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915 results for “graphs”
Data from: Improving quartet graph construction for scalable and accurate species tree estimation from gene trees
<p>Summary methods are one of the dominant approaches for estimating species trees from genome-scale data. However, they can fail to produce accurate species trees when the input gene trees are highly discordant due to gene tree estimation error as well as biological processes, like incomplete lineage sorting. Here, we introduce a new summary method TREE-QMC that offers improved accuracy and scalability under these challenging scenarios. TREE-QMC builds upon the algorithmic framework of QMC (Snir and Rao 2010) and its weighted version wQMC (Avni et al. 2014). Their approach takes weighted quartets (four-leaf trees) as input and builds a species tree in a divide-and-conquer fashion, at each step constructing a graph and seeking its max cut. We improve upon this methodology in two ways. First, we address scalability by providing an algorithm to construct the graph directly from the input gene trees. By skipping the quartet weighting step, TREE-QMC has a time complexity of O(n^3 k) with some assumptions on subproblem sizes, where n is the number of species and k is the number of gene trees. Second, we address accuracy by normalizing the quartet weights to account for "artificial taxa," which are introduced during the divide phase so that solutions on subproblems can be combined during the conquer phase. Together, these contributions enable TREE-QMC to outperform the leading methods (ASTRAL-III, FASTRAL, wQFM) in an extensive simulation study. We also present the application of these methods to an avian phylogenomics data set.</p>
Space of Optimal Solutions of the Correlation Clustering Problem for Complete Signed Graphs
<p><strong>Description. </strong>This is the data used in the experiments of the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs,” <em>Journal of Complex Networks </em>8(6):cnaa025, 2020. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02994011">hal-02994011</a>⟩</li> </ul> <p>This dataset contains:</p> <ul> <li>Plot files used in the article;</li> <li>Input signed networks;</li> <li>All optimal solutions (i.e. optimal solution space) of the corresponding networks;</li> <li>Evaluation files.</li> </ul> <p><strong>Source code. </strong>The code source is accessible on GitHub: <a href="https://github.com/CompNet/Sosocc">https://github.com/CompNet/Sosocc</a></p> <p><strong>Citation. </strong>If you use the data or source code, please cite the above article.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs},</code><br><code> journal = {Journal of Complex Networks},</code><br><code> year = {2020},</code><br><code> volume = {8},</code><br><code> number = {6},</code><br><code> pages = {cnaa025},</code><br><code> doi = {10.1093/comnet/cnaa025},</code><br><code>}</code><br><br></p> <p>--------------------------------------------</p> <p><strong>Details.</strong></p> <p><br><strong># PLOT FILES</strong><br>* `<em>Figure1.zip</em>`: Figures showing that there might be many distinct optimal solutions of a small-sized network.<br>* `<em>Figure2.zip</em>`: Figures showing that distinct optimal solutions of a given network might be partition-wise very similar or different.<br>* `<em>Figure4: All Results.zip</em>`: Figure 4 in the article contains only a few plots regarding the results for space considerations. This zip file contains all plots, and it is organized by the values of `<em>l<sub>0</sub></em>`. In each `<em>l<sub>0</sub></em>` folder, the results are shown in three different perspectives:<br>--- Detected Imbalance Percentage vs Graph Order (i.e. number of vertices)<br>--- Prop mispl vs Graph order<br>--- Graph order vs Prop mispl<br>* `<em>workflow.pdf</em>`: The workflow of the methodology used in the article.<br>* `<em>Syrian network With All Solutions.pdf</em>`: Syrian network (on top) with core part information through node colors, and its optimal solutions in which node colors represent partition information (on bottom).<br> </p> <p><strong>#NETWORKS</strong><br>All networks are in `<em>Input Signed Networks.tar.gz</em>`.<br>Networks are generated through a simple random model (available in <em>https://github.com/CompNet/SignedBenchmark</em>) designed to produce complete (or uncomplete) unweighted networks with built-in modular structure.<br>There are 3 parameters used for the generation:</p> <ol> <li>number of nodes (`<em>n</em>`)</li> <li>initial number of modules (`<em>l<sub>0</sub></em>`)</li> <li>proportion of misplaced links, i.e. proportion of frustrated links, (`<em>q<sub>m</sub></em>`)</li> </ol> <p>Inside `<em>Input Signed Networks.tar.gz</em>`:<br>NETWORKS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>The number of nodes, the initial number of modules and the network density are given. The network density is always 1, since we treat only complete signed networks.<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>Proportion of misplaced links is given.<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>Proportion of negative links (`<em>q<sub>n</sub></em>`) is specified. `<em>q<sub>n</sub></em>` changes depending on `<em>n</em>` and `<em>l<sub>0</sub></em>`. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters.<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>Network numbers are shown.<br>In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format.<br><br><strong># PARTITIONS</strong><br>All partition results are in `<em>Partition Results.tar.gz</em>`. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: <em>https://github.com/arinik9/ExCC</em><br>Inside `<em>Partition Results.tar.gz</em>`:<br><br>PARTITIONS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br>................|__"<em>ExCC-all</em>"<br>....................|__"<em>signed-unweighted</em>"<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>- The fifth hierarchy => the folders are named as follows: "<em>ExCC-all</em>"<br>The name of the partitioning method are shown. Since an exact partitioning method is used to obtain all distinct optimal solutions, it is named as "<em>ExCC-all</em>".<br>- The sixth hierarchy => the folders are named as follows: "<em>signed-unweighted</em>"<br>The type of signed networks are shown: signed and unweighted</p> <p>In the end, the partition results are located, and the file names are named as follows: <em>membership.txt</em>. Note that the first partition result number starts from zero.</p> <p> </p> <p><strong># EVALUATIONS</strong><br>Evaluation results related to our plots are in `<em>Evaluation Results.tar.gz</em>. Note that the hierarchy of this folder is the same as that of 'Partitions'. Inside `<em>Evaluation</em><em> Results.tar.gz</em>`:</p> <p>- `Best-k-for-kmedoids.csv`: It contains three columns. 1) the number of solution classes via kmedoids, 2) the best Silhouette score, 3) the best clustering in terms of Silhouette score, which represents solution classes.</p> <p>- `class-core-part-size-tresh=1.00.csv`. It indicates the proportion of core part size for each solution class.</p> <p>- `exec-time.csv`: It indicates the execution time in seconds.</p> <p>- `imbalance.csv`: It contains the information of imbalance as 1) count and 2) percentage</p> <p>- `nb-solution.csv`: It indicates the total number of solutions<br>--------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
Biodiversity Metadata Knowledge Graph (BMKG)
<p>Biodiversity Metadata Knowledge Graph (BMKG) has the <a href="https://doi.org/10.5281/zenodo.6948519">Biodiversity Metadata Ontology (BMO)</a> as its underlying schema. BMKG has 18 datasets instances. The BMKG is automatically generated using an embedding-based technique under the scope of <a href="https://github.com/fusion-jena/Meta2KG">Meta2KG </a>project.</p>
Knowledge Graph 'Bergbeschau: Schwaz / Rottenburg / Rattenberg' Tyrol, 17th Century
<p>The dataset contain a .nq file of a Knowledge Graph that is based on the extracted information / data of the historical document “Bergbeschau Schwaz/Rottenburg/Rattenberg” of the 17<sup>th</sup> century (approx. 1666). The document is currently stored by the Salinenarchiv Bad Ischl) using the Identifier XXD6.</p> <p>The dataset also encludes the affiliated RDF files and .csv files.</p> <p>The user of the KG may explore the mines associated mine sections as well as places of the historical mining areas in the mining district of Schwaz (Falkenstein, Ringenwechsel, Mehren, Reichental, Palleiten) as well as the mining district of Rattenberg (Groß-, Kleinkogel, Geyer).</p> <p>The ontology used to represent the claims is CIDOC CRM, an ISO certified ontology for Cultural Heritage documentation. Supported by the Karma tool the data is generated as RDF (Resource Description Framework). The generated RDF data is imported into a Triplestore, in this case GraphDB, and then displayed visually. This puts the data from the early mining texts into a semantically structured context and enables the exploration of a historical mining document from another perspective.</p>
LRGB: Long Range Graph Benchmark
<p>Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for learning a given task on graphs. Recently, there has been an increasing interest in development of Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI. However, MP-GNNs that simply rely on 1-hop message passing often fare better in several existing graph benchmarks when combined with positional feature representations, among other innovations, hence limiting the perceived utility and ranking of Transformer-like architectures. Here, we present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets: PascalVOC-SP, COCO-SP, PCQM-Contact, Peptides-func and Peptides-struct that arguably require LRI reasoning to achieve strong performance in a given task. We benchmark both baseline GNNs and Graph Transformer networks to verify that the models which capture long-range dependencies perform significantly better on these tasks. Therefore, these datasets are suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.</p> <p><a href="https://arxiv.org/abs/2206.08164">arXiv</a>, <a href="https://paperswithcode.com/paper/long-range-graph-benchmark">Papers with Code</a></p>
TwiBot22: Towards Graph-Based Twitter Bot Detection
<p>Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect.</p> <p>However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from <strong>limited dataset scale</strong>, <strong>incomplete graph structure</strong>, as well as <strong>low annotation quality</strong>. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches.</p> <p>In this paper, we propose <strong>TwiBot-22</strong>, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets.</p> <p>In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress.</p> <p>To facilitate further research, we consolidate all implemented codes and datasets into <strong>the TwiBot-22 evaluation framework</strong>, where researchers could consistently evaluate new models and datasets.</p> <p>Due to the limitation at Zenodo, you can access the whole dataset and more information on this <a href="https://twibot22.github.io/">website</a>.</p>
PheKnowLator Human Disease Knowledge Graphs - Build Data (Original)
<p><strong>RELEASE V2.1.0 KNOWLEDGE GRAPH: ORIGINAL 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>
Life tables and graphs for Bahry (2022) - Equilibrium conditions in the evolution of senescence [MSc thesis, Carleton Univeristy]
<p>Life table data, and derived quantities, for <em>Equilibrium Conditions in the Evolution of Senescence</em> (Bahry, 2022, MSc thesis); adapted from the supplementary data of (Jones et al., 2014). Life table data for human (Japan 2009), human (Aché hunter-gatherer), fruit fly, Soay sheep, freshwater hydra, and desert tortoise.</p> <p>Basic life table quantities: age interval <span class="math-tex">\((X)\)</span>; survival function <span class="math-tex">\((l_X)\)</span>; and age-specific interval fecundity <span class="math-tex">\((m_X)\)</span>. Derived quantities include interval average force of mortality; reproductive value; residual reproductive value; Hamilton's indicators of the age-specific forces of selection; and actual age-specific mortality vs. predicted age-specific mortality based on models treated in (Bahry, 2022).</p> <p>In the original life tables of Jones et al. (2014), desert tortoises negatively senesce over the range of observed ages, but had a final observed cut-off age of 74; this causes reproductive value to artifactually fall to 0 as age-approached the cutoff. To get around this, I also used an extrapolated desert tortoise life table, assuming the age-74 mortality and fecundity rates remained constant until age 1000, then using the extrapolated life table to calculate reproductive value (and Hamilton's indicators) up to the cutoff age 74.</p> <p><strong>References</strong></p> <p>Bahry, D. (2022). <em>Equilibrium Conditions in the Evolution of Senescence</em> [Master's thesis, Carleton University].</p> <p>Jones, O. R. et al. (2014). Diversity of ageing across the tree of life. <em>Nature</em> 505: 169–174. https://doi.org/10.1038/nature12789</p>
Data From: Emulation of Cardiac Mechanics using Graph Neural Networks
<p>Contains simulation results of the forward displacement from beginning to end-diastole for approximately 3000 synthetically generated left ventricle geometries.</p> <p>The simulation results are split into training, validation and test data.</p> <p>The data is described in detail in a forthcoming publication in <em>Computer Methods in Applied Mechanics and Engineering</em> - further information will be provided upon publication. A GitHub repository will also be made available, with code for processing the simulation data and training a Graph Neural Network emulator.</p>
Graphs of redirection: an examination of URIs in identity graphs
<p>This is the dataset for our paper</p> <p><strong>What does it mean when your URIs are redirected? Examining identity and redirection in the LOD cloud</strong></p> <p> </p> <p>Redirection of URIs is widely used in the LOD cloud, and is even part of the best practice guidelines as an approach to the ``curation problem'' on the semantic web (i.e. how to repair imperfections). When dereferencing, one URI is redirected to another URI. Such a redirection could be the result of an update of the namespace, a different encoding scheme, or some other reasons. In this paper, we study the semantics of redirection and examine if redirection indicates how entities in the LOD cloud evolve. More specifically, we focus on entities in the identity graphs: subgraphs in the semantic web restricted to identity links. The entities we study are from sameAs.cc, an identity graph extracted from a crawl of the semantic web in 2015. Our analytical results include an examination of edges and chains of redirection as well as a statistical analysis of the redirection behavior of sampled entities. Additionally, we present properties of the graphs formed by redirection relations. </p> <p> </p> <p>The dataset contains the redirect relations of four sets of sampled entities. These sampled files are:</p> <ul> <li>ite_uniform... the edges of redirection graph corresponding to uniform samplings</li> <li>cc_sample_2... the sampling regarding connected components of size 2, 3-10, 10+, respectively.</li> </ul> <p>The Python scripts are open source online at:</p> <p>https://github.com/shuaiwangvu/redirection</p> <p>The paper is attached. In case of any questions, please contact Shuai Wang at shuai.wang@vu.nl.</p>
Study: Layout of Causal Graphs
<p>This repository contains the material and obtained data of an eye tracking study on the topic "Layout of Causal Graphs".</p> <p>For more information, please feel free to contact <lisa.grabinger@oth-regensburg.de>.</p>
DrugProt Silver Standard Knowledge Graph
<p><strong>DrugProt Silver Standard Knowledge Graph</strong></p><p> </p><p> </p><p><strong>Please cite if you use any DrugProt resource:</strong></p><p>Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, <i>Database</i>, Volume 2023, 2023, baad080</p><blockquote><p><i>@article{miranda2023overview, title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations}, author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, journal={Database}, volume={2023}, pages={baad080}, year={2023}, publisher={Oxford University Press UK} }</i></p></blockquote><p>Miranda, Antonio, et al. "Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations." <i>Proceedings of the seventh BioCreative challenge evaluation workshop</i>. 2021.</p><blockquote><p><i>@inproceedings{miranda2021overview, title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations}, author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin}, booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop}, year={2021} }</i></p></blockquote><p><strong>Description</strong></p><p> </p><p> </p><p><strong>Files:</strong></p><ul><li>drugprot-silver-standard-kg.zip : JSON files with the relations predicted by the DrugProt systems and their precision</li><li>large_scale_network_abstracts.tsv : PubMed abstracts</li><li>large_scale_network_entities.tsv : CHEMICAL/drug and GENE/protein entities predicted by DrugProt NER Taggers</li><li>large_scale_network_pmids.txt : list of PMIDs</li></ul><p> </p><p><strong>Related resources:</strong></p><ul><li><a href="https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-1/">Web</a></li><li><a href="https://doi.org/10.5281/zenodo.4955410">DrugProt corpus</a></li><li><a href="https://github.com/tonifuc3m/drugprot-evaluation-library">Evaluation library</a></li><li><a href="https://codalab.lisn.upsaclay.fr/competitions/8293">Online evaluation (CodaLab)</a></li><li><a href="https://doi.org/10.5281/zenodo.4957137">Relation annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.4957576">Gene and protein annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.4957518">Chemicals and drugs annotation guidelines</a></li><li><a href="https://doi.org/10.5281/zenodo.5042178">FAQ</a></li><li><a href="https://doi.org/10.5281/zenodo.5119878">DrugProt Large Scale Additional SubTrack</a></li><li><a href="https://doi.org/10.5281/zenodo.5656991">DrugProt Large Scale document collection protocol</a></li></ul>
Internet AS Router Graphs
<p>Internet graph dataset that contains AS-level edges associated with major border router manufactures. </p>
Figure 1: The ECG model-MAPPING BETWEEN SEMANTIC GRAPHS AND SENTENCES IN GRAMMAR INDUCTION SYSTEM
<p>The following Figure 1 shows a sample semantic graph that describes a<br> simple test world.<br> During the processing of the ECG, the base units of the graph are the ECG<br> atoms. An ECG atom corresponds to a primitive statements related to one<br> predicate. It has a structure of one-level deep tree, where the root of the tree<br> is the predicate and the concepts linked to it are the leaves. The child concept<br> of the root predicate may be not only a single concept but it can be another<br> ECG atom.</p>
Figure 3: AntCo2 algorithm for graph clustering: on the left the output of the computation on a communication network; on the right the output on a regular grid
<p>Social and human developments are typical complex systems. Urban development<br> and dynamics are the perfect illustration of systems where spatial<br> emergence, self-organization and structural interaction between the system<br> and its components occur [3, 4, 5, 6]. In figure 4, we concentrate on the emergence<br> of organizational systems from geographical systems.</p>
Рис.5. СоотношениеразΛичныхгнезΑовыхтиповопорв 2018–2019 гг. поаΑминистративным районам (Λевый график) и в цеΛом по Амурской обΛасти (правый график) Fig. 5. The ratio of different types of nesting supports in 2018–2019 by administrative districts (right graph) and in general in the Amur region (left graph) in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис.5. СоотношениеразΛичныхгнезΑовыхтиповопорв 2018–2019 гг. поаΑминистративным районам (Λевый график) и в цеΛом по Амурской обΛасти (правый график) Fig. 5. The ratio of different types of nesting supports in 2018–2019 by administrative districts (right graph) and in general in the Amur region (left graph)
Gajderowicz, B., Fisher, A., Mago, V.: (preperation) "Graph pruning for identifying COVID-19 misinformation dissemination patterns and indicators on Twitter/X"
<p>This dataset is for the repository <a href="https://github.com/bgajdero/social-graph-analysis-2024">https://github.com/bgajdero/social-graph-analysis-2024</a>.</p>
Dataset and Model Weights for Plasma Sheet Model Graph Network Simulator
<p>This repository contains the simulation data and pre-trained Graph Neural Network (GNN) models produced in [1].</p> <p>Two *.zip files are provided:</p> <ul> <li>data.zip - contains the datasets of train/test simulations produced using the Sheet Model algorithm [1, 2]</li> <li>models.zip - contains the GNN model weights (<em>*.</em>pkl<em>) </em>+ relevant training information and model parameters <em>(*.</em>yml<em> and *</em>.txt)</li> </ul> <p>Dataset subfolders are named according to dataset/{'train' or 'test'}/{number of sheets}/{boundary condition}/. Each subfolder contains multiple simulations and a single info.yml file with relevant information regarding the overall setup. For each i-th simulation the following files are provided:</p> <ul> <li>x_{i}.npy - array with sheet trajectories (#time-steps, #sheets)</li> <li>v_{i}.npy - array with sheet velocities (#time-steps, #sheets)</li> <li>x_eq_{i}.npy - array with sheet equilibrium positions (#time-steps, #sheets)</li> </ul> <p> Model sub-folders are named according to :</p> <ul> <li>models/{time step}/{seed} - default architecture (preferred)</li> <li>models/{time step}/{'collisions', 'nosent' or 'equivariant'}/{seed} - alternative (less performing) architectures mentioned in the paper appendices.</li> </ul> <p>For each model we provide:</p> <ul> <li>params_best.pkl - model weights that performed the best during training on the validation set</li> <li>params_final.pkl - model weights at the end of training</li> <li>model_cfg.yml - GNN architecture metadata</li> <li>train_cfg.yml - training configuration metadata</li> <li>train_data.yml - training dataset metadata</li> <li>loss.txt - training and validation loss per epoch</li> <li>loss_i.txt - training loss per gradient update step</li> </ul> <h3>Source Code</h3> <p>The source code used to produce the data, train, and test the models can be found at: <a href="https://github.com/diogodcarvalho/gns-sheet-model">https://github.com/diogodcarvalho/gns-sheet-model</a></p> <h3>References</h3> <p>[1] D. D. Carvalho, D. R. Ferreira, L. O. Silva, "Learning the dynamics of a one-dimensional plasma model with graph neural networks<em>", Mach. Learn.: Sci. Technol. 5 025048 </em>(2024)</p> <p>[2] J. Dawson, "One‐Dimensional Plasma Model"<em>, The Physics of Fluids</em> 5.4 (1962): 445-459.</p> <p> </p>
Conformer datasets for "Equivariant Graph Neural Networks for Toxicity Prediction"
<p>Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain.</p> <p>PAPER</p> <p>https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.3c00032</p> <p>CODE and MODELS:</p> <p>The conformer data sets and trained toxicity models will be published upon acceptance of this work. The code has been made available at <a href="https://github.com/jule-c/ET-Tox">https://github.com/jule-c/ET-Tox</a>, and the processed data as well as pretrained models for training and testing can be downloaded from <a href="../record/7942946">https://zenodo.org/record/7942946</a>. We can provide the full list of conformers as XYZ files upon request.</p>
Helmholtz Knowledge Graph: RDF data dump
<p>Under this base DOI we regularly publish full RDF data dumps of the Helmholtz-Knowledge Graph.<br>Data dumps are typical associated with major releases, or major data updates.</p> <p>Dumps are serialized in .ttl and compressed with gzip.<br><br>For more information on deployment and documentation as well as data access see:<br>Search UI: https://search.unhide.helmholtz-metadaten.de/<br>SPARQL endpoint: https://sparql.unhide.helmholtz-metadaten.de/<br>Documentation: https://docs.unhide.helmholtz-metadaten.de/<br>Software: https://codebase.helmholtz.cloud/hmc/hmc-public/unhide</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.