PheKnowLator Human Disease Knowledge Graph Builds
Published May 1, 2021 | Version v2.1.0_01MAY2021
Dataset Open

PheKnowLator Human Disease Knowledge Graphs - Build Data (Processed)

Authors/Creators

  • 1. University of Colorado Anschutz Medical Campus

Description

RELEASE V2.1.0 KNOWLEDGE GRAPH: PROCESSED DATA SOURCES 

Release: v2.1.0 

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 Comparative Toxicogenomics Database and experimental data from the Human Protein Atlas.

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 (https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources) for additional details on each data source as well as citation information.

Data Access:

 

ONTOLOGIES

  • Cell Ontology
  • Cell Line Ontology
  • Chemical Entities of Biological Interest (ChEBI) Ontology
  • Gene Ontology
  • Human Phenotype Ontology
  • Mondo Disease Ontology
  • Pathway Ontology
  • Protein Ontology
  • Relations Ontology
  • Sequence Ontology
  • Uber-Anatomy Ontology
  • Vaccine Ontology

 

Cell Ontology (CL)

Homepage: GitHub
Citation:

Bard J, Rhee SY, Ashburner M. An ontology for cell types. Genome Biology. 2005;6(2):R21

Usage: Utilized to connect transcripts and proteins to cells. Additionally, the edges between this ontology and its dependencies are utilized:

 

Cell Line Ontology (CLO)

Homepage: http://www.clo-ontology.org/
Citation:

Sarntivijai S, Lin Y, Xiang Z, Meehan TF, Diehl AD, Vempati UD, Schürer SC, Pang C, Malone J, Parkinson H, Liu Y. CLO: the cell line ontology. Journal of Biomedical Semantics. 2014;5(1):37

Usage: Utilized this ontology to map cell lines to transcripts and proteins. Additionally, the edges between this ontology and its dependencies are utilized:

 

Chemical Entities of Biological Interest (ChEBI)

Homepage: https://www.ebi.ac.uk/chebi/
Citation:

Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, Turner S, Swainston N, Mendes P, Steinbeck C. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Research. 2015;44(D1):D1214-9

Usage: Utilized to connect chemicals to complexes, diseases, genes, GO biological processes, GO cellular components, GO molecular functions, pathways, phenotypes, reactions, and transcripts.

 

Gene Ontology (GO)

Homepage: http://geneontology.org/
Citations:

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA. Gene ontology: tool for the unification of biology. Nature Genetics. 2000;25(1):25

The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Research. 2018;47(D1):D330-8

Usage: Utilized to connect biological processes, cellular components, and molecular functions to chemicals, pathways, and proteins. Additionally, the edges between this ontology and its dependencies are utilized:

Other Gene Ontology Data Used: goa_human.gaf.gz

 

Human Phenotype Ontology (HPO)

Homepage: https://hpo.jax.org/
Citation:

Köhler S, Carmody L, Vasilevsky N, Jacobsen JO, Danis D, Gourdine JP, Gargano M, Harris NL, Matentzoglu N, McMurry JA, Osumi-Sutherland D. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research. 2018;47(D1):D1018-27

Usage: Utilized to connect phenotypes to chemicals, diseases, genes, and variants. Additionally, the edges between this ontology and its dependencies are utilized:

Files

 

Mondo Disease Ontology (Mondo)

Homepage: https://mondo.monarchinitiative.org/
Citation:

Mungall CJ, McMurry JA, Köhler S, Balhoff JP, Borromeo C, Brush M, Carbon S, Conlin T, Dunn N, Engelstad M, Foster E. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Research. 2017;45(D1):D712-22

Usage: Utilized to connect diseases to chemicals, phenotypes, genes, and variants. Additionally, the edges between this ontology and its dependencies are utilized:

 

Pathway Ontology (PW)

Homepage: rgd.mcw.edu
Citation:

Petri V, Jayaraman P, Tutaj M, Hayman GT, Smith JR, De Pons J, Laulederkind SJ, Lowry TF, Nigam R, Wang SJ, Shimoyama M. The pathway ontology–updates and applications. Journal of Biomedical Semantics. 2014;5(1):7.

Usage: Utilized to connect pathways to GO biological processes, GO cellular components, GO molecular functions, Reactome pathways. Several steps are taken in order to connect Pathway Ontology identifiers to Reactome pathways and GO biological processes. To connect Pathway Ontology identifiers to Reactome pathways, we use ComPath Pathway Database Mappings developed by Daniel Domingo-Fernández (PMID:30564458).

Files

 

Protein Ontology (PRO)

Homepage: https://proconsortium.org/
Citation:

Natale DA, Arighi CN, Barker WC, Blake JA, Bult CJ, Caudy M, Drabkin HJ, D’Eustachio P, Evsikov AV, Huang H, Nchoutmboube J. The Protein Ontology: a structured representation of protein forms and complexes. Nucleic Acids Research. 2010;39(suppl_1):D539-45

Usage: Utilized to connect proteins to chemicals, genes, anatomy, catalysts, cell lines, cofactors, complexes, GO biological processes, GO cellular components, GO molecular functions, pathways, proteins, reactions, and transcripts. Additionally, the edges between this ontology and its dependencies are utilized:

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 Data_Preparation.ipynb Jupyter Notebook.

Files

 

Relations Ontology (RO)

Homepage: GitHub
Citation:

Smith B, Ceusters W, Klagges B, Köhler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector AL, Rosse C. Relations in biomedical ontologies. Genome Biology. 2005;6(5):R46.

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

 

Sequence Ontology (SO)

Homepage: GitHub
Citation:

Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, Ashburner M. The Sequence Ontology: a tool for the unification of genome annotations. Genome Biology. 2005;6(5):R44

Usage: Utilized to connect transcripts and other genomic material like genes and variants.

Files

 

Uber-Anatomy Ontology (Uberon)

Homepage: GitHub
Citation:

Mungall CJ, Torniai C, Gkoutos GV, Lewis SE, Haendel MA. Uberon, an integrative multi-species anatomy ontology. Genome Biology. 2012;13(1):R5

Usage: Utilized to connect tissues, fluids, and cells to proteins and transcripts. Additionally, the edges between this ontology and its dependencies are utilized:

 

Vaccine Ontology (VO)

Homepage: http://www.violinet.org/vaccineontology/
Citations:

He Y, Racz R, Sayers S, Lin Y, Todd T, Hur J, Li X, Patel M, Zhao B, Chung M, Ostrow J. Updates on the web-based VIOLIN vaccine database and analysis system. 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. VIOLIN: vaccine investigation and online information network. Nucleic Acids Research. 2007;36(suppl_1):D923-8

Usage: Utilized the edges between this ontology and its dependencies:

 

DATABASE SOURCES

  • BioPortal
  • ClinVar
  • Comparative Toxicogenomics Database
  • DisGeNET
  • Ensembl
  • GeneMANIA
  • Genotype-Tissue Expression Project
  • Human Genome Organisation Gene Nomenclature Committee
  • Human Protein Atlas
  • National Center for Biotechnology Information Gene
  • Reactome Pathway Database
  • Search Tool for Recurring Instances of Neighbouring Genes Database
  • Universal Protein Resource Knowledgebase

 

BioPortal

Homepage: BioPortal
Citation:

BioPortal. Lexical OWL Ontology Matcher (LOOM)

Ghazvinian A, Noy NF, Musen MA. Creating mappings for ontologies in biomedicine: simple methods work. In AMIA Annual Symposium Proceedings 2009 (Vol. 2009, p. 198). American Medical Informatics Association

Usage: BioPortal was utilized to obtain mappings between MeSH identifiers and ChEBI identifiers for chemicals-diseases, chemicals-genes, chemical-GO biological processes, chemicals-GO cellular components, chemicals-GO molecular functions, chemicals-phenotypes, chemicals-proteins, and chemicals-transcripts. Additional information on how this data was processed can be obtained from the NCBO_rest_api.py 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 mesh2021.nt data file directly from MeSH and the Flat_file_tab_delimited/names.tsv.gz file directly from ChEBI. Using these files, we have recapitulated the LOOM algorithm implemented by BioPortal when creating mappings between these resources. The procedure is relatively straightforward and utilizes the following information from each resource:

  • For all MeSH SCR Chemicals, obtain the following information:
    • Identifiers: MeSH identifiers
    • Labels: string labels using the RDFS:label object property
    • Synonyms: track down all synonyms using the vocab:concept and vocab:preferredConcept object properties
  • For all ChEBI classes, obtain the following information:
    • Labels: string labels using the RDFS:label object property
    • Synonyms: track down all synonyms using all synonym object properties

Files

 

ClinVar

Homepage: https://www.ncbi.nlm.nih.gov/clinvar/
Citation:

Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research. 2017;46(D1):D1062-7

Usage: ClinVar was utilized to create variant-gene, variant-disease, and variant-phenotype edges. The original data is filtered such that only records meeting the following criteria were included:

  • Assembly = "GRCh38"

  • ClinSigSimple = 1

    • 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)"

  • ReviewStatus in ["criteria provided, multiple submitters, no conflicts", "reviewed by expert panel", "practice guideline"]

Files

 

Comparative Toxicogenomics Database (CTD)

Homepage: http://ctdbase.org/
Citations:

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: http://ctdbase.org/)

Davis AP, Grondin CJ, Johnson RJ, Sciaky D, McMorran R, Wiegers J, Wiegers TC, Mattingly CJ. The comparative toxicogenomics database: update 2019. Nucleic Acids Research. 2018;47(D1):D948-54

Usage: Comparative Toxicogenomics Database (CTD) was utilized to create chemical-disease, chemical-gene, chemical-GO biological process, chemical-GO cellular components, chemical-GO molecular functions, chemical-phenotype, chemical-protein, chemical-rna, and gene-pathway edges. The original data is filtered such that only records meeting the following criteria were included:

  • chemical-disease: DirectEvidence != ""
  • chemical-gene: Organism == "Homo sapiens", GeneForms == "gene", and affects not in InteractionActions
  • chemical-GO biological process: PhenotypeName == "Biological Process" and Interaction <= "1.04e-47" (10th percentile)
  • chemical-GO cellular components: PhenotypeName == "Cellular Component" and Interaction <= "1.04e-47" (10th percentile)
  • chemical-GO molecular functions: PhenotypeName == "Molecular Function" and Interaction <= "1.04e-47" (10th percentile)
  • chemical-phenotype: DirectEvidence != ""
  • chemical-protein: Organism == "Homo sapiens", GeneForms == "protein", and affects not in InteractionActions
  • chemical-rna: Organism == "Homo sapiens", GeneForms == "mRNA", and affects and activity not in InteractionActions
  • gene-pathway edges: PathwayName == R-HSA-

Files

 

DisGeNET

Homepage: https://www.disgenet.org/
Citation:

Gene-disease association data retrieved from DisGeNET v6.0 (http://www.disgenet.org/), 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. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Research. 2019.

Usage: DisGeNET was utilized to create gene-disease, and gene-phenotype edges. The original data is filtered such that only records meeting the following criteria were included: EI >= "1.0" (90th percentile). Additionally, data from this source was used to create mappings between different types of disease and phenotype identifiers, including:

  • OMIM, ORPHA, UMLS, ICD ➞ DOID
  • OMIM, ORPHA, UMLS, ICD ➞ HPO

Files

 

Ensembl

Homepage: https://uswest.ensembl.org/index.html
Citation:

Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, Billis K, Cummins C, Gall A, Girón CG, Gil L. Ensembl 2018. Nucleic Acids Research. 2017;46(D1):D754-61

Usage: Ensembl data was utilized to create mappings between Ensembl genes, transcripts, and proteins with NCBI Gene identifiers, HUGO gene symbols, UniProt Accession identifiers, and Protein Ontology identifiers in the knowledge graph (for additional details on the processing of these data, see Data_Preparation.ipynb):

  • Ensembl Transcript IDs ➞ PRO IDs
  • Gene Ensembl IDs ➞ Entrez Gene IDs
  • Gene Ensembl IDs ➞ PRO IDs
  • Gene Symbols ➞ Transcript Ensembl IDs
  • Entrez Gene IDs ➞ Transcript Ensembl IDs
  • Entrez Gene IDs ➞ PRO IDs
  • Protein Ensembl IDs ➞ UniProt Protein Accession
  • STRING IDs ➞ PRO IDs
  • UniProt Protein Accession ➞ Entrez Gene IDs

Files

 

GeneMANIA

Homepage: https://genemania.org/
Citation:

Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Research. 2010;38(suppl_2):W214-20

Usage: GeneMANIA was utilized to create gene-gene edges.

Files

 

Genotype-Tissue Expression Project (GTEx)

Homepage: https://gtexportal.org/home/
Citation:

Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, Hasz R, Walters G, Garcia F, Young N, Foster B. The genotype-tissue expression (GTEx) project. Nature Genetics. 2013;45(6):580

Usage: The Genotype-Tissue Expression (GTEx) Project was utilized to create edges between protein-cell, protein-anatomy, rna-cell and rna-anatomy entities. The original data were filtered such that only those edges where the median TPM was >=1.0 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.

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).

The file contains 54 unique tissue and/or cell types. GTEx provides mappings from tissue types to UBERON and EFO. These provided mappings 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 56 mappings (1.04 mappings/concepts).

Files

 

Human Genome Organisation Gene Nomenclature Committee (HUGO)

Homepage: https://www.genenames.org/
Citations:

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 www.genenames.org

Yates B, Braschi B, Gray K, Seal R, Tweedie S, Bruford E. Genenames.org: the HGNC and VGNC Resources in 2017. Nucleic Acids Research. 2017;45(D1):D619-625

Usage: The Human Genome Organisation (HUGO) data was utilized to obtain mappings between NCBI Gene identifiers, HUGO gene symbols, UniProt Accession identifiers, and Protein Ontology identifiers. For additional details on the processing of these data, see Data_Preparation.ipynb:

  • Ensembl Transcript IDs ➞ PRO IDs
  • Gene Ensembl IDs ➞ Entrez Gene IDs
  • Gene Ensembl IDs ➞ PRO IDs
  • Gene Symbols ➞ Transcript Ensembl IDs
  • Entrez Gene IDs ➞ Transcript Ensembl IDs
  • Entrez Gene IDs ➞ PRO IDs
  • Protein Ensembl IDs ➞ UniProt Protein Accession
  • STRING IDs ➞ PRO IDs
  • UniProt Protein Accession ➞ Entrez Gene IDs

Files

 

Human Protein Atlas (HPA)

Homepage: https://www.proteinatlas.org/
Citation:

Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419

Usage: The Human Protein Atlas (HPA) was utilized to create rna-cell, rna-anatomy, protein-cell, and protein-anatomy edges. Evidence between gene and RNA expression in specific tissue types was derived by HPA, such that the consensus normalized expression was >=1.0. Zooma was utilized to automatically annotate the 153 unique tissues and cell types from Human Protein Atlas for all human protein-coding genes in the Human Proteome 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 281 mappings (1.84 mappings/concepts).

Files

 

National Center for Biotechnology Information (NCBI) Entrez Gene

Homepage: https://www.ncbi.nlm.nih.gov/gene/
Citation:

Maglott D, Ostell J, Pruitt KD, Tatusova T. Entrez Gene: gene-centered information at NCBI. Nucleic Acids Research. 2005;33(suppl_1):D54-8.

Usage: The National Center for Biotechnology Information (NCBI) Gene data was utilized to obtain mappings between NCBI Gene identifiers, HUGO gene symbols, UniProt Accession identifiers, and Protein Ontology identifiers. For additional details on the processing of these data, see Data_Preparation.ipynb:

  • Ensembl Transcript IDs ➞ PRO IDs
  • Gene Ensembl IDs ➞ Entrez Gene IDs
  • Gene Ensembl IDs ➞ PRO IDs
  • Gene Symbols ➞ Transcript Ensembl IDs
  • Entrez Gene IDs ➞ Transcript Ensembl IDs
  • Entrez Gene IDs ➞ PRO IDs
  • Protein Ensembl IDs ➞ UniProt Protein Accession
  • STRING IDs ➞ PRO IDs
  • UniProt Protein Accession ➞ Entrez Gene IDs

Files

 

Reactome Pathway Database

Homepage: https://reactome.org/
Citation:

Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B, Milacic M. The reactome pathway knowledgebase. Nucleic Acids Research. 2017;46(D1):D649-55

Usage: The Reactome Database was utilized to create chemical-pathway, GO Biological process-pathway, pathway-GO Cellular component, GO Molecular function-pathway, and protein-pathway edges. The original data is filtered such that only records meeting the following criteria were included:

  • chemical-pathway: column[5] == "Homo sapiens"
  • GO Biological process-pathway: column[5] startswith "REACTOME", column[8] == "P", and column[12] == "taxon:9606"
  • pathway-GO Cellular component: column[5] startswith "REACTOME", column[8] == "C", and column[12] == "taxon:9606"
  • GO Molecular function-pathway: column[5] startswith "REACTOME", column[8] == "F", and column[12] == "taxon:9606"
  • protein-pathway: column[5] == "Homo sapiens"

Files

 

Search Tool for Recurring Instances of Neighbouring Genes (STRING) Database

Homepage: string-db.org
Citation:

Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research. 2018;47(D1):D607-13

Usage: The Search Tool for Recurring Instances of Neighbouring Genes (STRING) Database was utilized to create protein-protein edges. The original data is filtered such that only records meeting the following criteria were included: combined_score >= "700" (>90th percentile).

Files

 

Universal Protein Resource (UniProt) Knowledgebase

Homepage: https://www.uniprot.org/
Citation:

UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic acids research. 2018;47(D1):D506-15

Usage: The Universal Protein Resource (UniProt) Knowledgebase was utilized to obtain cofactor/catalyst-protein and protein-coding gene-protein edges as well as mappings between NCBI Gene identifiers, HUGO gene symbols, Universal Protein Resource (UniProt) Accession identifiers, and Protein Ontology identifiers. For additional details on the processing of these data, see Data_Preparation.ipynb:

  • Ensembl Transcript IDs ➞ PRO IDs
  • Gene Ensembl IDs ➞ Entrez Gene IDs
  • Gene Ensembl IDs ➞ PRO IDs
  • Gene Symbols ➞ Transcript Ensembl IDs
  • Entrez Gene IDs ➞ Transcript Ensembl IDs
  • Entrez Gene IDs ➞ PRO IDs
  • Protein Ensembl IDs ➞ UniProt Protein Accession
  • STRING IDs ➞ PRO IDs
  • UniProt Protein Accession ➞ Entrez Gene IDs

Files

 

This project is licensed under Apache License 2.0 - see the LICENSE.md file for details. If you intend to use any of the information on this Wiki, please provide the appropriate attribution by citing this repository:

@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}
}

Files

CLINVAR_VARIANT_GENE_DISEASE_PHENOTYPE_EDGES.txt

Files (4.9 GB)

Name Size
md5:e4486ed589a00c1f793e8fdbe856d8ff
599.1 MB Download
md5:80b2c65f4b9287b83742e403db089ba7
3.0 GB Preview Download
md5:5a32173b440a4c52e3567290f8ea64e0
122.3 MB Download
md5:3bca74c06b015a7951ee52f119d8917b
3.7 MB Preview Download
md5:643af404df0449ce60f6c5c7a1a8384d
5.2 kB Preview Download
md5:2e73a1af68e406d54609c004fadee2be
3.5 MB Preview Download
md5:d309dc49022dc62799d171388cd3659c
31.8 MB Preview Download
md5:0a2880eeb9d3598e3374e1fc7254b479
2.9 MB Preview Download
md5:3ac6b4e50537b0de0c2dac0e6774985f
15.2 MB Preview Download
md5:919e3a1a417eb4c85b828357c8bf1285
1.1 MB Preview Download
md5:50fceb6d2c8442d388297faa5fc0902f
75.4 MB Download
md5:c280033605e9f53a557d88ec3ecab694
19.3 MB Preview Download
md5:91eaf9121410ea9345447d16c9c2c7aa
153.2 MB Download
md5:03be359ef0a8a83bcfac79528d2b29fb
80.2 MB Download
md5:457542686e53355509b56c742f702616
20.6 MB Preview Download
md5:2d09d7dd5d597636dccd81441b096092
8.2 kB Preview Download
md5:bd7e6a3412f15bcb16be70a4a3f2107f
1.5 kB Preview Download
md5:0649197bacafbcd9a8179af289bf8fd3
7.4 MB Preview Download
md5:400bf67a8eaec7a5fcf46cd0bd16599f
4.5 kB Preview Download
md5:76233b40c35471a686113e3eb730ac12
111.2 MB Preview Download
md5:03abaa87e783b136db8263c5c3e0a9d3
353.4 kB Preview Download
md5:7bafb13e5cd66970d713817d3da9fb3c
226.0 MB Download
md5:856a433835fbd8a4155cef9c62ba2892
323.7 MB Download
md5:aaf3cf23757300ef8edebc68eeafc02a
1.4 MB Preview Download
md5:6eaf273a10090759c73cff9cd3772a74
1.5 kB Preview Download
md5:06ef57fdc690697766187c0feee0d2d3
77.2 MB Preview Download
md5:bf6f367c9e46757dd9df8c8dd84a8f03
699.8 kB Preview Download
md5:0f1a9a2571e2fdd74e1bdb58f71e4a78
7.4 MB Preview Download
md5:f5394e38b7e023d51895b412aa66af5f
15.3 kB Preview Download
md5:e80d7c5be0721fbe5b35e95cc4505f65
4.9 MB Download
md5:193967b63472d80331e07e1f7aba9434
393.7 kB Preview Download
md5:9d5145caf1928b4626cec650ddb13d9b
44.4 kB Preview Download
md5:ea1a7267d7c2be5e6295040ceee5ab6c
7.2 kB Preview Download
md5:50f2a2d3a9b69dc1af5d3e667e674952
845.6 kB Download
md5:eff3731dc6079eb290a82ad16e6437a4
5.0 MB Preview Download
md5:191ed5688ec0c4639752eb7585478576
4.9 MB Download
md5:423f2a6e7fe9b478666ec8a3a6221313
1.3 MB Preview Download
md5:f59669e8ffbd264c847cbe49b7d4e9aa
21.9 MB Download
md5:03cfb0f2c9b461292d1d10bc2fdc5167
3.7 MB Preview Download
md5:47c1268adabb26ec32fadcf32de260d7
1.6 MB Preview Download
md5:f4ac18d48dfd3551de80d3935b5c485a
177.6 kB Preview Download
md5:ca2148d04ec43fd02f149d6dfe8a9441
8.2 MB Download