte sub-populations, including three previously unidentified sub-populations programmed towards host-defense and intestinal development. Pseudotime analysis delineated the differentiation pathways of epithelial progenitors. Together, these data define healthy human maternal breastmilk cells and provide a basis for their application in maternal and infant medicine.</p>
A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set
<p>It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. In this study, we developed a data-driven approach termed <u>E</u>nvironmental <u>C</u>ovariate Search Affecting <u>G</u>enetic <u>C</u>orrelations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (<i>Glycine max</i>) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields. This dataset provides the data used in this study and supplementary tables cited in the manuscript.</p>
ScienceDex guides
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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.