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144 results for “graph data”
Graphs of data for elderly individuals (aged 60 to 120 years) with syphilis in Brazil
<p>A set of data graphs containing informations on elderly people with syphilis in Brazil, aged between 60-120 years with syphilis in Brazil, aged between 60-120 years and contains spreadsheet results of trend analysis of acquired syphilis, by regions of Brazil, in the period 2010-2020, referring to the article entitled "<strong>ACQUIRED SYPHILIS IN OLDER PEOPLE IN BRAZIL FROM 2010-2020".<br><br><br></strong>The dataset used to plot the graphs can be found at: <a href="https://doi.org/10.5281/zenodo.10086131">https://doi.org/10.5281/zenodo.10086131</a></p>
Graph Data: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios
<p>Data used for creating the figures in the paper: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios.</p> <p>It contains the flow exceedances (as mm day<sup>-1</sup>), flow duration slope, median elasticity and runoff ratio for the different afforestation scenarios. Also included is the information on the changes of broadleaf afforestation. </p> <p>If you have any questions, please email marcus.buechel@ouce.ox.ac.uk.</p>
Supplementary data files for manuscript titled "From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research"
<div>This data repository contains all the necessary supplementary files for the manuscript titled "<strong>From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research.</strong>"</div> <div> </div> <div>The repository is a copy of the <a href="https://github.com/IMI-COMBINE/template2graphs">GitHub page</a> with the source code used to generate the graph and additional files required for the Lab Data Template.</div> <div> </div> <div>Below we provide a brief overview of the data files in the `additional folder` and their underlying purpose:</div> <div> <ul> <li>The <strong>Data Survey</strong> collects relevant project and data set information to set up a Data Management Plan. It can serve as an input for Lab Data Template development.</li> <li>The <strong>Lab Data Templates</strong> facilitate the collection of AMR research data (in vivo and in vitro) in several sub-tables. The Excel format is compatible with upload procedures into the data repository 'grit' and serves as input for a knowledge graph workflow.</li> <li>The <strong>Data dictionary</strong> is connected to the Lab Data Templates and ensures harmonized data entries. In addition, the dictionaries collect metadata beyond the content of the Lab Data Template (e.g. bacterial strain information or compound information) and link to ontologies where possible.</li> <li>The <strong>FAIR assessments</strong> have been used as a primer for improving the template. This report is generated using the FAIR-DSM model.</li> </ul> </div> <div>The templates have been used during the IMI2 GNA NOW project to collect information and have been improved according to FAIR standards in collaboration with the IMI FAIRplus project ("post FAIRification").</div>
scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data
<p>This repository contains the training data and source code to reproduce the results of our paper:<br>scGraph2Vec: a deep generative model for gene embedding augmented by Graph Neural Network and single-cell omics data</p> <p>More description can be also found in GitHub (https://github.com/LPH-BIG/scGraph2Vec).</p>
UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC) Data Archive and Biodiversity Dataset Graph hash://md5/10663911550bb52a0f5741993f82db9d hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c
<p>A biodiversity dataset graph: UCSB-IZC</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC). UCSB-IZC is a natural history collection of invertebrate zoology at Cheadle Center of Biodiversity and Ecological Restoration, University of California Santa Barbara.</p> <p>This dataset provides versioned snapshots of the UCSB-IZC network as tracked by Preston [2,3] between 2021-10-08 and 2021-11-04 using [preston track "https://api.gbif.org/v1/occurrence/search/?datasetKey=d6097f75-f99e-4c2a-b8a5-b0fc213ecbd0"].</p> <p>This archive contains 14349 images related to 32533 occurrence/specimen records. See included sample-image.jpg and their associated meta-data sample-image.json [4].</p> <p>The images were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> | grep -o -P ".*depict"\<br> | sort\<br> | uniq\<br> | wc -l</p> <p>And the occurrences were counted using:</p> <p>$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\<br> | grep -o -P "occurrence/([0-9])+"\<br> | sort\<br> | uniq\<br> | wc -l</p> <p>The archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to establish a versioning mechanism.</p> <p>To retrieve and verify the downloaded UCSB-IZC biodiversity dataset graph, first download preston-*.tar.gz. Then, extract the archives into a "data" folder. Alternatively, you can use the Preston [2,3] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://archive.org/download/preston-ucsb-izc/data.zip/,https://zenodo.org/record/5557670/files,https://zenodo.org/record/5660088/files/</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history<br> <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .<br> <hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c> <http://purl.org/pav/previousVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .</p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c file:/home/jhpoelen/ucsb-izc/data/ce/1d/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c OK CONTENT_PRESENT_VALID_HASH 66438 hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c<br> hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 file:/home/jhpoelen/ucsb-izc/data/f6/8d/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 OK CONTENT_PRESENT_VALID_HASH 4093 hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844<br> hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef file:/home/jhpoelen/ucsb-izc/data/3e/70/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef OK CONTENT_PRESENT_VALID_HASH 5746 hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef<br> hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b file:/home/jhpoelen/ucsb-izc/data/99/58/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b OK CONTENT_PRESENT_VALID_HASH 6147 hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".</p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README</p> <p>-- executable java jar containing preston [2,3] v0.3.1. --<br> preston.jar</p> <p>-- preston archive containing UCSB-IZC (meta-)data/image files, associated provenance logs and a provenance index --<br> preston-[00-ff].tar.gz</p> <p>-- individual provenance index files --<br> 2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a</p> <p>-- example image and meta-data --<br> sample-image.jpg (with hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c)<br> sample-image.json (with hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844)</p> <p>--- end of file descriptions ---</p> <p><br> References</p> <p>[1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-11-04 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36 hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c.<br> [2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .<br> [3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132<br> [4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c</p>
Anonymized Graph Data with Friend Connections of 189505 VKontakte Users
<p>The dataset contains anonymized graph data with friend connections of 189505 VKontakte users. The dataset was used in <a href="http://github.com/filipp134/vk_bot_detection">this</a> Github project on the detection of social bots on VKontakte. The script which was used for the collection of the dataset is <a href="https://github.com/filipp134/vk_bot_detection/blob/main/Collecting%20datasets%20and%20merging%20them%20into%20one/collect_graph_data.py">here</a>.</p> <p>The dataset was collected in the following 2 steps by using the official <a href="https://dev.vk.com/api/getting-started">VKontakte API</a>:</p> <p>1. Friend connections of 11766 VKontakte users, who had 177739 unique friends, were collected.</p> <p>2. Friend connections of these 177739 users were collected. </p> <p>The dataset is in JSON format and is quite heavy: 424.5 MB.</p> <p> </p>
Supporting Online Toxicity Detection with Knowledge Graphs: Data
<p>This data repository contains the output files from the analysis of the paper "Supporting Online Toxicity Detection with Knowledge Graphs" presented at the International Conference on Web and Social Media 2022 (ICWSM-2022).</p> <p> </p> <p>The data contains annotations of gender and sexual orientation entities provided by the Gender and Sexual Orientation Ontology (https://bioportal.bioontology.org/ontologies/GSSO).</p> <p>We analyse demographic group samples from the Civil Comments Identities dataset (https://www.tensorflow.org/datasets/catalog/civil_comments).</p>
SeaLiT Knowledge Graphs - Maritime History Data in RDF using a CIDOC-CRM extension (SeaLiT Ontology)
<p><strong>SeaLiT Knowledge Graphs</strong> is an RDF dataset of maritime history data that has been transcribed (and then transformed) from original archival sources in the context of the <a href="http://www.sealitproject.eu/">SeaLiT Project</a> (Seafaring Lives in Transition, Mediterranean Maritime Labour and Shipping, 1850s-1920s). The underlying data model is the <a href="https://zenodo.org/record/5964240">SeaLiT Ontology</a>, an extension of the ISO standard <strong>CIDOC-CRM</strong> (ISO 21127:2014) for the modelling and integration of maritime history information. </p> <p>The knowledge graphs integrate data of totally 16 different types of archival sources:</p> <ul> <li>Crew Lists <ul> <li>Crew and displacement list (Roll)</li> <li>Crew List (Ruoli di Equipaggio)</li> <li>General Spanish Crew List</li> </ul> </li> <li>Registers / Lists <ul> <li>Students Register</li> <li>Civil Register</li> <li>Register of Maritime Personnel</li> <li>Register of Maritime Workers (Matricole della gente di mare)</li> <li>Sailors Register (Libro de registro de marineros)</li> <li>Naval Ship Register List</li> <li>Seagoing Personnel</li> <li>Lists of ships</li> </ul> </li> <li>Censuses <ul> <li>Census La Ciotat</li> <li>First National all-Russian Census of the Russian Empire</li> </ul> </li> <li>Payrolls <ul> <li>Payrolls of private archives and libraries in Greece</li> <li>Payrolls of Russian Steam Navigation and Trading Company</li> </ul> </li> <li>Employment records <ul> <li>Shipyards of Messageries Maritimes, La Ciotat</li> </ul> </li> </ul> <p>More information about the archival sources are available through the <a href="https://sealitproject.eu/dictionary-of-source-types-list">SeaLiT website</a>. Data exploration applications over these sources are also publicly available (<a href="https://catalogues.sealitproject.eu/">SeaLiT Catalogues</a>, <a href="http://rs.sealitproject.eu/">SeaLiT ResearchSpace</a>). </p> <p>Data from these archival sources has been transcribed in tabular form and then curated by historians of SeaLiT using the <a href="https://www.ics.forth.gr/isl/fast-cat">FAST CAT</a> system. The transcripts (records), together with the curated vocabulary terms and entity instances (ships, persons, locations, organizations), are then transformed to RDF using the SeaLiT Ontology as the target (domain) model. To this end, the corresponding schema mappings between the original schemata and the ontology were defined using the <a href="https://github.com/isl/x3ml">X3ML</a> mapping definition language, that were subsequently used for delivering the RDF datasets. </p> <p>More information about the FAST CAT system and the data transcription, curation and transformation processes can be found in the following paper:</p> <blockquote> <p>P. Fafalios, K. Petrakis, G. Samaritakis, K. Doerr, A. Kritsotaki, Y. Tzitzikas, M. Doerr, "FAST CAT: Collaborative Data Entry and Curation for Semantic Interoperability in Digital Humanities", ACM Journal on Computing and Cultural Heritage, 2021. <a href="https://doi.org/10.1145/3461460">https://doi.org/10.1145/3461460</a> [<a href="http://users.ics.forth.gr/~fafalios/files/pubs/fafaliosJOCCH2021.pdf">pdf</a>, <a href="http://users.ics.forth.gr/~fafalios/files/bibs/fafaliosJOCCH2021.bib">bib</a>]</p> </blockquote> <p>The RDF dataset is provided as a set of TriG files per record per archival source. For each record, the dataset provides: i) one trig file for the record's data (<em>records.trig</em>), ii) one trig file for the record's (curated) vocabulary terms (<em>vocabularies.trig</em>), and iii) four trig files for the record's (curated) entity instances (<em>ships.trig, persons.trig, persons.trig, organizations.trig</em>).</p> <p>We also provide the RDFS files of the used ontologies (SeaLiT Ontology verson 1.0, CIDOC-CRM version 7.1.1). </p>
Publication and Maintenance of Relational Data in Enterprise Knowledge Graphs Created (Files used in the experiments)
<p>This dataset contains two files created for the experiments presented in the article: Publication and Maintenance of RDB2RDF Views Externally Materialized in Enterprise Knowledge Graphs.</p> <p><strong>mapR2RML_MusicBrainz_completo.txt</strong><strong>:</strong> We created the R2RML mapping for translating MBD data into the Music Ontology vocabulary, which is used for publishing the LMB view. The LMB view was materialized using the D2RQ tool. It took 67 minutes to materialize the view with approximately 41.1 GB of NTriples. We also provided SPARQL endpoint for querying LMB View.</p> <p><strong>TriggersAndProcedures.txt</strong>: We created the triggers, procedures, and class in java to implement the rules required to compute and publish the changesets.</p> <p><strong>relationalViewDefinition.pdf</strong>: This document gives details about the process of creating the relational views used in the experiments.</p>
PheKnowLator Human Disease Knowledge Graphs - Build Data (Processed)
<p><strong>RELEASE V2.1.0 KNOWLEDGE GRAPH: PROCESSED DATA SOURCES </strong></p> <p><strong>Release:</strong> <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0">v2.1.0 </a></p> <p>The goal of this build was to create a knowledge graph that represented human disease mechanisms and included the central dogma. The data sources utilized in this release include many of the sources used in the initial release, as well as some new data made available by the <a href="https://ctdbase.org/">Comparative Toxicogenomics Database</a> and experimental data from the <a href="https://www.proteinatlas.org/">Human Protein Atlas</a>.</p> <p>Data sources are listed by type (Ontology and Data not represented in an ontology [Database Sources]). Additional details are provided for each data source below. Please see documentation on the primary release (<a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources">https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources</a>) for additional details on each data source as well as citation information.</p> <p><strong>Data Access:</strong></p> <ul> <li><a href="https://console.cloud.google.com/storage/browser/pheknowlator/archived_builds/release_v2.1.0/build_01MAY2021?project=pheknowlator">https://console.cloud.google.com/storage/browser/pheknowlator/archived_builds/release_v2.1.0/build_01MAY2021</a></li> </ul> <p> </p> <p><strong>ONTOLOGIES</strong></p> <ul> <li>Cell Ontology</li> <li>Cell Line Ontology</li> <li>Chemical Entities of Biological Interest (ChEBI) Ontology</li> <li>Gene Ontology</li> <li>Human Phenotype Ontology</li> <li>Mondo Disease Ontology</li> <li>Pathway Ontology</li> <li>Protein Ontology</li> <li>Relations Ontology</li> <li>Sequence Ontology</li> <li>Uber-Anatomy Ontology</li> <li>Vaccine Ontology</li> </ul> <p> </p> <p><strong>Cell Ontology (CL)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://github.com/obophenotype/cell-ontology"><code>GitHub</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Bard J, Rhee SY, Ashburner M. <a href="https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-2-r21">An ontology for cell types</a>. Genome Biology. 2005;6(2):R21</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>transcripts</code> and <code>proteins</code> to <code>cells</code>. Additionally, the edges between this ontology and its dependencies are utilized:</p> <ul> <li><strong><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></strong></li> <li><strong><a href="http://geneontology.org/"><code>GO</code></a></strong></li> <li><strong><a href="https://github.com/pato-ontology/pato/"><code>PATO</code></a></strong></li> <li><strong><a href="https://proconsortium.org/"><code>PRO</code></a></strong></li> <li><strong><a href="https://github.com/oborel/obo-relations/"><code>RO</code></a></strong></li> <li><strong><a href="https://uberon.github.io/"><code>UBERON</code></a></strong></li> </ul> <p> </p> <p><strong>Cell Line Ontology (CLO)</strong></p> <p><strong>Homepage:</strong> <strong><a href="http://www.clo-ontology.org/"><code>http://www.clo-ontology.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Sarntivijai S, Lin Y, Xiang Z, Meehan TF, Diehl AD, Vempati UD, Schürer SC, Pang C, Malone J, Parkinson H, Liu Y. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4387853/">CLO: the cell line ontology</a>. Journal of Biomedical Semantics. 2014;5(1):37</p> </blockquote> <p><strong>Usage:</strong> Utilized this ontology to map <code>cell lines</code> to <code>transcripts</code> and <code>proteins</code>. Additionally, the edges between this ontology and its dependencies are utilized:</p> <ul> <li><strong><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></strong></li> <li><strong><a href="http://disease-ontology.org/"><code>DOID</code></a></strong></li> <li><strong><a href="https://github.com/obophenotype/ncbitaxon"><code>NCBITaxon</code></a></strong></li> <li><strong><a href="https://uberon.github.io/"><code>UBERON</code></a></strong></li> </ul> <p> </p> <p><strong>Chemical Entities of Biological Interest (ChEBI)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://www.ebi.ac.uk/chebi/"><code>https://www.ebi.ac.uk/chebi/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, Turner S, Swainston N, Mendes P, Steinbeck C. <a href="https://academic.oup.com/nar/article-abstract/44/D1/D1214/2502583">ChEBI in 2016: Improved services and an expanding collection of metabolites</a>. Nucleic Acids Research. 2015;44(D1):D1214-9</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>chemicals</code> to <code>complexes</code>, <code>diseases</code>, <code>genes</code>, <code>GO biological processes</code>, <code>GO cellular components</code>, <code>GO molecular functions</code>, <code>pathways</code>, <code>phenotypes</code>, <code>reactions</code>, and <code>transcripts</code>.</p> <p> </p> <p><strong>Gene Ontology (GO)</strong></p> <p><strong>Homepage:</strong> <strong><a href="http://geneontology.org/"><code>http://geneontology.org/</code></a></strong><br> <strong>Citations:</strong></p> <blockquote> <p>Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA. <a href="https://www.nature.com/articles/ng0500_25">Gene ontology: tool for the unification of biology</a>. Nature Genetics. 2000;25(1):25</p> <p>The Gene Ontology Consortium. <a href="https://academic.oup.com/nar/article/47/D1/D330/5160994">The Gene Ontology Resource: 20 years and still GOing strong</a>. Nucleic Acids Research. 2018;47(D1):D330-8</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>biological processes</code>, <code>cellular components</code>, and <code>molecular functions</code> to <code>chemicals</code>, <code>pathways</code>, and <code>proteins</code>. Additionally, the edges between this ontology and its dependencies are utilized:</p> <ul> <li><strong><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></strong></li> <li><strong><a href="https://github.com/obophenotype/ncbitaxon"><code>NCBITaxon</code></a></strong></li> <li><strong><a href="https://github.com/oborel/obo-relations/"><code>RO</code></a></strong></li> <li><strong><a href="https://uberon.github.io/"><code>UBERON</code></a></strong></li> </ul> <p><strong>Other Gene Ontology Data Used:</strong> <a href="http://geneontology.org/gene-associations/goa_human.gaf.gz"><code>goa_human.gaf.gz</code></a></p> <p> </p> <p><strong>Human Phenotype Ontology (HPO)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://hpo.jax.org/"><code>https://hpo.jax.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Köhler S, Carmody L, Vasilevsky N, Jacobsen JO, Danis D, Gourdine JP, Gargano M, Harris NL, Matentzoglu N, McMurry JA, Osumi-Sutherland D. <a href="https://academic.oup.com/nar/article-abstract/47/D1/D1018/5198478">Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources</a>. Nucleic Acids Research. 2018;47(D1):D1018-27</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>phenotypes</code> to <code>chemicals</code>, <code>diseases</code>, <code>genes</code>, and <code>variants</code>. Additionally, the edges between this ontology and its dependencies are utilized:</p> <ul> <li><strong><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></strong></li> <li><strong><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></strong></li> <li><strong><a href="http://geneontology.org/"><code>GO</code></a></strong></li> <li><strong><a href="https://uberon.github.io/"><code>UBERON</code></a></strong></li> </ul> <p><strong>Files</strong></p> <ul> <li>Other Human Phenotype Ontology Data Used: <a href="http://purl.obolibrary.org/obo/hp/hpoa/phenotype.hpoa"><code>phenotype.hpoa</code></a></li> </ul> <p> </p> <p><strong>Mondo Disease Ontology (Mondo)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://mondo.monarchinitiative.org/"><code>https://mondo.monarchinitiative.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Mungall CJ, McMurry JA, Köhler S, Balhoff JP, Borromeo C, Brush M, Carbon S, Conlin T, Dunn N, Engelstad M, Foster E. <a href="https://academic.oup.com/nar/article-abstract/45/D1/D712/2605791">The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species</a>. Nucleic Acids Research. 2017;45(D1):D712-22</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>diseases</code> to <code>chemicals</code>, <code>phenotypes</code>, <code>genes</code>, and <code>variants</code>. Additionally, the edges between this ontology and its dependencies are utilized:</p> <ul> <li><strong><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></strong></li> <li><strong><a href="https://www.ncbi.nlm.nih.gov/taxonomy"><code>NCBITaxon</code></a></strong></li> <li><strong><a href="http://geneontology.org/"><code>GO</code></a></strong></li> <li><strong><a href="https://hpo.jax.org/"><code>HPO</code></a></strong></li> <li><strong><a href="https://uberon.github.io/"><code>UBERON</code></a></strong></li> </ul> <p> </p> <p><strong>Pathway Ontology (PW)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://rgd.mcw.edu/wg/home/pathway2/"><code>rgd.mcw.edu</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Petri V, Jayaraman P, Tutaj M, Hayman GT, Smith JR, De Pons J, Laulederkind SJ, Lowry TF, Nigam R, Wang SJ, Shimoyama M. <a href="https://www.ncbi.nlm.nih.gov/pubmed/24499703">The pathway ontology–updates and applications</a>. Journal of Biomedical Semantics. 2014;5(1):7.</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>pathways</code> to <code>GO biological processes</code>, <code>GO cellular components</code>, <code>GO molecular functions</code>, <code>Reactome pathways</code>. Several steps are taken in order to connect <code>Pathway Ontology</code> identifiers to <code>Reactome</code> pathways and <code>GO biological processes</code>. To connect <code>Pathway Ontology</code> identifiers to <code>Reactome</code> pathways, we use <a href="https://github.com/ComPath/resources/tree/master/mappings">ComPath Pathway Database Mappings</a> developed by Daniel Domingo-Fernández (<a href="https://www.ncbi.nlm.nih.gov/pubmed/30564458">PMID:30564458</a>).</p> <p><strong>Files</strong></p> <ul> <li>Downloaded Mapping Data <ul> <li><a href="http://compath.scai.fraunhofer.de/export_mappings"><code>curated_mappings.txt</code></a></li> <li><a href="https://github.com/ComPath/resources/blob/master/mappings/kegg_reactome.csv"><code>kegg_reactome.csv</code></a></li> </ul> </li> <li>Generated Mapping Data <ul> <li><a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/REACTOME_PW_GO_MAPPINGS.txt"><code>REACTOME_PW_GO_MAPPINGS.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Protein Ontology (PRO)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://proconsortium.org/"><code>https://proconsortium.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Natale DA, Arighi CN, Barker WC, Blake JA, Bult CJ, Caudy M, Drabkin HJ, D’Eustachio P, Evsikov AV, Huang H, Nchoutmboube J. <a href="https://academic.oup.com/nar/article-abstract/39/suppl_1/D539/2508558">The Protein Ontology: a structured representation of protein forms and complexes</a>. Nucleic Acids Research. 2010;39(suppl_1):D539-45</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>proteins</code> to <code>chemicals</code>, <code>genes</code>, <code>anatomy</code>, <code>catalysts</code>, <code>cell lines</code>, <code>cofactors</code>, <code>complexes</code>, <code>GO biological processes</code>, <code>GO cellular components</code>, <code>GO molecular functions</code>, <code>pathways</code>, <code>proteins</code>, <code>reactions</code>, and <code>transcripts</code>. Additionally, the edges between this ontology and its dependencies are utilized:</p> <ul> <li><strong><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></strong></li> <li><strong><a href="http://disease-ontology.org/"><code>DOID</code></a></strong></li> <li><strong><a href="http://geneontology.org/"><code>GO</code></a></strong></li> </ul> <p><strong>Notes:</strong> A partial, human-only version of this ontology was used. Details on how this version of the ontology was generated can be found under the Protein Ontology section of the <a href="https://github.com/callahantiff/PheKnowLator/blob/master/notebooks/Data_Preparation.ipynb"><code>Data_Preparation.ipynb</code></a> Jupyter Notebook.</p> <p><strong>Files</strong></p> <ul> <li> <p>Generated Human Version Protein Ontology (PRO)</p> <ul> <li><a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/human_pro.owl"><code>human_pro.owl</code></a> (closed with <a href="http://www.hermit-reasoner.com/">hermit reasoner</a>)</li> </ul> </li> <li> <p>Other PRO Data Used: <a href="https://proconsortium.org/download/current/promapping.txt"><code>promapping.txt</code></a></p> </li> <li> <p>Generated Mapping Data</p> <ul> <li>Merged Gene, RNA, Protein Map: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/Merged_gene_rna_protein_identifiers.pkl"><code>Merged_gene_rna_protein_identifiers.pkl</code></a></li> <li>Ensembl Transcript-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENTREZ_GENE_ENSEMBL_TRANSCRIPT_MAP.txt"><code>ENSEMBL_TRANSCRIPT_PROTEIN_ONTOLOGY_MAP.txt</code></a></li> <li>Entrez Gene-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENTREZ_GENE_PRO_ONTOLOGY_MAP.txt"><code>ENTREZ_GENE_PRO_ONTOLOGY_MAP.txt</code></a></li> <li>UniProt Accession-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/UNIPROT_ACCESSION_PRO_ONTOLOGY_MAP.txt"><code>UNIPROT_ACCESSION_PRO_ONTOLOGY_MAP.txt</code></a></li> <li>STRING-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/STRING_PRO_ONTOLOGY_MAP.txt"><code>STRING_PRO_ONTOLOGY_MAP.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Relations Ontology (RO)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://github.com/oborel/obo-relations/"><code>GitHub</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Smith B, Ceusters W, Klagges B, Köhler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector AL, Rosse C. <a href="https://genomebiology.biomedcentral.com/articles/10.1186/gb-2005-6-5-r46">Relations in biomedical ontologies</a>. Genome Biology. 2005;6(5):R46.</p> </blockquote> <p><strong>Usage:</strong> Utilizing this ontology to connect all data sources in knowledge graph. Additionally, the ontology is queried prior to building the knowledge graph to identify all relations, their inverse properties, and their labels.</p> <p><strong>Files</strong></p> <ul> <li>Generated RO Data <ul> <li><a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/INVERSE_RELATIONS.txt"><code>INVERSE_RELATIONS.txt</code></a></li> <li><a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/RELATIONS_LABELS.txt"><code>RELATIONS_LABELS.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Sequence Ontology (SO)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://github.com/The-Sequence-Ontology/SO-Ontologies"><code>GitHub</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, Ashburner M. <a href="https://link.springer.com/article/10.1186/gb-2005-6-5-r44">The Sequence Ontology: a tool for the unification of genome annotations</a>. Genome Biology. 2005;6(5):R44</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>transcripts</code> and other genomic material like <code>genes</code> and <code>variants</code>.</p> <p><strong>Files</strong></p> <ul> <li>Generated Mapping Data <ul> <li><a href="https://storage.googleapis.com/pheknowlator/curated_data/genomic_sequence_ontology_mappings.xlsx"><code>genomic_sequence_ontology_mappings.xlsx</code></a></li> <li><a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/SO_GENE_TRANSCRIPT_VARIANT_TYPE_MAPPING.txt"><code>SO_GENE_TRANSCRIPT_VARIANT_TYPE_MAPPING.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Uber-Anatomy Ontology (Uberon)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://uberon.github.io/"><code>GitHub</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Mungall CJ, Torniai C, Gkoutos GV, Lewis SE, Haendel MA. <a href="https://genomebiology.biomedcentral.com/articles/10.1186/gb-2012-13-1-r5">Uberon, an integrative multi-species anatomy ontology</a>. Genome Biology. 2012;13(1):R5</p> </blockquote> <p><strong>Usage:</strong> Utilized to connect <code>tissues</code>, <code>fluids</code>, and <code>cells</code> to <code>proteins</code> and <code>transcripts</code>. Additionally, the edges between this ontology and its dependencies are utilized:</p> <ul> <li><strong><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></strong></li> <li><strong><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></strong></li> <li><strong><a href="http://geneontology.org/"><code>GO</code></a></strong></li> <li><strong><a href="https://proconsortium.org/"><code>PRO</code></a></strong></li> </ul> <p> </p> <p><strong>Vaccine Ontology (VO)</strong></p> <p><strong>Homepage:</strong> <strong><a href="http://www.violinet.org/vaccineontology/"><code>http://www.violinet.org/vaccineontology/</code></a></strong><br> <strong>Citations:</strong></p> <blockquote> <p>He Y, Racz R, Sayers S, Lin Y, Todd T, Hur J, Li X, Patel M, Zhao B, Chung M, Ostrow J. <a href="https://academic.oup.com/nar/article-abstract/42/D1/D1124/1053128">Updates on the web-based VIOLIN vaccine database and analysis system</a>. Nucleic Acids Research. 2013;42(D1):D1124-32</p> <p>Xiang Z, Todd T, Ku KP, Kovacic BL, Larson CB, Chen F, Hodges AP, Tian Y, Olenzek EA, Zhao B, Colby LA. <a href="https://academic.oup.com/nar/article-abstract/36/suppl_1/D923/2505793">VIOLIN: vaccine investigation and online information network</a>. Nucleic Acids Research. 2007;36(suppl_1):D923-8</p> </blockquote> <p><strong>Usage:</strong> Utilized the edges between this ontology and its dependencies:</p> <ul> <li><strong><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></strong></li> <li><strong><a href="http://disease-ontology.org/"><code>DOID</code></a></strong></li> <li><strong><a href="http://geneontology.org/"><code>GO</code></a></strong></li> <li><strong><a href="https://proconsortium.org/"><code>PRO</code></a></strong></li> <li><strong><a href="https://uberon.github.io/"><code>UBERON</code></a></strong></li> </ul> <p> </p> <p><strong>DATABASE SOURCES</strong></p> <ul> <li>BioPortal</li> <li>ClinVar</li> <li>Comparative Toxicogenomics Database</li> <li>DisGeNET</li> <li>Ensembl</li> <li>GeneMANIA</li> <li>Genotype-Tissue Expression Project</li> <li>Human Genome Organisation Gene Nomenclature Committee</li> <li>Human Protein Atlas</li> <li>National Center for Biotechnology Information Gene</li> <li>Reactome Pathway Database</li> <li>Search Tool for Recurring Instances of Neighbouring Genes Database</li> <li>Universal Protein Resource Knowledgebase</li> </ul> <p> </p> <p><strong>BioPortal</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://bioportal.bioontology.org/"><code>BioPortal</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>BioPortal. <a href="https://www.bioontology.org/wiki/LOOM">Lexical OWL Ontology Matcher (LOOM)</a></p> <p>Ghazvinian A, Noy NF, Musen MA. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/pmc2815474/">Creating mappings for ontologies in biomedicine: simple methods work</a>. In AMIA Annual Symposium Proceedings 2009 (Vol. 2009, p. 198). American Medical Informatics Association</p> </blockquote> <p><strong>Usage:</strong> BioPortal was utilized to obtain mappings between <code>MeSH identifiers</code> and <code>ChEBI identifiers</code> for <code>chemicals-diseases</code>, <code>chemicals-genes</code>, <code>chemical-GO biological processes</code>, <code>chemicals-GO cellular components</code>, <code>chemicals-GO molecular functions</code>, <code>chemicals-phenotypes</code>, <code>chemicals-proteins</code>, and <code>chemicals-transcripts</code>. Additional information on how this data was processed can be obtained from the <a href="https://gist.github.com/callahantiff/a28fb3160782f42f104e9ec41553af0d"><code>NCBO_rest_api.py</code></a> GitHub Gist script.</p> <p>⭐ <strong>ALTERNATIVE METHOD</strong>⭐ Since the above approach can take over two days to process, we have developed an alternative solution that downloads the <a><code>mesh2021.nt</code></a> data file directly from MeSH and the <a><code>Flat_file_tab_delimited/names.tsv.gz</code></a> file directly from ChEBI. Using these files, we have recapitulated the <a href="https://www.bioontology.org/wiki/BioPortal_Mappings"><code>LOOM</code></a> algorithm implemented by BioPortal when creating mappings between these resources. The procedure is relatively straightforward and utilizes the following information from each resource:</p> <ul> <li>For all MeSH <code>SCR Chemicals</code>, obtain the following information: <ul> <li>Identifiers: MeSH identifiers</li> <li>Labels: string labels using the <code>RDFS:label</code> object property</li> <li>Synonyms: track down all synonyms using the <code>vocab:concept</code> and <code>vocab:preferredConcept</code> object properties</li> </ul> </li> <li>For all ChEBI classes, obtain the following information: <ul> <li>Labels: string labels using the <code>RDFS:label</code> object property</li> <li>Synonyms: track down all synonyms using all <code>synonym</code> object properties</li> </ul> </li> </ul> <p><strong>Files</strong></p> <ul> <li>Generated Data: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/MESH_CHEBI_MAP.txt"><code>MESH_CHEBI_MAP.txt</code></a></li> </ul> <p> </p> <p><strong>ClinVar</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://www.ncbi.nlm.nih.gov/clinvar/"><code>https://www.ncbi.nlm.nih.gov/clinvar/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K. <a href="https://academic.oup.com/nar/article-abstract/46/D1/D1062/4641904">ClinVar: improving access to variant interpretations and supporting evidence</a>. Nucleic Acids Research. 2017;46(D1):D1062-7</p> </blockquote> <p><strong>Usage:</strong> ClinVar was utilized to create <code>variant-gene</code>, <code>variant-disease</code>, and <code>variant-phenotype</code> edges. The original data is filtered such that only records meeting the following criteria were included:</p> <ul> <li> <p><code>Assembly</code> = "GRCh38"</p> </li> <li> <p><code>ClinSigSimple</code> = <code>1</code></p> <ul> <li> <blockquote> <p>1 = at least one current record submitted with an interpretation of Likely pathogenic or Pathogenic (independent of whether that record includes assertion criteria and evidence)"</p> </blockquote> </li> </ul> </li> <li> <p><code>ReviewStatus</code> in ["criteria provided, multiple submitters, no conflicts", "reviewed by expert panel", "practice guideline"]</p> </li> </ul> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data</p> <ul> <li><a href="https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz"><code>variant_summary.txt.gz</code></a></li> <li><a href="https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/var_citations.txt"><code>var_citations.txt</code></a></li> <li><a href="https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/allele_gene.txt.gz"><code>allele_gene.txt.gz</code></a></li> </ul> </li> <li> <p>Generated Edge Data: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/CLINVAR_VARIANT_GENE_DISEASE_PHENOTYPE_EDGES.txt"><code>CLINVAR_VARIANT_GENE_DISEASE_PHENOTYPE_EDGES.txt</code></a></p> </li> </ul> <p> </p> <p><strong>Comparative Toxicogenomics Database (CTD)</strong></p> <p><strong>Homepage:</strong> <strong><a href="http://ctdbase.org/"><code>http://ctdbase.org/</code></a></strong><br> <strong>Citations:</strong></p> <blockquote> <p>Curated [chemical–gene interactions|chemical-go interactions|chemical–disease interactions|gene–pathway interactions] data were retrieved from the Comparative Toxicogenomics Database (CTD), MDI Biological Laboratory, Salisbury Cove, Maine, and NC State University, Raleigh, North Carolina. World Wide Web (URL: <a href="http://ctdbase.org/">http://ctdbase.org/</a>)</p> <p>Davis AP, Grondin CJ, Johnson RJ, Sciaky D, McMorran R, Wiegers J, Wiegers TC, Mattingly CJ. <a href="https://academic.oup.com/nar/article-abstract/47/D1/D948/5106145">The comparative toxicogenomics database: update 2019</a>. Nucleic Acids Research. 2018;47(D1):D948-54</p> </blockquote> <p>Usage: Comparative Toxicogenomics Database (CTD) was utilized to create <code>chemical-disease</code>, <code>chemical-gene</code>, <code>chemical-GO biological process</code>, <code>chemical-GO cellular components</code>, <code>chemical-GO molecular functions</code>, <code>chemical-phenotype</code>, <code>chemical-protein</code>, <code>chemical-rna</code>, and <code>gene-pathway</code> edges. The original data is filtered such that only records meeting the following criteria were included:</p> <ul> <li><code>chemical-disease</code>: <code>DirectEvidence</code> != ""</li> <li><code>chemical-gene</code>: <code>Organism</code> == "Homo sapiens", <code>GeneForms</code> == "gene", and affects not in <code>InteractionActions</code></li> <li><code>chemical-GO biological process</code>: <code>PhenotypeName</code> == "Biological Process" and <code>Interaction</code> <= "1.04e-47" (10th percentile)</li> <li><code>chemical-GO cellular components</code>: <code>PhenotypeName</code> == "Cellular Component" and <code>Interaction</code> <= "1.04e-47" (10th percentile)</li> <li><code>chemical-GO molecular functions</code>: <code>PhenotypeName</code> == "Molecular Function" and <code>Interaction</code> <= "1.04e-47" (10th percentile)</li> <li><code>chemical-phenotype</code>: <code>DirectEvidence</code> != ""</li> <li><code>chemical-protein</code>: <code>Organism</code> == "Homo sapiens", <code>GeneForms</code> == "protein", and affects not in <code>InteractionActions</code></li> <li><code>chemical-rna</code>: <code>Organism</code> == "Homo sapiens", <code>GeneForms</code> == "mRNA", and affects and activity not in <code>InteractionActions</code></li> <li><code>gene-pathway edges</code>: <code>PathwayName</code> == R-HSA-</li> </ul> <p><strong>Files</strong></p> <ul> <li>Downloaded Data <ul> <li>Chemical-Gene Relations: <a href="http://ctdbase.org/reports/CTD_chem_gene_ixns.tsv.gz"><code>CTD_chem_gene_ixns.tsv.gz</code></a></li> <li>Chemical-Disease/Phenotype Relations: <a href="http://ctdbase.org/reports/CTD_chemicals_diseases.tsv.gz"><code>CTD_chemicals_diseases.tsv.gz</code></a></li> <li>Chemical-GO Relations: <a href="http://ctdbase.org/reports/CTD_chem_go_enriched.tsv.gz"><code>CTD_chem_go_enriched.tsv.gz</code></a></li> <li>Gene-Pathway Relations: <a href="http://ctdbase.org/reports/CTD_genes_pathways.tsv.gz"><code>CTD_genes_pathways.tsv.gz</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>DisGeNET</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://www.disgenet.org/"><code>https://www.disgenet.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Gene-disease association data retrieved from DisGeNET v6.0 (<a href="http://www.disgenet.org/">http://www.disgenet.org/</a>), Integrative Biomedical Informatics Group GRIB/IMIM/UPF. [December, 2019].</p> <p>Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, Furlong LI. <a href="https://academic.oup.com/nar/advance-article-abstract/doi/10.1093/nar/gkz1021/5611674">The DisGeNET knowledge platform for disease genomics: 2019 update</a>. Nucleic Acids Research. 2019.</p> </blockquote> <p><strong>Usage:</strong> DisGeNET was utilized to create <code>gene-disease</code>, and <code>gene-phenotype</code> edges. The original data is filtered such that only records meeting the following criteria were included: <code>EI</code> >= "1.0" (90th percentile). Additionally, data from this source was used to create mappings between different types of disease and phenotype identifiers, including:</p> <ul> <li>OMIM, ORPHA, UMLS, ICD ➞ DOID</li> <li>OMIM, ORPHA, UMLS, ICD ➞ HPO</li> </ul> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data</p> <ul> <li>Disease/Phenotype-Gene Relations: <a href="https://www.disgenet.org/static/disgenet_ap1/files/downloads/curated_gene_disease_associations.tsv.gz"><code>curated_gene_disease_associations.tsv.gz</code></a></li> <li>Disease Identifier Mapping: <a href="https://www.disgenet.org/static/disgenet_ap1/files/downloads/disease_mappings.tsv.gz"><code>disease_mappings.tsv.gz</code></a></li> </ul> </li> <li> <p>Generated Mapping Data</p> <ul> <li>Disease Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/PHENOTYPE_HPO_MAP.txt"><code>PHENOTPYE_HPO_MAP.txt</code></a></li> <li>Phenotype Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/DISEASE_MONDO_MAP.txt"><code>DISEASE_DOID_MAP.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Ensembl</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://uswest.ensembl.org/index.html"><code>https://uswest.ensembl.org/index.html</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, Billis K, Cummins C, Gall A, Girón CG, Gil L. <a href="https://academic.oup.com/nar/article/46/D1/D754/4634002">Ensembl 2018</a>. Nucleic Acids Research. 2017;46(D1):D754-61</p> </blockquote> <p><strong>Usage:</strong> Ensembl data was utilized to create mappings between Ensembl genes, transcripts, and proteins with <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#national-center-for-biotechnology-information-gene">NCBI Gene identifiers</a>, <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#human-genome-organisation-gene-nomenclature-committee">HUGO gene symbols</a>, <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#universal-protein-resource-knowledgebase">UniProt Accession identifiers</a>, and <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#protein-ontology">Protein Ontology identifiers</a> in the knowledge graph (for additional details on the processing of these data, see <a href="https://github.com/callahantiff/PheKnowLator/blob/master/notebooks/Data_Preparation.ipynb"><code>Data_Preparation.ipynb</code></a>):</p> <ul> <li>Ensembl Transcript IDs ➞ PRO IDs</li> <li>Gene Ensembl IDs ➞ Entrez Gene IDs</li> <li>Gene Ensembl IDs ➞ PRO IDs</li> <li>Gene Symbols ➞ Transcript Ensembl IDs</li> <li>Entrez Gene IDs ➞ Transcript Ensembl IDs</li> <li>Entrez Gene IDs ➞ PRO IDs</li> <li>Protein Ensembl IDs ➞ UniProt Protein Accession</li> <li>STRING IDs ➞ PRO IDs</li> <li>UniProt Protein Accession ➞ Entrez Gene IDs</li> </ul> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data</p> <ul> <li><a><code>Homo_sapiens.GRCh38.102.gtf</code></a></li> <li><a><code>Homo_sapiens.GRCh38.102.uniprot.tsv.gz</code></a></li> <li><a><code>Homo_sapiens.GRCh38.102.entrez.tsv.gz</code></a></li> </ul> </li> <li> <p>Generated Mapping Data</p> <ul> <li>Cleaned Ensembl Gene Set: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ensembl_identifier_data_cleaned.txt"><code>ensembl_identifier_data_cleaned.txt</code></a></li> <li>Merged Gene, RNA, Protein Map: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/Merged_gene_rna_protein_identifiers.pkl"><code>Merged_gene_rna_protein_identifiers.pkl</code></a></li> <li>Ensembl Transcript-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENSEMBL_TRANSCRIPT_PROTEIN_ONTOLOGY_MAP.txt"><code>ENSEMBL_TRANSCRIPT_PROTEIN_ONTOLOGY_MAP.txt</code></a></li> <li>Gene Symbol-Ensembl Transcript Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/GENE_SYMBOL_ENSEMBL_TRANSCRIPT_MAP.txt"><code>GENE_SYMBOL_ENSEMBL_TRANSCRIPT_MAP.txt</code></a></li> <li>Entrez Gene-Ensembl Transcript Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENTREZ_GENE_ENSEMBL_TRANSCRIPT_MAP.txt"><code>ENTREZ_GENE_ENSEMBL_TRANSCRIPT_MAP.txt</code></a></li> <li>Entrez Gene-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENTREZ_GENE_PRO_ONTOLOGY_MAP.txt"><code>ENTREZ_GENE_PRO_ONTOLOGY_MAP.txt</code></a></li> <li>Ensembl Gene-Entrez Gene Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENSEMBL_GENE_ENTREZ_GENE_MAP.txt"><code>ENSEMBL_GENE_ENTREZ_GENE_MAP.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>GeneMANIA</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://genemania.org/"><code>https://genemania.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A. <a href="https://academic.oup.com/nar/article-abstract/38/suppl_2/W214/1126704">The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function</a>. Nucleic Acids Research. 2010;38(suppl_2):W214-20</p> </blockquote> <p><strong>Usage:</strong> GeneMANIA was utilized to create <code>gene-gene</code> edges.</p> <p><strong>Files</strong></p> <ul> <li>Downloaded Data: <a href="http://genemania.org/data/current/Homo_sapiens.COMBINED/COMBINED.DEFAULT_NETWORKS.BP_COMBINING.txt"><code>COMBINED.DEFAULT_NETWORKS.BP_COMBINING.txt</code></a></li> </ul> <p> </p> <p><strong>Genotype-Tissue Expression Project (GTEx)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://gtexportal.org/home/"><code>https://gtexportal.org/home/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, Hasz R, Walters G, Garcia F, Young N, Foster B. <a href="http://www.nature.com/ng/journal/v45/n6/full/ng.2653.html">The genotype-tissue expression (GTEx) project</a>. Nature Genetics. 2013;45(6):580</p> </blockquote> <p><strong>Usage:</strong> The Genotype-Tissue Expression (GTEx) Project was utilized to create edges between <code>protein-cell</code>, <code>protein-anatomy</code>, <code>rna-cell</code> and <code>rna-anatomy</code> entities. The original data were filtered such that only those edges where the median TPM was >=<code>1.0</code> and genes were of any type other than protein-coding were included. It should also be noted that we chose to use the RNASeQC file over the RSEM file as advised by the GTEx website.</p> <blockquote> <p>The RSEM estimates are based on combining isoform-level estimates, which adds uncertainty to the resulting gene-level values (the isoform-level estimates are highly inaccurate in some cases).</p> </blockquote> <p>The file contains <code>54</code> unique tissue and/or cell types. GTEx provides mappings from tissue types to UBERON and EFO. These provided <a href="https://gtexportal.org/home/samplingSitePage">mappings</a> were verified and extended, such that all samples which referenced a cell type were also mapped to the Cell and the Cell Line ontologies. This resulted in a total of <code>56</code> mappings (<code>1.04</code> mappings/concepts).</p> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data: <a href="https://storage.googleapis.com/gtex_analysis_v8/rna_seq_data/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.gz"><code>GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct</code></a></p> </li> <li> <p>Mapping Results: <a href="https://storage.googleapis.com/pheknowlator/curated_data/zooma_tissue_cell_mapping_04JAN2020.xlsx"><code>zooma_tissue_cell_mapping_04JAN2020.xlsx</code></a></p> </li> <li> <p>Generated Data<br> The final mapping set was combined with terms from the <a href="https://www.proteinatlas.org/">Human Protein Atlas</a>, see <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources/t#human-protein-atlas">here</a> for more information.</p> <ul> <li>All HPA tissue and cell type strings: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/HPA_tissues.txt"><code>HPA_tissues.txt</code></a></li> <li>Final Term Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/HPA_GTEx_TISSUE_CELL_MAP.txt"><code>HPA_GTEx_TISSUE_CELL_MAP.txt</code></a></li> <li>Final RNA, Gene, Protein-Tissues and Cell Types Relations: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/HPA_GTEX_RNA_GENE_PROTEIN_EDGES.txt"><code>HPA_GTEX_RNA_GENE_PROTEIN_EDGES.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Human Genome Organisation Gene Nomenclature Committee (HUGO)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://www.genenames.org/"><code>https://www.genenames.org/</code></a></strong><br> <strong>Citations:</strong></p> <blockquote> <p>HGNC Database, HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom <a href="https://www.genenames.org/">www.genenames.org</a></p> <p>Yates B, Braschi B, Gray K, Seal R, Tweedie S, Bruford E. <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210531/">Genenames.org: the HGNC and VGNC Resources in 2017</a>. Nucleic Acids Research. 2017;45(D1):D619-625</p> </blockquote> <p><strong>Usage:</strong> The Human Genome Organisation (HUGO) data was utilized to obtain mappings between <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#national-center-for-biotechnology-information-gene">NCBI Gene identifiers</a>, HUGO gene symbols, <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#universal-protein-resource-knowledgebase">UniProt Accession identifiers</a>, and <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#protein-ontology">Protein Ontology identifiers</a>. For additional details on the processing of these data, see <a href="https://github.com/callahantiff/PheKnowLator/blob/master/notebooks/Data_Preparation.ipynb"><code>Data_Preparation.ipynb</code></a>:</p> <ul> <li>Ensembl Transcript IDs ➞ PRO IDs</li> <li>Gene Ensembl IDs ➞ Entrez Gene IDs</li> <li>Gene Ensembl IDs ➞ PRO IDs</li> <li>Gene Symbols ➞ Transcript Ensembl IDs</li> <li>Entrez Gene IDs ➞ Transcript Ensembl IDs</li> <li>Entrez Gene IDs ➞ PRO IDs</li> <li>Protein Ensembl IDs ➞ UniProt Protein Accession</li> <li>STRING IDs ➞ PRO IDs</li> <li>UniProt Protein Accession ➞ Entrez Gene IDs</li> </ul> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data: <a href="http://ftp.ebi.ac.uk/pub/databases/genenames/hgnc/tsv/hgnc_complete_set.txt"><code>hgnc_complete_set.txt</code></a></p> </li> <li> <p>Generated Data</p> <ul> <li>Merged Gene, RNA, Protein Map: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/Merged_gene_rna_protein_identifiers.pkl"><code>Merged_gene_rna_protein_identifiers.pkl</code></a></li> <li>Gene Symbol-Ensembl Transcript Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/GENE_SYMBOL_ENSEMBL_TRANSCRIPT_MAP.txt"><code>GENE_SYMBOL_ENSEMBL_TRANSCRIPT_MAP.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Human Protein Atlas (HPA)</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://www.proteinatlas.org/"><code>https://www.proteinatlas.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I. <a href="https://science.sciencemag.org/content/347/6220/1260419.short">Tissue-based map of the human proteome</a>. Science. 2015;347(6220):1260419</p> </blockquote> <p><strong>Usage:</strong> The Human Protein Atlas (HPA) was utilized to create <code>rna-cell</code>, <code>rna-anatomy</code>, <code>protein-cell</code>, and <code>protein-anatomy</code> edges. Evidence between gene and RNA expression in specific tissue types was derived by HPA, such that the <a href="https://www.proteinatlas.org/about/assays+annotation#normalization_rna">consensus normalized expression</a> was >=<code>1.0</code>. Zooma was utilized to automatically annotate the <code>153</code> unique tissues and cell types from Human Protein Atlas for all human protein-coding genes in the <a href="https://www.proteinatlas.org/humanproteome">Human Proteome</a> to the Cell Ontology, Cell Line Ontology, and the Uber-Anatomy Ontology. To best represent each concept, the automatic mappings from Zooma were extend through manual mapping efforts to ensure each concept cell type was matched to a Cell Ontology, Cell Line Ontology, and UBERON ontology term. This resulted in a total of <code>281</code> mappings (<code>1.84</code> mappings/concepts).</p> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data: <a href="https://www.proteinatlas.org/api/search_download.php?search=&columns=g,eg,up,pe,rnatsm,rnaclsm,rnacasm,rnabrsm,rnabcsm,rnablsm,scl,t_RNA_adipose_tissue,t_RNA_adrenal_gland,t_RNA_amygdala,t_RNA_appendix,t_RNA_basal_ganglia,t_RNA_bone_marrow,t_RNA_breast,t_RNA_cerebellum,t_RNA_cerebral_cortex,t_RNA_cervix,_uterine,t_RNA_colon,t_RNA_corpus_callosum,t_RNA_ductus_deferens,t_RNA_duodenum,t_RNA_endometrium_1,t_RNA_epididymis,t_RNA_esophagus,t_RNA_fallopian_tube,t_RNA_gallbladder,t_RNA_heart_muscle,t_RNA_hippocampal_formation,t_RNA_hypothalamus,t_RNA_kidney,t_RNA_liver,t_RNA_lung,t_RNA_lymph_node,t_RNA_midbrain,t_RNA_olfactory_region,t_RNA_ovary,t_RNA_pancreas,t_RNA_parathyroid_gland,t_RNA_pituitary_gland,t_RNA_placenta,t_RNA_pons_and_medulla,t_RNA_prostate,t_RNA_rectum,t_RNA_retina,t_RNA_salivary_gland,t_RNA_seminal_vesicle,t_RNA_skeletal_muscle,t_RNA_skin_1,t_RNA_small_intestine,t_RNA_smooth_muscle,t_RNA_spinal_cord,t_RNA_spleen,t_RNA_stomach_1,t_RNA_testis,t_RNA_thalamus,t_RNA_thymus,t_RNA_thyroid_gland,t_RNA_tongue,t_RNA_tonsil,t_RNA_urinary_bladder,t_RNA_vagina,t_RNA_B-cells,t_RNA_dendritic_cells,t_RNA_granulocytes,t_RNA_monocytes,t_RNA_NK-cells,t_RNA_T-cells,t_RNA_total_PBMC,cell_RNA_A-431,cell_RNA_A549,cell_RNA_AF22,cell_RNA_AN3-CA,cell_RNA_ASC_diff,cell_RNA_ASC_TERT1,cell_RNA_BEWO,cell_RNA_BJ,cell_RNA_BJ_hTERT+,cell_RNA_BJ_hTERT+_SV40_Large_T+,cell_RNA_BJ_hTERT+_SV40_Large_T+_RasG12V,cell_RNA_CACO-2,cell_RNA_CAPAN-2,cell_RNA_Daudi,cell_RNA_EFO-21,cell_RNA_fHDF/TERT166,cell_RNA_HaCaT,cell_RNA_HAP1,cell_RNA_HBEC3-KT,cell_RNA_HBF_TERT88,cell_RNA_HDLM-2,cell_RNA_HEK_293,cell_RNA_HEL,cell_RNA_HeLa,cell_RNA_Hep_G2,cell_RNA_HHSteC,cell_RNA_HL-60,cell_RNA_HMC-1,cell_RNA_HSkMC,cell_RNA_hTCEpi,cell_RNA_hTEC/SVTERT24-B,cell_RNA_hTERT-HME1,cell_RNA_HUVEC_TERT2,cell_RNA_K-562,cell_RNA_Karpas-707,cell_RNA_LHCN-M2,cell_RNA_MCF7,cell_RNA_MOLT-4,cell_RNA_NB-4,cell_RNA_NTERA-2,cell_RNA_PC-3,cell_RNA_REH,cell_RNA_RH-30,cell_RNA_RPMI-8226,cell_RNA_RPTEC_TERT1,cell_RNA_RT4,cell_RNA_SCLC-21H,cell_RNA_SH-SY5Y,cell_RNA_SiHa,cell_RNA_SK-BR-3,cell_RNA_SK-MEL-30,cell_RNA_T-47d,cell_RNA_THP-1,cell_RNA_TIME,cell_RNA_U-138_MG,cell_RNA_U-2_OS,cell_RNA_U-2197,cell_RNA_U-251_MG,cell_RNA_U-266/70,cell_RNA_U-266/84,cell_RNA_U-698,cell_RNA_U-87_MG,cell_RNA_U-937,cell_RNA_WM-115,blood_RNA_basophil,blood_RNA_classical_monocyte,blood_RNA_eosinophil,blood_RNA_gdT-cell,blood_RNA_intermediate_monocyte,blood_RNA_MAIT_T-cell,blood_RNA_memory_B-cell,blood_RNA_memory_CD4_T-cell,blood_RNA_memory_CD8_T-cell,blood_RNA_myeloid_DC,blood_RNA_naive_B-cell,blood_RNA_naive_CD4_T-cell,blood_RNA_naive_CD8_T-cell,blood_RNA_neutrophil,blood_RNA_NK-cell,blood_RNA_non-classical_monocyte,blood_RNA_plasmacytoid_DC,blood_RNA_T-reg,blood_RNA_total_PBMC,brain_RNA_amygdala,brain_RNA_basal_ganglia,brain_RNA_cerebellum,brain_RNA_cerebral_cortex,brain_RNA_hippocampal_formation,brain_RNA_hypothalamus,brain_RNA_midbrain,brain_RNA_olfactory_region,brain_RNA_pons_and_medulla,brain_RNA_thalamus&format=tsv"><code>proteinatlas_search.tsv</code></a></p> </li> <li> <p>Mapping Results: <a href="https://storage.googleapis.com/pheknowlator/curated_data/zooma_tissue_cell_mapping_04JAN2020.xlsx"><code>zooma_tissue_cell_mapping_04JAN2020.xlsx</code></a></p> </li> <li> <p>Generated Data</p> <ul> <li>Final Term Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/HPA_GTEx_TISSUE_CELL_MAP.txt"><code>HPA_GTEx_TISSUE_CELL_MAP.txt</code></a></li> <li>Final RNA, Gene, Protein-Tissues and Cell Types Relations: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/HPA_GTEX_RNA_GENE_PROTEIN_EDGES.txt"><code>HPA_GTEX_RNA_GENE_PROTEIN_EDGES.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>National Center for Biotechnology Information (NCBI) Entrez Gene</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://www.ncbi.nlm.nih.gov/gene/"><code>https://www.ncbi.nlm.nih.gov/gene/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Maglott D, Ostell J, Pruitt KD, Tatusova T. <a href="https://academic.oup.com/nar/article-abstract/33/suppl_1/D54/2505255">Entrez Gene: gene-centered information at NCBI</a>. Nucleic Acids Research. 2005;33(suppl_1):D54-8.</p> </blockquote> <p><strong>Usage:</strong> The National Center for Biotechnology Information (NCBI) Gene data was utilized to obtain mappings between <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#ncbi-gene">NCBI Gene identifiers</a>, <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#hugo-gene-nomenclature-committee">HUGO gene symbols</a>, <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#uniprot-knowledgebase">UniProt Accession identifiers</a>, and <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#protein-ontology">Protein Ontology identifiers</a>. For additional details on the processing of these data, see <a href="https://github.com/callahantiff/PheKnowLator/blob/master/notebooks/Data_Preparation.ipynb"><code>Data_Preparation.ipynb</code></a>:</p> <ul> <li>Ensembl Transcript IDs ➞ PRO IDs</li> <li>Gene Ensembl IDs ➞ Entrez Gene IDs</li> <li>Gene Ensembl IDs ➞ PRO IDs</li> <li>Gene Symbols ➞ Transcript Ensembl IDs</li> <li>Entrez Gene IDs ➞ Transcript Ensembl IDs</li> <li>Entrez Gene IDs ➞ PRO IDs</li> <li>Protein Ensembl IDs ➞ UniProt Protein Accession</li> <li>STRING IDs ➞ PRO IDs</li> <li>UniProt Protein Accession ➞ Entrez Gene IDs</li> </ul> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data: <a href="https://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz"><code>Homo_sapiens.gene_info.gz</code></a></p> </li> <li> <p>Generated Data</p> <ul> <li>Merged Gene, RNA, Protein Map: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/Merged_gene_rna_protein_identifiers.pkl"><code>Merged_gene_rna_protein_identifiers.pkl</code></a></li> <li>Entrez Gene-Ensembl Transcript Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENTREZ_GENE_ENSEMBL_TRANSCRIPT_MAP.txt"><code>ENTREZ_GENE_ENSEMBL_TRANSCRIPT_MAP.txt</code></a></li> <li>Entrez Gene-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENTREZ_GENE_PRO_ONTOLOGY_MAP.txt"><code>ENTREZ_GENE_PRO_ONTOLOGY_MAP.txt</code></a></li> <li>Ensembl Gene-Entrez Gene Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/ENSEMBL_GENE_ENTREZ_GENE_MAP.txt"><code>ENSEMBL_GENE_ENTREZ_GENE_MAP.txt</code></a></li> <li>Uniprot Accession-Entrez Gene Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/UNIPROT_ACCESSION_ENTREZ_GENE_MAP.txt"><code>UNIPROT_ACCESSION_ENTREZ_GENE_MAP.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Reactome Pathway Database</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://reactome.org/"><code>https://reactome.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B, Milacic M. <a href="https://academic.oup.com/nar/article-abstract/46/D1/D649/4626770">The reactome pathway knowledgebase</a>. Nucleic Acids Research. 2017;46(D1):D649-55</p> </blockquote> <p><strong>Usage:</strong> The Reactome Database was utilized to create <code>chemical-pathway</code>, <code>GO Biological process-pathway</code>, <code>pathway-GO Cellular component</code>, <code>GO Molecular function-pathway</code>, and <code>protein-pathway</code> edges. The original data is filtered such that only records meeting the following criteria were included:</p> <ul> <li><code>chemical-pathway</code>: column[5] == "Homo sapiens"</li> <li><code>GO Biological process-pathway</code>: column[5] startswith "REACTOME", column[8] == "P", and column[12] == "taxon:9606"</li> <li><code>pathway-GO Cellular component</code>: column[5] startswith "REACTOME", column[8] == "C", and column[12] == "taxon:9606"</li> <li><code>GO Molecular function-pathway</code>: column[5] startswith "REACTOME", column[8] == "F", and column[12] == "taxon:9606"</li> <li><code>protein-pathway</code>: column[5] == "Homo sapiens"</li> </ul> <p><strong>Files</strong></p> <ul> <li>Downloaded Data <ul> <li>Chemical-Pathway Relations: <a href="https://reactome.org/download/current/ChEBI2Reactome_All_Levels.txt"><code>ChEBI2Reactome_All_Levels.txt</code></a></li> <li>Pathway-GO Relations: <a href="https://reactome.org/download/current/gene_association.reactome"><code>gene_association.reactome</code></a></li> <li>Protein-Pathway Relations: <a href="https://reactome.org/download/current/UniProt2Reactome_All_Levels.txt"><code>UniProt2Reactome_All_Levels.txt</code></a></li> </ul> </li> </ul> <p> </p> <p><strong>Search Tool for Recurring Instances of Neighbouring Genes (STRING) Database</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://string-db.org/"><code>string-db.org</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ. <a href="https://academic.oup.com/nar/article-abstract/47/D1/D607/5198476">STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets</a>. Nucleic Acids Research. 2018;47(D1):D607-13</p> </blockquote> <p><strong>Usage:</strong> The Search Tool for Recurring Instances of Neighbouring Genes (STRING) Database was utilized to create <code>protein-protein</code> edges. The original data is filtered such that only records meeting the following criteria were included: <code>combined_score</code> >= "700" (>90th percentile).</p> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data: <a href="https://stringdb-static.org/download/protein.links.v11.0/9606.protein.links.v11.0.txt.gz"><code>9606.protein.links.v11.0.txt.gz</code></a></p> </li> <li> <p>Generated Data: STRING-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/STRING_PRO_ONTOLOGY_MAP.txt"><code>STRING_PRO_ONTOLOGY_MAP.txt</code></a></p> </li> </ul> <p> </p> <p><strong>Universal Protein Resource (UniProt) Knowledgebase</strong></p> <p><strong>Homepage:</strong> <strong><a href="https://www.uniprot.org/"><code>https://www.uniprot.org/</code></a></strong><br> <strong>Citation:</strong></p> <blockquote> <p>UniProt Consortium. <a href="https://academic.oup.com/nar/article-abstract/47/D1/D506/5160987">UniProt: a worldwide hub of protein knowledge</a>. Nucleic acids research. 2018;47(D1):D506-15</p> </blockquote> <p><strong>Usage:</strong> The Universal Protein Resource (UniProt) Knowledgebase was utilized to obtain <code>cofactor</code>/<code>catalyst</code>-<code>protein</code> and <code>protein-coding gene</code>-<code>protein</code> edges as well as mappings between <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#national-center-for-biotechnology-information-gene">NCBI Gene identifiers</a>, <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#human-genome-organisation-gene-nomenclature-committee">HUGO gene symbols</a>, <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#universal-protein-resource-knowledgebase">Universal Protein Resource (UniProt) Accession identifiers</a>, and <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources#protein-ontology">Protein Ontology identifiers</a>. For additional details on the processing of these data, see <a href="https://github.com/callahantiff/PheKnowLator/blob/master/notebooks/Data_Preparation.ipynb"><code>Data_Preparation.ipynb</code></a>:</p> <ul> <li>Ensembl Transcript IDs ➞ PRO IDs</li> <li>Gene Ensembl IDs ➞ Entrez Gene IDs</li> <li>Gene Ensembl IDs ➞ PRO IDs</li> <li>Gene Symbols ➞ Transcript Ensembl IDs</li> <li>Entrez Gene IDs ➞ Transcript Ensembl IDs</li> <li>Entrez Gene IDs ➞ PRO IDs</li> <li>Protein Ensembl IDs ➞ UniProt Protein Accession</li> <li>STRING IDs ➞ PRO IDs</li> <li>UniProt Protein Accession ➞ Entrez Gene IDs</li> </ul> <p><strong>Files</strong></p> <ul> <li> <p>Downloaded Data</p> <ul> <li>Cofactor and Catalyst relations: <a href="https://www.uniprot.org/uniprot/?query=&fil=organism%3A%22Homo%20sapiens%20(Human)%20%5B9606%5D%22&columns=id%2Centry%20name%2Creviewed%2Cdatabase(PRO)%2Cchebi(Cofactor)%2Cchebi(Catalytic%20activity)"><code>Cofactor/Catalyst Query Results</code></a></li> <li>UniProt Identifier Mapping: <a href="https://www.uniprot.org/uniprot/?query=&fil=organism%3A%22Homo%20sapiens%20(Human)%20%5B9606%5D%22&columns=id%2Cdatabase(GeneID)%2Cdatabase(Ensembl)%2Cdatabase(HGNC)%2Cgenes(PREFERRED)%2Cgenes(ALTERNATIVE)"><code>UniProt Identifier Query Results</code></a></li> </ul> </li> <li> <p>Generated Data</p> <ul> <li>Merged Gene, RNA, Protein Map: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/Merged_gene_rna_protein_identifiers.pkl"><code>Merged_gene_rna_protein_identifiers.pkl</code></a></li> <li>Protein-Cofactor Relations: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/UNIPROT_PROTEIN_COFACTOR.txt"><code>UNIPROT_PROTEIN_COFACTOR.txt</code></a></li> <li>Protein-Catalyst Relations: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/UNIPROT_PROTEIN_CATALYST.txt"><code>UNIPROT_PROTEIN_CATALYST.txt</code></a></li> <li>UniProt Accession-PRO Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/UNIPROT_ACCESSION_PRO_ONTOLOGY_MAP.txt"><code>UNIPROT_ACCESSION_PRO_ONTOLOGY_MAP.txt</code></a></li> <li>UniProt Accession-Entrez Gene Identifier Mapping: <a href="https://storage.googleapis.com/pheknowlator/current_build/data/processed_data/UNIPROT_ACCESSION_ENTREZ_GENE_MAP.txt"><code>UNIPROT_ACCESSION_ENTREZ_GENE_MAP.txt</code></a></li> </ul> </li> </ul> <p> </p> <p>This project is licensed under Apache License 2.0 - see the <strong><a href="https://github.com/callahantiff/PheKnowLator/blob/master/LICENSE"><code>LICENSE.md</code></a></strong> file for details. If you intend to use any of the information on this Wiki, please provide the appropriate attribution by citing this repository:</p> <pre><code>@misc{callahan_tj_2019_3401437, author = {Callahan, TJ}, title = {PheKnowLator}, month = mar, year = 2019, doi = {10.5281/zenodo.3401437}, url = {https://doi.org/10.5281/zenodo.3401437} }</code></pre>
Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks.
<p>These data sets can be used to re-run the experiments from our paper, Protein Interface Prediction using Graph Convolutional Networks. The data are derived from protein complexes in the docking benchmark dataset v. 5.0. Each file is a python tuple that has been saved using cPickle and compressed using gzip.</p> <p>Links:</p> <p>Paper: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks</p> <p>Poster: https://zenodo.org/record/1134154</p> <p>Code: https://github.com/fouticus/pipgcn</p> <p> </p> <p><strong>File Descriptions:</strong></p> <p>train.cpkl.gz and test.cpkl.gz have the data formatted for neighborhood based graph convolutions. The diffc_ files are the same data formatted for the diffusion convolutional neural networks that we compare against. </p> <p>train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of length 175 containing the PDB codes from the docking benchmark dataset</li> <li>element 1 is a list of length 175 containing features for each protein. Each element is a dictionary containing the following keys: <ul> <li>r_vertex: vertex (residue) features for the receptor. numpy array of shape (x, 70) where x is the number of residues in the receptor and 70 is the number of features.</li> <li>l_vertex: vertex (residue) features for the ligand. analogous to above, with shape (y, 70) where y is the number of residues in the ligand.</li> <li>complex_code: PDB code of the complex. matches the list of codes described above.</li> <li>l_edge: edge features for the neighborhood around each residue in the ligand. numpy array of shape (y, 20, 2) where y is defined as above. the second dimension is the edges to the 20 nearest neighboring residues, ordered by decreasing distance. The third dimension allows for two features per edge. </li> <li>r_edge: edge features for the neighborhood around each residue in the receptor. numpy array of shape (x, 20, 2) where x is as above. </li> <li>l_hood_indices: the index of the 20 closest residues to each residue, ordered by decreasing distance. numpy array of shape (y, 20, 1). "Index" means which row in l_vertex gives the vertex features for the closest neighbor, second closest neighbor, etc. </li> <li>r_hood_indices: analogous to above, shape (x, 20, 1).</li> <li>label: 1 or -1 label for each residue pair. numpy array of shape (x*y, 3). Each row looks like (i, j, k) where i is the index of the ligand residue, j is the index of the receptor residue, and k is either -1 (negative example) or 1 (positive example).</li> </ul> </li> </ul> <p>test.cpkl.gz matches the structure of train.cpkl.gz except it has the test set of 55 complexes. </p> <p>Descriptions of the vertex and edge features can be found in Appendix A of <a href="https://mountainscholar.org/handle/10217/185661">this.</a></p> <p>diffc_g2_p2_train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of the same 175 PDB codes as above. </li> <li>element 1 is a list of features for the 175 complexes. Each element is a dictionary of features with these keys: <ul> <li>r_vertex, l_vertex, complex_code, label: these are the same as described above. </li> <li>'r_power_series': Stacked diffusion matrices which are powers of the similarity matrix used in the DCNN method. numpy array of shape (x, 2, x) where x is the number of receptor residues. the middle dimension 2 indicates how many "hops" is used for that diffusion (1 vs. 2). In other words, element (i, 0, j) is the similarity after 1 hops between residues i and j. element (i, 1, j) is the similarity after 2 hops. See DCNN paper for details.</li> <li>'l_power_series': same as above but for the ligand. shape is (y, 2, y).</li> </ul> </li> </ul> <p>diffc_g2_p2_test.cpkl.gz is the same as diffc_g2_p2_train.cpkl.gz but for the 55 test complexes.</p> <p>diff_g2_p5_train.cpkl.gz and diff_g2_p5_test.cpkl.gz are the same as the p2 version above, except that the diffusion matrices have shape (x, 5, x) and (y, 5, y) because one of our comparisons against the DCNN model uses 5 hops instead of just 2. </p> <p> </p> <p>Note: these files were pickled with Python 2.7. If you're unpickling with Python 3.x you might have to specify encoding as 'latin1'. </p> <p> </p> <p>Please direct any questions to:</p> <ul> <li>Alex Fout (fout@colostate.edu)</li> <li>Jonathon Byrd (jonbyrd@colostate.edu)</li> <li>Basir Shariat (basir@cs.colostate.edu</li> <li>Asa Ben-Hur (asa@cs.colostate.edu)</li> </ul>
Bi-equivalent planar graphs data files
<p>The archive file contains files describing every bi-equivalent planar graph.<br>Each folder of the type LM contains the graph of valencies L and M.<br>Each sub-folder in these folder correspond to 1 graph.<br>Their content is:<br>- f.par : the PGC for the graph<br>- a txt file: file describing the connectivity of the graph (see below for the format).<br>- a svg file: vector graphic file for the graph.</p> <p>Syntax of the txt file:<br>Line 1 : the number of nodes for the graph<br>Line 2 : the nodes for the outside face (usually "0 1 2 P-1")<br>Following lines except last 2 lines: "n1 n2" : the indices of 2 nodes linked together.<br>Line -2 from the end: "N0 n1 n2 ... nn" : the list of nodes of the 1st type.<br>Last line: "N1 n1 n2 ... nn", the list of nodes of the 2nd type.</p>
Data for Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery
<p><strong>Description of the datasets</strong></p> <p>Data are organized as folders and compressed with tar.gz.</p> <p>There are two compressed data folder: <strong>data </strong>which used for cattle genome graphs experiment and <strong>data_human</strong> which we used for human genome graphs experiment. </p> <p><strong>Cattle genome graphs experiments</strong></p> <p>First you need to unzip the file using command <em>tar -xvzf data.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>Utilities: contain bovine ARS-UCD 1.2 fasta reference with the accompanying index.</li> <li>Bin: contain the softwares used in the paper (vg, liftover, vcf2diploid)</li> <li>Part1: data for analysis in variant prioritization section, further subdivided into: <ul> <li>vcf_sim: variant files from four animal in each breed used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul> </li> <li>Part2: data used for analysis in the section of graph mapping with breeds-filtered variants, further subdivided into: <ul> <li>vcf_breed: variant files used to graphs construction.</li> </ul> </li> <li>Part3: data used for analysis in the section of consensus genome, further subdivided into: <ul> <li>read_sims: simulated reads as in the part1, but the coordinates are liftovered to the new consensus genomes.</li> <li>reference: contain the original reference and consensus references.</li> <li>vcf_consensus: contain major allele variants to construct consensus genomes.</li> </ul> </li> <li>Part4: data analysis in the section of whole genome graph construction and variant genotyping. <ul> <li>vcf_construct: variants from chromosome 1-29 from 82 Brown Swiss used to construct BSW whole genome graph.</li> <li>BSW_graph: whole genome Brown Swiss graph with the three accompanying indexes (xg,gcsa, and gbwt).</li> </ul> </li> </ul> <p><strong>Human genome graphs experiments</strong></p> <p>First you need to unzip the <em>data_human</em> file using command <em>tar -xvzf data</em><em>_hum.tar.gz</em>. After unzipping, the data folder is organized as follows:</p> <ul> <li>reference: the g1k_v37 reference used as a graph backbone</li> <li>vcf_sim: variant files from four individuals in each population used to simulate reads</li> <li>reads_sim: simulated short reads used for read mapping</li> <li>vcf_freq: variants augmented to graphs filtered based on allele frequency</li> </ul>
Supporting data for Novel functional sequences uncovered through a bovine multi-assembly graph
<p><strong>Description of the datasets</strong></p> <p>Data are organized as a folder and compressed with tar.gz.</p> <p>You need to unzip the folder using the command <em>tar -xz</em><em>v</em><em>f</em> data.tar.gz. Unzipping will output a folder named <em>data_tidy</em>, which is organized as follow:</p> <ul> <li>graph.gfa : Graph in GFA format constructed from 6 cattle assemblies</li> <li>nonref.fa : Non-reference sequences extracted from the graph</li> <li>nonref.fa.masked: Hard masked repetitive regions version of nonref.fa</li> <li>nonref_woflanking.fa: Nonref.fa without flanking sequences</li> <li>nonref_woflanking.fa.masked: Masked version of nonref_woflanking.fa</li> <li>augustus_predict.gtf: Annotated gene models of Augustus from non-ref sequences</li> <li>augustus_prot.fa: Protein fasta of the predicted gene models from Augustus</li> <li>breeds_assembled.gtf: Annotation of the StringTie assembled across-breed transcriptome</li> <li>breeds_expressed.tsv: Expression data of breeds_assembled.gtf</li> <li>de_assembled.gtf: Annotation of the StringTie assembled differentially-expressed transcriptome on non-ref sequences</li> <li>de_expression.tsv: Differential expression results from de_assembled.gtf</li> <li>variant_nonref.tsv: Variants called from non-ref sequences (-1, 0, 1, 2 indicates no call, hom ref, het, and hom alt respectively)</li> </ul>
Data Set Knowledge Graph (DSKG)
<p>We present the <strong>Data Set Knowledge Graph (<a href="http://dskg.org">DSKG.org</a>)</strong>, an <strong>RDF</strong> <strong>dataset about datasets </strong>that are <strong>linked to publications</strong> (modeled in the Microsoft Academic Knowledge Graph, MAKG) that mention the datasets. The metadata of the datasets is based on datasets that are registered in <strong>OpenAIRE</strong> and <strong>Wikidata</strong>.</p> <p><strong>What exactly do we provide?</strong></p> <ol> <li>Periodically updated <strong><a href="http://dskg.org">RDF dump files</a></strong> of the Data Set Knowledge Graph.</li> <li><strong><a href="http://dskg.org">URI resolution</a></strong> of the Data Set Knowledge Graph within the Linked Open Data.</li> <li>A publicly accessible <strong><a href="http://dskg.org">SPARQL endpoint</a></strong> containing the latest Dataset Knowledge Graph data.</li> </ol> <p><strong>How big is the Dataset Knowledge Graph?</strong></p> <p>The <a href="http://dskg.org">Dataset Knowledge Graph</a> models, among others,</p> <ul> <li>2,208 datasets from all scientific disciplines</li> <li>813,551 links to 634,803 unique papers</li> <li>1,169 authors of datasets</li> <li>208 ORCID IDs.</li> </ul> <p><strong>Potential use cases:</strong></p> <ul> <li>Use the DSKG for the development of semantic search engines (e.g. use the metadata of the linked publications of the datasets for advanced search capabilities)</li> <li>Easier data integration by using the RDF standard vocabulary DCAT and by linking resources to other data sources (e.g., combining the DSKG with other dataset collections in RDF).</li> <li>Data analysis to measure and award the provisioning of datasets (e.g., determine the scientific influence of datasets and authors).</li> </ul>
Graphing and tabulating next-generation sequencing and genotyping data
<p>Making figures and tables for publication. Each zip archive contains input data, shell script to initiate and log R script, one R script for generating several graphs and tables, and the output graphs and tables themselves.</p> <p>Data was generated by whole-genome resequencing of 22 individual D.melanogaster from Sussex-LHM population and 2 from the Sussex RG line, followed by read-mapping, then genotyping with Haplotype Caller and Genomestrip.</p> <p>Locations for raw data, code, logs, extended QC data:</p> <p>Sequence reads NCBI SRA268956</p> <p>NCBI dbSNP https://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1062461</p> <p>NCBI dbVar accession number pre-release nstd134</p> <p> </p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p> <p> </p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Added graph to previous Alsop data set
<p>A previous plot compared CHU 14.67 MHz Doppler shift between the day before and day of eclipse.</p> <p>I also had available Doppler shift data from 8/19 (two days before). I added that to the graph to perhaps help detect or dismiss a previously observed effect. The two days before the eclipse were pretty comparable from a Doppler Shift standpoint.</p>
Data Set for the Journal Article "Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates"
<p>This repository containes the data generated by ASAP and discussed in the journal article [Csizi, K.-S. and Reiher, M., 2023, arXiv:2307.16344], including Cartesian coordinates of training and test set molecules, and MD trajectories. </p>
SemTab 2024: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching Data Sets - WikidataTables2024R1 and WikidataTables2024R2
<p>Data Sets from the ISWC 2024 Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, Round 1, Wikidata Tables. Links to other datasets can be found on the challenge website: https://sem-tab-challenge.github.io/2024/ as well as the proceedings of the challenge published on CEUR.</p> <p>For details about the challenge, see: http://www.cs.ox.ac.uk/isg/challenges/sem-tab/</p> <p>For 2024 edition, see: https://sem-tab-challenge.github.io/2024/</p> <p>Note on License: This data includes data from the following sources. Refer to each source for license details:<br>- Wikidata https://www.wikidata.org/</p> <p>THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</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.