Find research datasets worth reusing

Dataset on Alternaria disease on rocket under simulated climate change conditions

This dataset is related to disease severity caused by the Alternaria spp. isolates tested in different temperature and CO2 combinations on cultivated rocket and published in https://doi.org/10.3920/WMJ2016.2108 (Figure 1) and in https://doi.org/10.1007/s42161-018-0125-8,

Natural Killer cells demonstrate distinct eQTL and transcriptome-wide disease associations, highlighting their role in autoimmunity.

Abstract  Natural Killer (NK) cells are innate lymphocytes with central roles in immunosurveillance and are implicated in autoimmune pathogenesis. The degree to which regulatory variants affect NK gene expression is poorly understood. We performed expression quantitative trait locus (eQTL) mapping of negatively selected NK cells from a population of healthy Europeans (n=245). We find a significant subset of genes demonstrate eQTL specific to NK cells and these are highly informative of human disease, in particular autoimmunity. An NK cell transcriptome-wide association study (TWAS) across five common autoimmune diseases identified further novel associations at 27 genes. In addition to these cis observations, we find novel master-regulatory regions impacting expression of trans gene networks at regions including 19q13.4, the Killer cell Immunoglobulin-like Receptor (KIR) Region, GNLY, MC1R and UVSSA. Our findings provide new insights into the unique biology of NK cells, demonstrating markedly different eQTL from other immune cells, with implications for disease mechanisms. Preprint https://www.biorxiv.org/content/10.1101/2021.05.10.443088v1 Dataset nk_raw_for_zenodo.txt: Matrix of raw gene expression at 47,209 probes in primary human NK cells from 245 healthy individuals of European ancestry. Gene expression is quantified using the Illumina HumanHT-12 v4 BeadChip gene expression array platform. Column names represent Array Address ID for each probe, and row names represent pseudonymised sample identifiers, which can be matched to sample genotypes. Sample genotypes are available at the European Genome-Phenome Archive with accession ID EGAS00000000109). probes_passing_QC.txt: List of probes passing quality control; probe sequences mapping to a unique genomic locus, and probe sequences not containing common genomic variation (minor allele frequency >1%), n=29,002. Column names are Array Address ID (probeID), Ensembl ID (ensembl), Gene ID (gene), and Illumina probe ID (ilmn).

Supplementary Material: Climate-sensitive disease outbreaks in the aftermath of extreme climatic events: a scoping review

Supplemental experimental procedures General Information Here we provide the data extraction of the studies retrieved for the scoping review "Climate-sensitive disease outbreaks in the aftermath of extreme climatic events" following PRISMA-ScR guidelines. Data were extracted for the following variables: title, first author, year of publication, country/region studied, extreme climate event, extreme climate event name (tropical cyclones are often named e.g. Typhoon Haiyan), Index used to measure climate anomaly, extreme climate event definition, text description of extreme climate event, disease, outbreak definition, time period of the study, data source, baseline/reference period, study design, statistics, outcome, outcome quantification, outbreak risk, qualitative description of extreme climate event and outbreak risk, time lag.  Outcome was defined as either disease cases or incidence.

openother-openMar 2022View details →

Global predictions for the risk of establishment of Pierce's disease of grapevines

Example to compute the risk of Pierce's Disease establishment from ERA5-Land temperature data. Related to the publication <a href="https://www.nature.com/articles/s42003-022-04358-w" target="_blank" rel="noopener">Global predictions for the risk of establishment of Pierce’s disease of grapevines</a> See <a href="https://github.com/agimenezromero/PierceDisease-GlobalRisk-Predictions" target="_blank" rel="noopener">GitHub</a> repo.

opencc-by-4.0Apr 2022View details →

Development of a machine learning model to predict non- durable response to anti-TNF therapy in Crohn's disease using transcriptome imputed from genotypes

This is the expression value predicted using PrediXcan version 7 to find a gene feature that can distinguish between patients with and without effect on infliximab. Among the various tissue models provided by PrediXcan v7, three models were selected and used: whole blood, Colon transverse, and terminal ileum of small intestine, and the predicted gene counts of each model were 6,294, 5,612 and 3,107. For each of the three models, predicted gene expression values and phenotype information per sample were submitted.

opencc-by-4.0Jan 2022View details →

Structural basis for Parkinson's Disease-linked LRRK2's binding to microtubules

This dataset includes all of the tabular data used in the figures in the article. Original article is available at: https://doi.org/10.1101/2022.01.21.477284 Leucine Rich Repeat Kinase 2 (LRRK2) is one of the most commonly mutated genes in familial Parkinson’s Disease (PD). Under some circumstances, LRRK2 co-localizes with microtubules in cells, an association enhanced by PD mutations. We report a cryo-electron microscopy structure of the catalytic half of LRRK2, containing its kinase, which is in a closed conformation, and GTPase domains, bound to microtubules. We also report a structure of the catalytic half of LRRK1, which is closely related to LRRK2, but is not linked to PD. LRRK1’s structure is similar to LRRK2, but LRRK1 does not interact with microtubules. Guided by these structures, we identify amino acids in LRRK2’s GTPase domain that mediate microtubule binding; mutating them disrupts microtubule binding in vitro and in cells, without affecting LRRK2’s kinase activity. Our results have implications for the design of therapeutic LRRK2 kinase inhibitors.

opencc-by-4.0Jul 2022View details →

Systematic analysis of disease-linked rare germline variants reveals new classes of cancer predisposing genes

<ul> <li>GEMs_Liver-HCC: 312 cancer patient-specific genome-scale metabolic models (GEMs) for Liver-HCC reconstructed using the RNA-seq data from PCAWG-TCGA Liver-HCC samples and generic human GEM 'Recon 2M.2'</li> <li>GEMs_Lung-SCC: 493 cancer patient-specific GEMs for Lung-SCC reconstructed using the RNA-Seq data from PCAWG-TCGA Lung-SCC samples and generic human GEM 'Recon 2M.2'</li> </ul> All the patient-specific GEMs were generated using a previously developed method (i.e., tINIT algorithm with a rank-based weight function), which is available at <a href="https://bitbucket.org/kaistmbel/recon-manager">https://bitbucket.org/kaistmbel/recon-manager</a>.

opencc-zeroJul 2022View details →

Can disease resistance evolve independently at different ages? Genetic variation in age-dependent resistance to disease in three wild plant species

1. Juveniles are typically less resistant (more susceptible) to infectious disease than adults, and this difference in susceptibility can help fuel the spread of pathogens in age-structured populations. However evolutionary explanations for this variation in resistance across age remain to be tested. 2. One hypothesis is that natural selection has optimized resistance to peak at ages where disease exposure is greatest. A central assumption of this hypothesis is that hosts have the capacity to evolve resistance independently at different ages. This would mean that hosts populations have a) standing genetic variation in resistance at both juvenile and adult stages, and b) that this variation is not strongly correlated between age-classes so that selection acting at one age does not produce a correlated response at the other age 3. Here we evaluated the capacity of three wild plant species (Silene latifolia, S. vulgaris, and Dianthus pavonius) to evolve resistance to their anther-smut pathogens (Microbotryum fungi), independently at different ages. The pathogen is pollinator-transmitted, and thus exposure risk is considered to be highest at the adult flowering stage. 4. Within each species we grew families to different ages, inoculated individuals with anther smut, and evaluated the effects of age, family and their interaction on infection. 5. In two of the plant species, S. latifolia and D. pavonius, resistance to smut at the juvenile stage was not correlated with resistance to smut at the adult stage. In all three species, we show there are significant age*family interaction effects, indicating that age-specificity of resistance varies among the plant families. 6. Synthesis: These results indicate that different mechanisms likely underlie resistance at juvenile and adult stages and support the hypothesis that resistance can evolve independently in response to differing selection pressures as hosts age. Taken together our results provide new insight into the structure of genetic variation in age-dependent resistance in three well-studied wild host-pathogen systems.

SocialDisNER corpus: gold standard annotations for detection of disease mentions in Spanish tweets

If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo:  Luis Gasco Sánchez, Darryl Estrada Zavala, Eulàlia Farré-Maduell, Salvador Lima-López, Antonio Miranda-Escalada, and Martin Krallinger. 2022. <a href="https://aclanthology.org/2022.smm4h-1.48">The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora</a>. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 182–189, Gyeongju, Republic of Korea. Association for Computational Linguistics. <pre><code class="language-json">@inproceedings{gasco2022socialdisner, title = "The {S}ocial{D}is{NER} shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora", author = "Gasco S{\'a}nchez, Luis and Estrada Zavala, Darryl and Farr{\'e}-Maduell, Eul{\`a}lia and Lima-L{\'o}pez, Salvador and Miranda-Escalada, Antonio and Krallinger, Martin", booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.smm4h-1.48", pages = "182--189" }</code></pre>   Introduction: The SocialDisNER corpus of the SMM4H 2022 – Task 10 task focus on the recognition of disease mentions in tweets written in Spanish after selecting primarily first-hand experience of diseases and other health-relevant content (from patient associations, professional healthcare institutions, and through followers of patient association accounts of a diversity of pathologies including rare diseases, mental health, cancer, etc..). SocialDisNER Gold Standard The Gold Standard corpus was manually annotated by medical experts following the <a href="https://doi.org/10.5281/zenodo.6983041">SMM4H-SocialDisNER guidelines</a>. These guidelines were adapted from previous efforts used to annotate patient clinical records and medical literature. It covers rules for annotating mentions of diseases in health-related tweets in Spanish, The training set consists of 5000 tweets written in Spanish and the validation set consists of 2500 tweets written in Spanish. Both sets have been manually annotated by healthcare professionals. The test dataset contains 23430 tweets, although only 2000 will be used to evaluate the systems participating in the task (the rest is background set). We don't plan to publish the test set, but if you want you can test your system from <a href="https://codalab.lisn.upsaclay.fr/competitions/3531">SocialDisNER Codalab</a>. SocialDisNER Large Scale Corpus The large-scale data contains mentions automatically extracted from a set of 85000 tweets. Separate datasets are shown for each entity including diseases, drugs, symptoms, professions, procedures, species, morphology neoplasm, and persons. SocialDisNER co-mention networks We have computed a co-occurrence matrix of the extracted diseases, as well as several co-mention matrices between the disease mentions and the rest of the entities in the large-scale corpora.   File structure: The structure of the corpus is:  <ul> <li>SocialDisNER_Data: <ul> <li>training-validation-data folder <ul> <li>train-valid-txt-files:  folder with training and validation text files. One text file per tweet, the file name corresponds to the tweet id. One sub-directory per corpus split (train and valid). The files named ids_dev_set.txt and ids_train_set.txt contain the list of file identifiers for each of the data splits (validation and train).</li> <li>mentions.tsv: This file contains the manually annotated disease mentions. The file has the following fields: <ul> <li>tweets_id: This is the id of the tweet, using Twitter API you can query the content of the tweet.</li> <li>Begin: This is the position in the tweet where the annotation was found.</li> <li>End: This is the position of the last character of the annotation in the tweet.</li> <li>Type: This is the type of entity found, in our case "ENFERMEDAD".</li> <li>Extraction: This is the literal extraction, in other words, the fragment of text which refers to the annotation. </li> </ul> </li> </ul> </li> <li>test-data folder: <ul> <li>test-data-txt-files: folder with test text files. One file per tweet, the file name corresponds to the tweet id. The folder contains 23430 tweets to be used as test set of the task. Of them, 2000 will be used to evaluate the participating systems.</li> </ul> </li> </ul> </li> </ul>   <ul> <li>SocialDisNER_LargeScale_additionaldata: <ul> <li>socialdisner_diseases: <ul> <li>tweets_txt: Folder with large-scale tweet database. One text file per tweet, the file name corresponds to the tweet id.</li> <li>diseases_mentions.tsv: This file contains the automatically annotated disease mentions from the large-scale SocialDisNER corpus (Silver Standard). The structure is the same than the Golden Standard annotations.</li> </ul> </li> <li>socialdisner_ENTITY: Each folder with this naming convention contains the following data structure. Corpora have been generated with mentions of diseases, drugs, symptoms, professions, procedures, species, morphology neoplasm and persons <ul> <li>tweets_txt: Folder with large-scale tweet database. One text file per tweet, the file name corresponds to the tweet id.</li> <li>ENTITY_mentions.tsv: This file contains the automatically annotated mentions of type “ENTITY” from the large-scale SocialDisNER corpus (Silver Standard). The structure is the same than the Golden Standard annotations.</li> </ul> </li> <li>socialdisner_networks: This folder contains tsv files containing the co-mention matrices between the diseases and the rest of the entities of the large-scale socialdisner data. Each file follows the following naming convention: <ul> <li>socialdisner_disease-ENTITY_net.tsv:  The tsv file contains a series of columns and rows corresponding to the mentions used for building the matrix. Each column is separated by “;”. The type of each mention is identified by the label in parentheses of each title. The count represents the number of times that mention x and mention y were found in the same tweet of the large-scale dataset.</li> <li>socialdiser_disease_net.tsv: This tsv file contains the array of socialdisner-disease large-scale corpus co-mentions separated by ";". This file can be loaded into NetworkX to perform disease co-morbidity analysis on the socialdisner-disease large-scale data.</li> </ul> </li> </ul> </li> </ul> Note: In previous versions of the dataset the order of the columns in the mentions.tsv file was not in the correct order. From this version onwards the order is correct and adequate to send the predictions of the task.   For further information, please visit <a href="https://temu.bsc.es/socialdisner/">https://temu.bsc.es/socialdisner/</a> Summary statistics: <table> <caption>Manually annotated data</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">Training set</th> <th scope="col">Development set</th> </tr> </thead> <tbody> <tr> <th scope="row"># tweets</th> <td>5000</td> <td>2500</td> </tr> <tr> <th scope="row"># characters</th> <td>1253431</td> <td>516768</td> </tr> <tr> <th scope="row"># tokens</th> <td>211555</td> <td>84478</td> </tr> <tr> <th scope="row">Avg. char / tweet</th> <td>250.69</td> <td>206.71</td> </tr> <tr> <th scope="row">Avg. tok. / tweet</th> <td>42.31</td> <td>33.79</td> </tr> <tr> <th scope="row"># mentions</th> <td>15173</td> <td>4252</td> </tr> <tr> <th scope="row"># unique mentions</th> <td>4407</td> <td>1413</td> </tr> </tbody> </table>   <table> <caption>Large-scale annotated data (Silver Standard)</caption> <tbody> <tr> <td> </td> <td>Socialdisner-diseases</td> <td>Socialdisner-pharma</td> <td>Socialdisner-morphology_neoplasms</td> <td>Socialdisner-symptoms</td> <td>Socialdisner-professions</td> <td>Socialdisner-Procedures</td> <td>Socialdisnerv-Person</td> <td>Socialdisner-Species</td> </tr> <tr> <td># tweets</td> <td>85077</td> <td>1759</td> <td>8518</td> <td>12624</td> <td>15831</td> <td>11462</td> <td>41033</td> <td>12118</td> </tr> <tr> <td># characters</td> <td>19920670</td> <td>435141</td> <td>2082574</td> <td>3023784</td> <td>4063114</td> <td>2873791</td> <td>10273278</td> <td>2933925</td> </tr> <tr> <td># tokens</td> <td>3236411</td> <td>68269</td> <td>332539</td> <td>521503</td> <td>660071</td> <td>467059</td> <td>1689479</td> <td>486249</td> </tr> <tr> <td>Avg. char / tweet</td> <td>234.15</td> <td>247.38</td> <td>244.49</td> <td>239.53</td> <td>256.66</td> <td>250.72</td> <td>250.37</td> <td>242.11</td> </tr> <tr> <td>Avg. tok. / tweet</td> <td>38.04</td> <td>38.81</td> <td>39.04</td> <td>41.31</td> <td>41.69</td> <td>40.75</td> <td>41.17</td> <td>40.13</td> </tr> <tr> <td># mentions</td> <td>116260</td> <td>1029</td> <td>8943</td> <td>12896</td> <td>18590</td> <td>10080</td> <td>58007</td> <td>14014</td> </tr> <tr> <td># unique mentions</td> <td>16034</td> <td>530</td> <td>541</td> <td>6991</td> <td>3667</td> <td>3841</td> <td>3446</td> <td>1676</td> </tr> </tbody> </table>       Do not share the data with other individuals/teams without permission from the task organizer. Tweets IDs are the primary source of information. Tweet texts are provided as support material. By downloading this resource, you agree to the Twitter <a href="https://twitter.com/en/tos">Terms of Service</a>, <a href="https://twitter.com/en/privacy">Privacy Policy</a>, <a href="https://developer.twitter.com/en/developer-terms/agreement">Developer Agreement</a>, and <a href="https://developer.twitter.com/en/developer-terms/policy">Developer Policy</a>.

opencc-zeroJul 2022View details →

Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study

We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.

opencc-zeroJul 2022View details →

Climatic niche conservatism in a clade of disease vectors (Diptera: Phlebotominae)

Sandflies of the family Psychodidae show notable diversity in both disease vector status and climatic niche. Psychodid species' ranges can be solely tropical, confined to the temperate zones, or span both. We obtained observation site data, and associated climate data, for 223 psychodid species to understand which aspects of climate most closely predict distribution. Temperature and seasonality are strong determinants of species occurrence within the clade. We built a mitochondrial DNA phylogeny of Psychodidae, and found a positive relationship between pairwise genetic distance and climate niche differentiation, which indicates strong niche conservatism. This result is also supported by strong phylogenetic signals of metrics of climate differentiation. Finally, we used ancestral trait reconstruction to infer the tropicality (i.e., proportion of latitudinal range in the tropics minus the proportion of the latitudinal range in temperate areas) of ancestral species, and counted transitions to and from tropicality states, finding that tropical and temperate species respectively produced almost entirely tropical and temperate descendant species, a result consistent for vector and non-vector species. Taken together, these results imply that while vectors of Leishmania can survive in a variety of climates, their climate niches are strongly predicted by phylogeny.

opencc-by-4.0Jul 2022View details →

The efficacy of wildlife fences for keeping reindeer outside a chronic wasting disease risk area

Data to "The efficacy of wildlife fences for keeping reindeer outside a chronic wasting disease risk area" accepted in Ecological Solutions and Evidence

opencc-by-4.0Jul 2022View details →

A hydroponics based high throughput screening system for clubroot disease pathotyping

Clubroot is a devastating disease affecting the canola industry and caused by the protist Plasmodiophora brassicae. Since the 1940s, several pathotyping systems have been developed and different classifications have been used to differentiate P. brassicae isolates based on their ability to infect different hosts. Unfortunately, none of previously developed pathotyping systems discriminate virulent and avirulent isolates of P. brassicae against the clubroot resistance profiles of commercially available canola cultivars. To try to solve this limitation we have developed a hydroponic-based bioassay using P. brassicae single spore isolates (SSIs), and four canola inbreed homozygous lines (CIH). These SSIs are representative of the virulence widely spread in the field, while these CIH are representative of the resistance commercially available to growers. Through this new phenotyping scheme, we have been able to connect P. brassicae isolates with their ability to break down clubroot resistance in canola in shorter time and economically more efficiently. This will help to tailor the selection of resistant canola varieties to use in an infested field for the longest possible time, empowering producers to make informed decisions about the best canola cultivar to use based on the P. brassicae diversity in their field

opencc-by-4.0Aug 2022View details →

STORMS checklist: Dysbiosis of the enteric microbiota due to Crohn's disease treatment associated with MAIT cell activation

STORMS checklist initiative to standardize reporting of human microbiome research, regarding submission to peer-reviewed journal.

opencc-by-4.0Apr 2021View details →

PheKnowLator Human Disease Knowledge Graphs - Build Data (Original)

RELEASE V2.1.0 KNOWLEDGE GRAPH: ORIGINAL DATA SOURCES  Release: <a href="https://github.com/callahantiff/PheKnowLator/wiki/v2.0.0">v2.1.0 </a> 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>. 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. Data Access: <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>   ONTOLOGIES <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>   Cell Ontology (CL) Homepage: <a href="https://github.com/obophenotype/cell-ontology"><code>GitHub</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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: <ul> <li><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></li> <li><a href="http://geneontology.org/"><code>GO</code></a></li> <li><a href="https://github.com/pato-ontology/pato/"><code>PATO</code></a></li> <li><a href="https://proconsortium.org/"><code>PRO</code></a></li> <li><a href="https://github.com/oborel/obo-relations/"><code>RO</code></a></li> <li><a href="https://uberon.github.io/"><code>UBERON</code></a></li> </ul>   Cell Line Ontology (CLO) Homepage: <a href="http://www.clo-ontology.org/"><code>http://www.clo-ontology.org/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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: <ul> <li><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></li> <li><a href="http://disease-ontology.org/"><code>DOID</code></a></li> <li><a href="https://github.com/obophenotype/ncbitaxon"><code>NCBITaxon</code></a></li> <li><a href="https://uberon.github.io/"><code>UBERON</code></a></li> </ul>   Chemical Entities of Biological Interest (ChEBI) Homepage: <a href="https://www.ebi.ac.uk/chebi/"><code>https://www.ebi.ac.uk/chebi/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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>.   Gene Ontology (GO) Homepage: <a href="http://geneontology.org/"><code>http://geneontology.org/</code></a> Citations: <blockquote> 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 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 </blockquote> Usage: 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: <ul> <li><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></li> <li><a href="https://github.com/obophenotype/ncbitaxon"><code>NCBITaxon</code></a></li> <li><a href="https://github.com/oborel/obo-relations/"><code>RO</code></a></li> <li><a href="https://uberon.github.io/"><code>UBERON</code></a></li> </ul> Other Gene Ontology Data Used: <a href="http://geneontology.org/gene-associations/goa_human.gaf.gz"><code>goa_human.gaf.gz</code></a>   Human Phenotype Ontology (HPO) Homepage: <a href="https://hpo.jax.org/"><code>https://hpo.jax.org/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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: <ul> <li><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></li> <li><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></li> <li><a href="http://geneontology.org/"><code>GO</code></a></li> <li><a href="https://uberon.github.io/"><code>UBERON</code></a></li> </ul> Files <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>   Mondo Disease Ontology (Mondo) Homepage: <a href="https://mondo.monarchinitiative.org/"><code>https://mondo.monarchinitiative.org/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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: <ul> <li><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></li> <li><a href="https://www.ncbi.nlm.nih.gov/taxonomy"><code>NCBITaxon</code></a></li> <li><a href="http://geneontology.org/"><code>GO</code></a></li> <li><a href="https://hpo.jax.org/"><code>HPO</code></a></li> <li><a href="https://uberon.github.io/"><code>UBERON</code></a></li> </ul>   Pathway Ontology (PW) Homepage: <a href="https://rgd.mcw.edu/wg/home/pathway2/"><code>rgd.mcw.edu</code></a> Citation: <blockquote> 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. </blockquote> Usage: 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>). Files <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>   Protein Ontology (PRO) Homepage: <a href="https://proconsortium.org/"><code>https://proconsortium.org/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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: <ul> <li><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></li> <li><a href="http://disease-ontology.org/"><code>DOID</code></a></li> <li><a href="http://geneontology.org/"><code>GO</code></a></li> </ul> Notes: 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. Files <ul> <li> Generated Human Version Protein Ontology (PRO) <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> Other PRO Data Used: <a href="https://proconsortium.org/download/current/promapping.txt"><code>promapping.txt</code></a> </li> <li> Generated Mapping Data <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>   Relations Ontology (RO) Homepage: <a href="https://github.com/oborel/obo-relations/"><code>GitHub</code></a> Citation: <blockquote> 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. </blockquote> Usage: 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. Files <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>   Sequence Ontology (SO) Homepage: <a href="https://github.com/The-Sequence-Ontology/SO-Ontologies"><code>GitHub</code></a> Citation: <blockquote> 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 </blockquote> Usage: Utilized to connect <code>transcripts</code> and other genomic material like <code>genes</code> and <code>variants</code>. Files <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>   Uber-Anatomy Ontology (Uberon) Homepage: <a href="https://uberon.github.io/"><code>GitHub</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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: <ul> <li><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></li> <li><a href="http://www.obofoundry.org/ontology/cl.html"><code>CL</code></a></li> <li><a href="http://geneontology.org/"><code>GO</code></a></li> <li><a href="https://proconsortium.org/"><code>PRO</code></a></li> </ul>   Vaccine Ontology (VO) Homepage: <a href="http://www.violinet.org/vaccineontology/"><code>http://www.violinet.org/vaccineontology/</code></a> Citations: <blockquote> 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 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 </blockquote> Usage: Utilized the edges between this ontology and its dependencies: <ul> <li><a href="https://www.ebi.ac.uk/chebi/"><code>ChEBI</code></a></li> <li><a href="http://disease-ontology.org/"><code>DOID</code></a></li> <li><a href="http://geneontology.org/"><code>GO</code></a></li> <li><a href="https://proconsortium.org/"><code>PRO</code></a></li> <li><a href="https://uberon.github.io/"><code>UBERON</code></a></li> </ul>   DATABASE SOURCES <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>   BioPortal Homepage: <a href="https://bioportal.bioontology.org/"><code>BioPortal</code></a> Citation: <blockquote> BioPortal. <a href="https://www.bioontology.org/wiki/LOOM">Lexical OWL Ontology Matcher (LOOM)</a> 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 </blockquote> Usage: 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. ⭐ ALTERNATIVE METHOD⭐ 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: <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> Files <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>   ClinVar Homepage: <a href="https://www.ncbi.nlm.nih.gov/clinvar/"><code>https://www.ncbi.nlm.nih.gov/clinvar/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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: <ul> <li> <code>Assembly</code> = "GRCh38" </li> <li> <code>ClinSigSimple</code> = <code>1</code> <ul> <li> <blockquote> 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)" </blockquote> </li> </ul> </li> <li> <code>ReviewStatus</code> in ["criteria provided, multiple submitters, no conflicts", "reviewed by expert panel", "practice guideline"] </li> </ul> Files <ul> <li> Downloaded Data <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> 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> </li> </ul>   Comparative Toxicogenomics Database (CTD) Homepage: <a href="http://ctdbase.org/"><code>http://ctdbase.org/</code></a> Citations: <blockquote> 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>) 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 </blockquote> 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: <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> Files <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>   DisGeNET Homepage: <a href="https://www.disgenet.org/"><code>https://www.disgenet.org/</code></a> Citation: <blockquote> 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]. 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. </blockquote> Usage: 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: <ul> <li>OMIM, ORPHA, UMLS, ICD ➞ DOID</li> <li>OMIM, ORPHA, UMLS, ICD ➞ HPO</li> </ul> Files <ul> <li> Downloaded Data <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> Generated Mapping Data <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>   Ensembl Homepage: <a href="https://uswest.ensembl.org/index.html"><code>https://uswest.ensembl.org/index.html</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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>): <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> Files <ul> <li> Downloaded Data <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> Generated Mapping Data <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>   GeneMANIA Homepage: <a href="https://genemania.org/"><code>https://genemania.org/</code></a> Citation: <blockquote> 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 </blockquote> Usage: GeneMANIA was utilized to create <code>gene-gene</code> edges. Files <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>   Genotype-Tissue Expression Project (GTEx) Homepage: <a href="https://gtexportal.org/home/"><code>https://gtexportal.org/home/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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. <blockquote> 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). </blockquote> 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). Files <ul> <li> 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> </li> <li> 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> </li> <li> Generated Data 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. <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>   Human Genome Organisation Gene Nomenclature Committee (HUGO) Homepage: <a href="https://www.genenames.org/"><code>https://www.genenames.org/</code></a> Citations: <blockquote> 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> 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 </blockquote> Usage: 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>: <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> Files <ul> <li> 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> </li> <li> Generated Data <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>   Human Protein Atlas (HPA) Homepage: <a href="https://www.proteinatlas.org/"><code>https://www.proteinatlas.org/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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). Files <ul> <li> 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> </li> <li> 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> </li> <li> Generated Data <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>   National Center for Biotechnology Information (NCBI) Entrez Gene Homepage: <a href="https://www.ncbi.nlm.nih.gov/gene/"><code>https://www.ncbi.nlm.nih.gov/gene/</code></a> Citation: <blockquote> 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. </blockquote> Usage: 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>: <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> Files <ul> <li> 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> </li> <li> Generated Data <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>   Reactome Pathway Database Homepage: <a href="https://reactome.org/"><code>https://reactome.org/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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: <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> Files <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>   Search Tool for Recurring Instances of Neighbouring Genes (STRING) Database Homepage: <a href="https://string-db.org/"><code>string-db.org</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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). Files <ul> <li> 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> </li> <li> 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> </li> </ul>   Universal Protein Resource (UniProt) Knowledgebase Homepage: <a href="https://www.uniprot.org/"><code>https://www.uniprot.org/</code></a> Citation: <blockquote> 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 </blockquote> Usage: 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>: <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> Files <ul> <li> Downloaded Data <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> Generated Data <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>   This project is licensed under Apache License 2.0 - see the <a href="https://github.com/callahantiff/PheKnowLator/blob/master/LICENSE"><code>LICENSE.md</code></a> file for details. If you intend to use any of the information on this Wiki, please provide the appropriate attribution by citing this repository: <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>

opencc-zeroAug 2022View details →

Know what you don't know: Embracing state uncertainty in disease-structured multistate models

Hidden Markov models (HMMs) are broadly applicable hierarchical models that derive their utility from separating state processes from observation processes yielding the data. Multistate models such as mark-recapture and dynamic multistate occupancy models are examples of HMMs that are frequently used in ecology. In their early formulations, states, such as pathogen infection status, were assumed to be perfectly observed without ambiguity in state assignment. However, state uncertainty is a pervasive feature of many ecological systems, and multievent models were developed to explicitly account for it. We developed a novel extended multievent mark-recapture model that incorporates state uncertainty at multiple levels of detection. Using a disease-structured example, both false-negative and false-positive state assignment errors are modeled at two levels of state assignment---the pathogen sampling process and the diagnostic process that samples are subjected to. We additionally describe methods to jointly model infection intensity to integrate heterogeneity in ecological parameters, such as survival, and the pathogen detection processes. We provide code to simulate and analyze datasets with various underlying ecological processes and fit our model to a mark-recapture dataset of Mixophyes fleayi (Fleay's barred frog) infected with the amphibian chytrid fungus (Batrachochytrium dendrobatidis, Bd). In our case study, we found evidence for various state assignment errors: the sampling protocol performed poorly in detecting Bd, pathogen detection was highly dependent on infection intensity, and false-positives were non-negligible. Incorporating state uncertainty yielded significantly higher estimates of infection prevalence and 4--5 times lower rates of infection state transitions compared to those obtained from a traditional multistate model. Our results highlight that incorporating state assignment errors improves inference on the ecological state process, especially when sensitivity and specificity of the state assignment processes are low. The general model structure can be applied to other HMMs, providing a foundation for modeling state uncertainty in a range of related models. --

opencc-by-4.0Jan 2022View details →

A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities

This file contains the data that was used in the paper titled "A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities" which will be published in Frontiers of Pharmacology.

opencc-zeroSep 2022View details →

Data from: Habitat quality influences pollinator pathogen prevalence through both habitat–disease and biodiversity–disease pathways

The dilution effect hypothesis posits that increasing biodiversity reduces infectious disease transmission. Here, we propose that habitat quality might modulate this negative biodiversity–disease relationship. Habitat may influence pathogen prevalence directly by affecting host traits like nutrition and immune response (we coined this as the 'habitat–disease relationship') or indirectly by changing host biodiversity (biodiversity–disease relationship). We used a path model to test the relative strength of links between habitat, biodiversity, and pathogen prevalence in a pollinator–virus system. High-quality habitat metrics were directly associated with viral prevalence, providing evidence for a habitat–disease relationship. However, the strength and direction of specific habitat effects on viral prevalence varied based on the characteristics of the habitat, host, and pathogen. In general, more natural area and richness of landcover types were directly associated with increased viral prevalence, while greater floral density was associated with reduced viral prevalence. More natural habitat was also indirectly associated with reduced prevalence of two key viruses (black queen cell virus and deformed wing virus) via increased pollinator species richness, providing evidence for a habitat-mediated dilution effect on viral prevalence. Biodiversity–disease relationships varied across viruses, as prevalence of sacbrood virus was not associated with any habitat quality or pollinator community metrics. Across all viruses and hosts, habitat–disease and biodiversity–disease paths had effects of similar magnitude on viral prevalence. Therefore, habitat quality is a key driver of variation in pathogen prevalence among communities via both direct habitat–disease and indirect biodiversity–disease pathways, though the specific patterns varied among different viruses and host species. Critically, habitat–disease relationships could either contribute to or obscure dilution effects in natural systems depending on the relative strength and direction of the habitat–disease and biodiversity–disease pathways in that host–pathogen system. Therefore, habitat may be an important driver in the complex interactions between hosts and pathogens.

opencc-by-4.0Oct 2022View details →

Figure S1. Mediation analysis on the effect of insulin resistance on intraocular pressure. Figure S2. Forest plot showing the OR (95% CI) for EIOP of ALD versus NAFLD and the OR (95% CI) for EIOP of drinkers versus non-drinkers. Abbreviations: OR, odds ratio; CI, confidence interval; ALD, alcoholic liver disease; NAFLD, non-alcoholic fatty liver disease.

Figure S1. Mediation analysis on the effect of insulin resistance on intraocular pressure. Figure S2. Forest plot showing the OR (95% CI) for EIOP of ALD versus NAFLD and the OR (95% CI) for EIOP of drinkers versus non-drinkers. Abbreviations: OR, odds ratio; CI, confidence interval; ALD, alcoholic liver disease; NAFLD, non-alcoholic fatty liver disease.