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63 results for “ncbi”
NCBI Enterobacteriacea Closed Chromosome sequences
<p>This is a dataset of high-quality complete chromosomes from NCBI which can be used as part of the MOB-suite as a chromosomal DNA depletion strategy to identify plasmids.</p>
LEMMI instance 2022_01_PROK_NCBI, July 2022
<p>LEMMI instance 2022_01_PROK_NCBI, to reproduce what is found on https://lemmi.ezlab.org</p> <p>See README.md</p>
Catalog of NCBI sequence read archive (SRA) data for salamanders at the Hubbard Brook Experimental Forest 2012-2021
This project was designed to describe fine-scale population genetic differentiation of the stream salamander Gryinophilus porphyriticus among five study streams in the Hubbard Brook Experimental Forest. The data are paired with intensive capture-recapture data to assess direct fitness effects of individual genetic diversity, including effects of individual multilocus heterozygosity on stage-specific survival probabilities. This dataset publishes a manifest of the genomic sequence reads submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). These samples are published at NCBI under the BioProject ID 1090913 (https://www.ncbi.nlm.nih.gov/bioproject/1090913). The tables here include sample metadata and the NCBI URLs to each sample. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
COInr a comprehensive, non-redundant COI database from NCBI-nt and BOLD
<p>COInr is a non-redundant, comprehensive database of COI sequences extracted from NCBI-nt and BOLD. It is not limited to a taxon, a gene region, or a taxonomic resolution. Sequences are dereplicated between databases and within taxa.</p> <p>Each taxon has a unique taxonomic Identifier (taxID), fundamental to avoid ambiguous associations of homonyms and synonyms in the source database. TaxIDs form a coherent hierarchical system fully compatible with the NCBI taxIDs allowing creating their full or ranked linages.<br> <br> COInr is a good starting point to create custom databases according to the users’ needs using mkCOInr scripts available at <a href="https://github.com/meglecz/mkCOInr">https://github.com/meglecz/mkCOInr</a> <br> It is possible to select/eliminate sequences for a list of taxa, select a specific gene region, select for minimum taxonomic resolution, add new custom sequences, and format the database for BLAST, QIIME, RDP classifiers.</p> <p> </p>
Global Biotic Interactions: Elton Dataset Cache NCBI Virus
<p>Global Biotic Interactions: Elton Dataset Cache NCBI Virus</p> <p>The intended use of this archive/cache is to allow for offline-enabled access versions of existing species interaction datasets. The program "Elton" (https://doi.org/10.5281/zenodo.998263) was used to populate the content of elton-datasets.tar.gz . The same program can be used to extract information from the cache archive also. Global Biotic Interactions (https://globalbioticinteractions.org, https://doi.org/10.1016/j.ecoinf.2014.08.005) also uses these archives to create derived species interaction data archives, search indexes and APIs.</p> <p>Please note that due to size considerations, offline-enabled access to an elton dataset cache of iNaturalist interaction data has been excluded from this publications and moved into a separate Zenodo publication at https://doi.org/10.5281/zenodo.3950546 .</p> <p>Contents<br> --------</p> <p>README:<br> this file</p> <p>elton-datasets.tar.gz:<br> versioned archive with species interaction datasets</p> <p>elton-datasets.tar.sha256:<br> content signature of elton-datasets.tar</p> <p>elton-datasets.tsv:<br> list of included datasets</p> <p>elton.jar:<br> commandline program to help access the species interaction datasets</p> <p>Usage<br> -----</p> <p>To install, extract elton-datasets.tar.gz into a directory of choice using:</p> <p>tar xfz elton-dataset.tar.gz</p> <p>To use, download elton.jar included this publication and execute the following to get a list of available datasets:</p> <p>java -Xmx4G -jar elton.jar datasets</p> <p>on a system that has java v8+ installed.</p> <p>If all goes well, you should be able to regenerate the included file elton-dataset.tsv .</p> <p>For more information on how to use elton.jar, execute:</p> <p>java -jar elton.jar usage</p> <p>or visit https://github.com/globalbioticinteractions/elton for more available commands.</p> <p>Alternatively, without using Elton, you can access the data by inspecting the access.tsv files in the various directories of the datasets directory.</p> <p>When using these datasets in a publication or product, please cite the *original* data providers and publications. You can find the citations in the data.</p> <p>Included datasets:</p> <p>globalbioticinteractions/ncbi-virus Eneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Schäffer, J. Rodney Brister, Virus Variation Resource – improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482–D490, https://doi.org/10.1093/nar/gkw1065 . https://github.com/globalbioticinteractions/ncbi-virus/archive/531a8d743d7adcf1153a19087e5d3c5b76750e3e.zip 2021-11-13T01:25:31.596Z 7ccb94a24adcb5af77ba7d72d04649ae0189b76eed5e9d4a86e2938d87a1353e 0.12.2</p> <p>Associated content ids:</p> <p>hash://sha256/970526ccd38fe336a0c863fce9cc9967ed07c1ac1dbd7aa46b5a6abac1b77868<br> hash://sha256/c1612d2a43b12a1c2d5a93d8562428bcd802e316f45aefeed4bbc6afbd1529dd<br> hash://sha256/5008acd88c69cc63763c69d481b20042789ffd47b27968a731a6fe0d29ff1675<br> hash://sha256/609b4ee69df8728ce75f6d50c28fbdfe0a4280b38ce947c9f88a08c2d2b15c61<br> hash://sha256/2017cf4bb2b5ebadbfbb587e2532a400402830f633d70a2c7a82b31964a697ec<br> hash://sha256/1e38594c3210ce1bd13e7fee0643d0abd23aa3c4a8cb659d4108bff960e41ea9<br> hash://sha256/aaff351d4a7f3d2da4f8a742e05a6969a25490e553eedf3b1eded689e079fa0c<br> hash://sha256/3ae5b5cd979453c04e2dded1a7075eee1e7fe2f626eeef3f3150e80423c8e8b1<br> hash://sha256/eed5e9e44dd0b4b5c03aaa435589ee2a863c4544cc8ad5041ba7fb64e672068a<br> hash://sha256/3a68707a1933034666b40aff81df7dc4cde581b3bfc1cf60a82ef3339c091fe6<br> hash://sha256/785863eb02633a4d8fb5e7e261e40bd57fc3c6d24dbcb039e00097ae9e9501aa<br> hash://sha256/c48b8ce1c9069117fc063eaa9a06378b55ff0b3559bbb27bfde8bb1fa81699cf<br> hash://sha256/05c0d01bb92c8d69ec5843a7117d43a849aaae674225ed5ae18d76ba14650416<br> hash://sha256/2a7f1e738afdf09c709cd1c6b59c8d5345433f5ce4a4a0683e7703ace0be0fff<br> hash://sha256/5868c9331bb8dc82f7b463616857e4c3919c9621a9cb7481ee120a539a937fb4<br> hash://sha256/dcc2abe8d1ed3a0e62e57ff6d4afcd5475b8dd1e527b3da2a782ec7398a71a08<br> hash://sha256/094b503799330971b9909ccdd9cd2d1e51ecc4160ed5248b02c46b17b9031994<br> hash://sha256/4fda5e4d75695eec1217cf513568407690db37e3ce2a9d4cca1b9967f9fd5353<br> 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hash://sha256/979ca1b20ccc38e47a0d9cd9443784576112819b110b6a89df76feccd81e4992<br> hash://sha256/879c07f3192109f13f85f3550c9ece53b2001a5c5d0314aa261915bc53878444<br> hash://sha256/951775e4d47efead0297f20c55281be43989c72110b9de9bd2328fdedaad7c49<br> hash://sha256/905d6b50299242ae3b15f0022bdcc8d1063813be74fb767bec8df77cd9fae45b<br> hash://sha256/b8fb6ed7970cc19e92daaa1fcd2fd06ec7e390309e0a0be7775ed6981143a418<br> hash://sha256/788d0ee0e001b05c7a1f3ea8ce6dd8492c4cfa60c69ff8d641c9df76fec9b811<br> hash://sha256/753f23df2ea90267068b44a28a66a3f1e55e4f5bfee162e0bd311db7ec55b85a<br> hash://sha256/7110370ef6cb20ad9fdb93126b1e78fbe3ca31c54e09f456c9188c8abc7efdb8<br> hash://sha256/0d227d490b8c95ea15edd4bec3119cd29a0f02fceb501701815359740875c73a<br> hash://sha256/672a1e0cc42ce0847568822e8846c89b36d78fbc968afe53205b579a6e590d64<br> hash://sha256/bd81d95e9dafcd889c48fe1d8f58010b82c7ebffb961f3f5e7ca1c76bce43910<br> hash://sha256/c6a127579d7a35bb06134016ebd47cd8f7c363d8d6384d2279462c104b52f30a<br> 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LEMMI instance 2021_9_VIR_NCBI, July 2022
<p>LEMMI instance 2021_9_VIR_NCBI, to reproduce what is found on https://lemmi.ezlab.org</p> <p>See README.md</p>
Coronavirus Spike Protein Sequences from NCBI Virus
<p>For the study of taxonomic classification of coronaviruses across all genera (Alpha-, Beta-, Gamma-, and Deltacoronavirus), we can use spike protein sequences downloaded from the NCBI Virus website, \url{https://www.ncbi.nlm.nih.gov/labs/virus/vssi/}. The spike protein sequences used in this study were downloaded on November 21 and 27, 2021 using search terms such as ``spike'', ``S1 protein'', ``S2 protein'', and ``S protein'', in order to download as many spike protein sequences as possible.</p>
NCBI genes with citation counts
<p>This dataset is a tab-separated file containing a list of human genes as listed on NCBI's Gene database and annotated with citation counts. The columns are:</p> <p>taxid gene_id unused gene_symbol gene_description gene_type citation_count</p> <p>Example:</p> <p>9606 3553 - IL1B interleukin 1 beta protein-coding 2462</p>
NCBI::Taxonomy data set
<p>This is a data set for NCBI::Taxonomy Module.</p> <p>The sizes of the decompressed files are:</p> <pre><code class="language-bash">all_taxonomy.bin 25 GBytes nodes.bin 185 MBytes nucl_est_taxonomy.bin 5.1 GBytes nucl_gb_taxonomy.bin 6.1 GBytes nucl_gss_taxonomy.bin 4.5 GBytes nucl_wgs_taxonomy.bin 14 GBytes pdb_taxonomy.bin 4.2 GBytes prot_taxonomy.bin 13 GBytes ranks.bin 325 Bytes </code></pre> <p> </p>
Figure. Interferon alpha-A based phylogenetic tree (neighbor joining method) constructed by MEGA 6.1 for Punjab urial in comparison with other mammalian species sequences available from GenBank (NCBI). in Characterization of interferon alpha of major histocompatibility complex class I in Punjab urial (Ovis vignei punjabiensis)
Figure. Interferon alpha-A based phylogenetic tree (neighbor joining method) constructed by MEGA 6.1 for Punjab urial in comparison with other mammalian species sequences available from GenBank (NCBI).
Genome Annotation Xff Temecula1 (NCBI accession GCF_000007245.1) with Bakta
<p>The genome of <em>Xff </em>strain Temecula1 (NCBI accession GCF_000007245.1) was re-annotated using the Bakta pipeline (Schwengers et al., 2021).</p>
BLASTDB NCBI GeneBank viruses complete genomes 2023/01/01
<p>Database: NCBI GeneBank Viruses Complete genomes 2023_01_01<br> 86,641 sequences; 2,391,589,518 total bases<br> Date: Jan 1, 2023 4:32 PM<br> BLASTDB Version: 5</p> <p> </p>
Field-wide assessment of differential HT-seq from NCBI GEO database
<p>We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. </p> <p> </p> <p>- This release includes GEO series published up to Dec-31, 2020;</p> <p> </p> <p><a href="https://zenodo.org/api/files/320af641-0181-4e73-adfa-d2d2e61dbf23/geo-htseq.tar.gz?versionId=852d4e0b-3c5b-4156-ac80-9b27cc1e53cb">geo-htseq.tar.gz </a>archive contains following files:</p> <p>- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).</p> <p>- output/document_summaries.csv, document summaries of NCBI GEO series.</p> <p>- output/suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions.</p> <p>- output/suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO.</p> <p>- output/publications.csv, publication info of NCBI GEO series.</p> <p>- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series</p> <p>- output/spots.csv, NCBI SRA sequencing run metadata.</p> <p>- output/cancer.csv, cancer related experiment accessions.</p> <p>- output/transcription_factor.csv, TF related experiment accessions.</p> <p>- output/single-cell.csv, single cell experiment accessions.</p> <p>- blacklist.txt, list of supplementary files that were either too large to import or were causing computing environment crash during import.</p> <p> </p> <p>Workflow to produce this dataset is available on Github at <a href="https://github.com/rstats-tartu/geo-htseq">rstats-tartu/geo-htseq</a>.</p> <p> </p> <p><a href="https://zenodo.org/api/files/320af641-0181-4e73-adfa-d2d2e61dbf23/geo-htseq-updates.tar.gz">geo-htseq-updates.tar.gz </a>archive contains files:</p> <p>- results/detools_from_pmc.csv, differential expression analysis programs inferred from published articles</p> <p>- results/n_data.csv, manually curated sample size info for NCBI GEO HT-seq series</p> <p>- results/simres_df_parsed.csv, pi0 values estimated from differential expression results obtained from simulated RNA-seq data</p> <p>- results/data/parsed_suppfiles_rerun.csv, pi0 values estimated using smoother method from anti-conservative p-value sets</p> <p> </p> <p> </p>
Diamond NCBI Genbank Viral database for SOVAP
<p><strong>Diamond NCBI Genbank Viral database</strong></p> <p>Database type: Diamond database</p> <p>Database format version: 3</p> <p>Label: 2023-03-18_18-40-17</p> <p>Sequences: 3,191,190</p> <p>Sum length: 824,564,244</p> <p>Assembly summary entries: 58,201</p> <p>--------------------------------------------------------</p> <p><strong>SOVAP v.1.3: </strong><a href="https://github.com/poursalavati/SOVAP">GitHub</a></p> <p><strong><em>Soil Virome Analysis Pipeline</em></strong></p> <p>Description</p> <p>The study of viral communities in complex environmental samples, such as <strong>soil</strong>, can provide valuable insights into the diversity and functions of viral communities in the ecosystem. However, processing and analyzing of virome data can be a challenging task that requires the integration of various computational tools and techniques.</p> <p>To address these challenges, we have developed <strong>SOVAP</strong> pipeline that utilizes a suite of state-of-the-art tools for processing, analysis, and annotation viromics and metagenomics data.</p> <p>It utilizes various tools such as <strong>Fastp</strong> and <strong>Centrifuge</strong> for preprocessing and contamination removal, <strong>geNomad</strong>, <strong>Diamond</strong> and <strong>Megan</strong> for identification and annotation of viral contigs which are assembled and clustered using <strong>Megahit</strong> and <strong>CD-HIT</strong>. Additionally, this pipeline provides an <strong>estimate of the abundance</strong> of viral contigs, allowing for a more comprehensive understanding of the virome within the sample. The integration of these tools offers a reliable and effective means of taxonomy classification and annotation of viral contigs, aiding researchers in gaining insight into the composition and function of the virome within the analyzed sample.</p> <p>By integrating the SOVAP pipeline with <strong>IMG/VR</strong> and <strong>geNomad</strong>, it is possible to identify a wider range of viruses, including those that were previously unknown.</p> <p>The <strong>batch-mode</strong> script allows for the processing of multiple datasets using the SOVAP pipeline. This feature is particularly useful for <strong>large-scale</strong> analyses, such as those involving multiple environmental samples or large sequencing datasets.</p>
Metadata related to Optimized Intercropping System (NCBI PRJNA928765)
<p><strong>The Sahel of W Africa is a climate change hotspot, where soil degradation and recurring drought significantly reduce agricultural productivity. Erratic rainfall, exacerbated by climate change, t contributes to ongoing and future food insecurity in this region. The staple crop, pearl millet (Pennisetum glaucum), is grown by subsistence farmers during the rainy season without fertilizer or irrigation (conditions also desired for US domestic biofuel cultivation). Indigenous farming practices demonstrate one solution: where farmers intercrop with an native woody shrub (Guiera senegalensis), millet drought resilience - and crop yields - are dramatically increased. One mechanism of this beneficial interaction: shrubs perform hydraulic redistribution of water to surface soil, assisting crops through growing-season drought periods. Additionally, the moister and more nutrient rich soils under the shrub canopy harbor a distinct and active microbial community; this work examines the role of these microbes in aiding the millet. We have characterized this potential in partnership with the Joint Genome Institute (JGI, project number 505730) to metagenomically sequence the millet rhizo- and endo-sphere communities, with and without shrub intercropping, in long-term experimental plots in Senegal. Using the framework of known plant-microbe beneficial interactions we are analyzing metagenomes for genes and genomes related to plant growth promotion, and metagenomic data is publicly available via NCBI under PRJNA928765. Here, we publish soil chemical, plant biomass, and PLFA data obtained from the OSS during the 2019 sampling season and corresponding 2020 dry season. </strong></p>
Converting NCBI Data to the AnnData Format
<p>Input dataset for the tutorial "Converting NCBI data to the AnnData Format" hosted on the Galaxy Training Network</p>
ncbi_results
<p>Data for the article 'Transmembrane helices are an over-presented and evolutionarily conserved source of major histocompatibility complex class I and II epitopes' by Richèl J.C. Bilderbeek, Maksim V. Baranov, Geert van den Bogaart and Frans Bianchi.</p> <p>Link to article: [https://github.com/richelbilderbeek/bbbq_article](https://github.com/richelbilderbeek/bbbq_article)</p>
ncbi_peregrine
<p>Data for the article 'Transmembrane helices are an over-presented and evolutionarily conserved source of major histocompatibility complex class I and II epitopes' by Richèl J.C. Bilderbeek, Maksim V. Baranov, Geert van den Bogaart and Frans Bianchi.</p> <p>Link to article: [https://github.com/richelbilderbeek/bbbq_article](https://github.com/richelbilderbeek/bbbq_article)</p>
Blastn index for NCBI nr virus sequences 12/2020
<p>Blastn index for NCBI nr virus sequences</p> <p>Updated 12/2020</p>
LivingNER terminology: NCBI Taxonomy translated to Spanish
<pre>Official NCBI Taxonomy FTP dump (https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/) with the terms translated to Spanish by a Neural Machine Translator fine-tuned for the biomedical domain.</pre> <p>We have added the following terms: </p> <pre><code>2560602 Mumps orthorubulavirus Mumps orthorubulavirus scientific name Paperas ortorubulavirus 2560526 Human orthorubulavirus 4 Human orthorubulavirus 4 scientific name orthorubulavirus humano 4 2847144 hepatitis C virus genotype 1a hepatitis C virus genotype 1a scientific name virus de la hepatitis C genotipo 1a 2560525 Human orthorubulavirus 2 Human orthorubulavirus 2 scientific name orthorubulavirus humano 2 _NOCODE_ out of NCBI Taxonomy scope out of NCBI Taxonomy scope NA mención no codificable a NCBI Taxonomy</code></pre> <p>The first 4 were added because they appear in the LivingNER corpus, and are present in the browser version of NCBI Taxonomy.</p> <p>The last one (_NOCODE_) is added to identify terms in LivingNER corpus not present in the NCBI Taxonomy.</p> <p> </p> <p> </p> <p><strong>Format:</strong></p> <p>Tab-separated file with the following columns:</p> <ul> <li>tax_id: the id of node associated with this name</li> <li>name_txt: name itself</li> <li>unique name: the unique variant of this name if name not unique</li> <li>name class: (synonym, common name, scientific name, ...)</li> <li>Spanish name: name in Spanish</li> </ul> <p> </p> <p><strong>Please cite if you use this dataset:</strong></p> <p>A. Miranda-Escalada, E. Farré-Maduell, S. Lima-López, D. Estrada, L. Gascó, M. Krallinger, Mention detection, normalization & classification of species, pathogens, humans and food in clinical documents: Overview of livingner shared task and resources, <em>Procesamiento del Lenguaje Natural</em> (2022)</p> <pre><code>@article{amiranda2022nlp, title={Mention detection, normalization \& classification of species, pathogens, humans and food in clinical documents: Overview of LivingNER shared task and resources}, author={Miranda-Escalada, Antonio and Farr{\'e}-Maduell, Eul{`a}lia and Lima-L{\'o}pez, Salvador and Estrada, Darryl and Gasc{\'o}, Luis and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, year={2022} }</code></pre> <p> </p> <p><strong>Resources</strong></p> <ul> <li><a href="https://temu.bsc.es/livingner/"><strong>Web</strong></a></li> <li><a href="https://doi.org/10.5281/zenodo.6376662"><strong>Training and validation set</strong></a></li> <li><a href="https://doi.org/10.5281/zenodo.6385162"><strong>Annotation Guidelines</strong></a></li> <li><a href="https://github.com/tonifuc3m/livingner-evaluation-library"><strong>Evaluation library</strong></a></li> </ul> <p> </p> <p>For more information visit <a href="https://temu.bsc.es/livingner/">https://temu.bsc.es/livingner/</a> or email us at encargo-pln-life@bsc.es</p> <p>Check out the translator demo: https://textmining.bsc.es/translator</p>
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.