SlideShare a Scribd company logo
RDF
•


•



    etc


•




•
B=

     150B



     113B



     75B



     38B



       B
       1982   1986   1990   1994   1998   2002   2006   2010
Linked Data for integrating life-science databases
Linked Data for integrating life-science databases
Linked Data for integrating life-science databases
ID




Gene Ontology, EC   etc
Linked Data for integrating life-science databases
RDF

•


•         UniProt


•         PDBJ DDBJ


•Bio2RDF BioGateway
    RDF
UniProt RDF

• UniProt




•




• UniProt     RDF
UniProt

  Name                          Description                     Source          File size    #triples
  uniprot    Protein annotation data                       UniProt consortium     14G         3.3 B
  uniref     Clusters of proteins with similar sequences   UniProt consortium     7G          900M
  uniparc    Non-redundant archive of UniProt sequences    UniProt consortium     65G          1B
 citations   Literature citations                          UniProt consortium   1355M       10,177,308
 taxonomy    Classification of organisms                    UniProt consortium    421M       5,041,437
 journals    Journals                                      UniProt consortium     3M         34,850
 pathways    Pathways                                      UniProt consortium   1000K         8,865
 keywords    Keywords                                      UniProt consortium    940K         8,449
 locations   Subcellular locations                         UniProt consortium    468K         4,476
  tissues    TIssues                                       UniProt consortium    572K         7439
components   Cellular components (Organelles)              UniProt consortium     6K           43
    go       Gene onotology                                       SBI            25M         263,944
 enzymes     Classification of enzymes                       GO consortium         4M          4,476
 core.owl    Classes and properties for UniProt RDF        UniProt consortium    152K
#triples
  Sesame       Java                       70 M
   4store       C                         15 B
   5store       C
  Virtuoso      C                        15.4 B
   Jena        Java                       1.7 B
  Bigdata      Java                      12.7 B
   ARC         PHP
AllegroGraph   Lisp                        1B
                      http://esw.w3.org/LargeTripleStores
Protein                                          UniProt
         Components                        encodedIn
                            core.owl

<owl:ObjectProperty rdf:about="encodedIn">
    <rdfs:label rdf:datatype="&xsd;string">encoded in</rdfs:label>
    <rdfs:comment rdf:datatype="&xsd;string"
        >The subcellular location where a protein is encoded.</rdfs:comment>
    <rdfs:domain rdf:resource="Protein"/>
    <rdfs:range rdf:resource="Subcellular_Location"/>
</owl:ObjectProperty>
RDF                                                     purl
             http://purl.uniprot.org/{database}/{identifier}

                 UniProt

                     http://purl.uniprot.org/core/

                                Gene                           URI

                  http://purl.uniprot.org/core/Gene

      type
PDBJ, DDBJ                  RDF

• PDBJ
                 47     4.7B


• http://www.pdbj.org/rdf      ID


• DDBJ                                    INSD: International Nucleotide
 Sequence Database                  1.2                              76
      7.6B


• mulgara (http://mulgara.org/)
RDF



     KEGG Taxonomy          23,238
KEGG GENES Cyanobacteria    708,745
        KEGG OC            10,384,602
 hmmer Pfam-A vs Cyano     11,881,212
 hmmer Pfam-B vs Cyano     7,007,154
    Kazusa Annotatioin     2,807,879
1

•


• Synechococcus


• 1.0e-20


•                 Pfam
1     SPARQL
SPARQL 
PREFIX hmmer: <http://hmmer.janelia.org/>
PREFIX kegg: <http://www.kegg.jp/>
PREFIX kg:     <http://www.kegg.jp/entry/>
PREFIX pfam: <http://pfam.sanger.ac.uk/>
PREFIX kt:     <http://www.kegg.jp/taxon/>
SELECT ?pfam1, ?pfam2, COUNT(DISTINCT(?org))
WHERE {
  GRAPH <hmmer_pfam_a_cyano> {
    ?gene hmmer:hit        ?n1 .
    ?gene hmmer:hit        ?n2 .
    ?n1    pfam:pfam_id    ?pfam1 .
    ?n1    hmmer:i-evalue ?eval1 .
    ?n2    pfam:pfam_id    ?pfam2 .
    ?n2    hmmer:i-evalue ?eval2 .
  }
  GRAPH <http://www.kegg.jp/genes> {
    ?gene kegg:belongs_to ?org .
  }
  GRAPH <http://www.kegg.jp/taxonomy> {
    ?org kegg:belongs_to kt:Synechococcus .
  }
  FILTER (?eval1 < 1.0e-10 && ?eval2 < 1.0e-10 && ?pfam1 != ?pfam2)
};
10

  Domain I     Domain II             #genes            #species
RNA_pol_Rpb2 RNA_pol_Rpb2              9                  9
     _3
  G6PD_N          _1
               G6PD_C                  9                  9
5_3_exonuc_N     5_3_exonuc           9                   9
      HIT          DcpS_C             9                   9
Glyco_hydro_38 Glyco_hydro_38         9                   9
                     C
RNA_pol_Rpb2 RNA_pol_Rpb2             9                   9
      _6
   GARS_N            _3
                  GARS_C              9                   9
    DSHCT           DEAD              9                   9
   adh_short         KR               12                  9
    EFG_C          EFG_IV             10                  9
                    ....   171   9     Synechococcus
2

• KEGG                    OC


• Cyanobacteria


• Kazusa Annotation    PumMed


• KO   KEGG Othology
2     SPARQL
SPARQL
PREFIX kegg: <http://www.kegg.jp/>
PREFIX kg: <http://www.kegg.jp/entry/>
PREFIX kt: <http://www.kegg.jp/taxon/>
PREFIX kns: <http://a.kazusa.or.jp/ns/>
SELECT ?oc, ?gene, ?ko, COUNT(DISTINCT(?pm))
WHERE {
  GRAPH <http://www.kegg.jp/oc> {
    ?gene kegg:belongs_to ?oc .
  }
  GRAPH <http://www.kegg.jp/genes> {
    ?gene kegg:belongs_to ?taxon .
    ?gene kegg:linked_to ?cb_gene .
    OPTIONAL {
      ?gene kg:ortholog ?ko .
    }
  }
  GRAPH <http://www.kegg.jp/taxonomy> {
    ?taxon kegg:belongs_to kt:Cyanobacteria .
  }
  GRAPH <http://kazusa.or.jp/cyanobase> {
    ?cb_gene ?p1 ?bm .
    ?bm      ?p2 ?pm .
  }
};
PumMed ID                         10

      OC        #gene with PMID        #PMID
 Genes_537709          3                1296
 Genes_565278          3                761
 Genes_710476          2                527
 Genes_189668          1                497
 Genes_710587          1                479
 Genes_710480          1                416
 Genes_711471          1                407
 Genes_71824           1                393
 Genes_75617           5                381
 Genes_711511          1                376
Semantic Web

•       URI


•


•


• W3C
Semantic Web

• SPARQL
                          ->




•              ->


•


•                    ->

More Related Content

What's hot

EB-eye Back End
EB-eye Back EndEB-eye Back End
EB-eye Back End
Franck Valentin
 
Seqr - Protein Sequence Search: Presented by Lianyi Han, Medical Science & Co...
Seqr - Protein Sequence Search: Presented by Lianyi Han, Medical Science & Co...Seqr - Protein Sequence Search: Presented by Lianyi Han, Medical Science & Co...
Seqr - Protein Sequence Search: Presented by Lianyi Han, Medical Science & Co...
Lucidworks
 
NCBI Boot Camp for Beginners Slides
NCBI Boot Camp for Beginners SlidesNCBI Boot Camp for Beginners Slides
NCBI Boot Camp for Beginners Slides
Jackie Wirz, PhD
 
PAG-2004-Roe
PAG-2004-RoePAG-2004-Roe
PAG-2004-Roe
mounir elharam
 
Sequencing and Bioinformatics PGRP Summer 2015
Sequencing and Bioinformatics PGRP Summer 2015Sequencing and Bioinformatics PGRP Summer 2015
Sequencing and Bioinformatics PGRP Summer 2015
Surya Saha
 
Next-generation sequencing from 2005 to 2020
Next-generation sequencing from 2005 to 2020Next-generation sequencing from 2005 to 2020
Next-generation sequencing from 2005 to 2020
Christian Frech
 

What's hot (6)

EB-eye Back End
EB-eye Back EndEB-eye Back End
EB-eye Back End
 
Seqr - Protein Sequence Search: Presented by Lianyi Han, Medical Science & Co...
Seqr - Protein Sequence Search: Presented by Lianyi Han, Medical Science & Co...Seqr - Protein Sequence Search: Presented by Lianyi Han, Medical Science & Co...
Seqr - Protein Sequence Search: Presented by Lianyi Han, Medical Science & Co...
 
NCBI Boot Camp for Beginners Slides
NCBI Boot Camp for Beginners SlidesNCBI Boot Camp for Beginners Slides
NCBI Boot Camp for Beginners Slides
 
PAG-2004-Roe
PAG-2004-RoePAG-2004-Roe
PAG-2004-Roe
 
Sequencing and Bioinformatics PGRP Summer 2015
Sequencing and Bioinformatics PGRP Summer 2015Sequencing and Bioinformatics PGRP Summer 2015
Sequencing and Bioinformatics PGRP Summer 2015
 
Next-generation sequencing from 2005 to 2020
Next-generation sequencing from 2005 to 2020Next-generation sequencing from 2005 to 2020
Next-generation sequencing from 2005 to 2020
 

Similar to Linked Data for integrating life-science databases

Role of bioinformatics in life sciences research
Role of bioinformatics in life sciences researchRole of bioinformatics in life sciences research
Role of bioinformatics in life sciences research
Anshika Bansal
 
RML NCBI Resources
RML NCBI ResourcesRML NCBI Resources
RML NCBI Resources
Jackie Wirz, PhD
 
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle
RNA-Seq transcriptome analysis of Gonium pectorale cell cycleRNA-Seq transcriptome analysis of Gonium pectorale cell cycle
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle
Jennifer Shelton
 
Bio2RDF@BH2010
Bio2RDF@BH2010Bio2RDF@BH2010
Bio2RDF@BH2010
François Belleau
 
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle.
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle.RNA-Seq transcriptome analysis of Gonium pectorale cell cycle.
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle.
Jennifer Shelton
 
ICAR 2015 Workshop - Nick Provart
ICAR 2015 Workshop - Nick ProvartICAR 2015 Workshop - Nick Provart
ICAR 2015 Workshop - Nick Provart
Araport
 
Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System
Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge SystemBio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System
Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System
François Belleau
 
Bioinformatic_Databases_2.ppt
Bioinformatic_Databases_2.pptBioinformatic_Databases_2.ppt
Bioinformatic_Databases_2.ppt
NaglaaFathy42
 
Bioinformatic_Databases_2xcxzczxcxzxcxzc
Bioinformatic_Databases_2xcxzczxcxzxcxzcBioinformatic_Databases_2xcxzczxcxzxcxzc
Bioinformatic_Databases_2xcxzczxcxzxcxzc
AdiM27
 
Bioinformatic databases 2
Bioinformatic databases 2Bioinformatic databases 2
Bioinformatic databases 2
Razzaqe
 
Bioinformatic databases 2
Bioinformatic databases 2Bioinformatic databases 2
Bioinformatic databases 2
Razzaqe
 
Towards a Reference Genome for Switchgrass (Panicum virgatum) - Schmutz jeremy
Towards a Reference Genome for Switchgrass (Panicum virgatum) - Schmutz jeremyTowards a Reference Genome for Switchgrass (Panicum virgatum) - Schmutz jeremy
Towards a Reference Genome for Switchgrass (Panicum virgatum) - Schmutz jeremy
Shaojun Xie
 
第2回LinkedData勉強会@yayamamo
第2回LinkedData勉強会@yayamamo第2回LinkedData勉強会@yayamamo
第2回LinkedData勉強会@yayamamo
yayamamo @ DBCLS Kashiwanoha
 
Bioinformatic_Databases_2.ppt Bioinformatics
Bioinformatic_Databases_2.ppt BioinformaticsBioinformatic_Databases_2.ppt Bioinformatics
Bioinformatic_Databases_2.ppt Bioinformatics
MohamedHasan816582
 
Bio2RDF @ W3C HCLS2009
Bio2RDF @ W3C HCLS2009Bio2RDF @ W3C HCLS2009
Bio2RDF @ W3C HCLS2009
François Belleau
 
Protein synthesis
Protein synthesis Protein synthesis
Protein synthesis
Samson Sakala Jnr
 
PDF文档.pdf
PDF文档.pdfPDF文档.pdf
PDF文档.pdf
SanaKhan250785
 
Crispr/cas9 101
Crispr/cas9 101Crispr/cas9 101
Crispr/cas9 101
Suk Namgoong
 
Submitted sequence (strains)
Submitted sequence (strains)Submitted sequence (strains)
Submitted sequence (strains)
Syeda Masoom Fatima
 
What's New at Araport - ICAR 2017
What's New at Araport - ICAR 2017What's New at Araport - ICAR 2017
What's New at Araport - ICAR 2017
Vivek Krishnakumar
 

Similar to Linked Data for integrating life-science databases (20)

Role of bioinformatics in life sciences research
Role of bioinformatics in life sciences researchRole of bioinformatics in life sciences research
Role of bioinformatics in life sciences research
 
RML NCBI Resources
RML NCBI ResourcesRML NCBI Resources
RML NCBI Resources
 
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle
RNA-Seq transcriptome analysis of Gonium pectorale cell cycleRNA-Seq transcriptome analysis of Gonium pectorale cell cycle
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle
 
Bio2RDF@BH2010
Bio2RDF@BH2010Bio2RDF@BH2010
Bio2RDF@BH2010
 
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle.
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle.RNA-Seq transcriptome analysis of Gonium pectorale cell cycle.
RNA-Seq transcriptome analysis of Gonium pectorale cell cycle.
 
ICAR 2015 Workshop - Nick Provart
ICAR 2015 Workshop - Nick ProvartICAR 2015 Workshop - Nick Provart
ICAR 2015 Workshop - Nick Provart
 
Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System
Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge SystemBio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System
Bio2RDF: Towards A Mashup To Build Bioinformatics Knowledge System
 
Bioinformatic_Databases_2.ppt
Bioinformatic_Databases_2.pptBioinformatic_Databases_2.ppt
Bioinformatic_Databases_2.ppt
 
Bioinformatic_Databases_2xcxzczxcxzxcxzc
Bioinformatic_Databases_2xcxzczxcxzxcxzcBioinformatic_Databases_2xcxzczxcxzxcxzc
Bioinformatic_Databases_2xcxzczxcxzxcxzc
 
Bioinformatic databases 2
Bioinformatic databases 2Bioinformatic databases 2
Bioinformatic databases 2
 
Bioinformatic databases 2
Bioinformatic databases 2Bioinformatic databases 2
Bioinformatic databases 2
 
Towards a Reference Genome for Switchgrass (Panicum virgatum) - Schmutz jeremy
Towards a Reference Genome for Switchgrass (Panicum virgatum) - Schmutz jeremyTowards a Reference Genome for Switchgrass (Panicum virgatum) - Schmutz jeremy
Towards a Reference Genome for Switchgrass (Panicum virgatum) - Schmutz jeremy
 
第2回LinkedData勉強会@yayamamo
第2回LinkedData勉強会@yayamamo第2回LinkedData勉強会@yayamamo
第2回LinkedData勉強会@yayamamo
 
Bioinformatic_Databases_2.ppt Bioinformatics
Bioinformatic_Databases_2.ppt BioinformaticsBioinformatic_Databases_2.ppt Bioinformatics
Bioinformatic_Databases_2.ppt Bioinformatics
 
Bio2RDF @ W3C HCLS2009
Bio2RDF @ W3C HCLS2009Bio2RDF @ W3C HCLS2009
Bio2RDF @ W3C HCLS2009
 
Protein synthesis
Protein synthesis Protein synthesis
Protein synthesis
 
PDF文档.pdf
PDF文档.pdfPDF文档.pdf
PDF文档.pdf
 
Crispr/cas9 101
Crispr/cas9 101Crispr/cas9 101
Crispr/cas9 101
 
Submitted sequence (strains)
Submitted sequence (strains)Submitted sequence (strains)
Submitted sequence (strains)
 
What's New at Araport - ICAR 2017
What's New at Araport - ICAR 2017What's New at Araport - ICAR 2017
What's New at Araport - ICAR 2017
 

Recently uploaded

BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
SAI KAILASH R
 
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
AimanAthambawa1
 
Finetuning GenAI For Hacking and Defending
Finetuning GenAI For Hacking and DefendingFinetuning GenAI For Hacking and Defending
Finetuning GenAI For Hacking and Defending
Priyanka Aash
 
LeadMagnet IQ Review: Unlock the Secret to Effortless Traffic and Leads.pdf
LeadMagnet IQ Review:  Unlock the Secret to Effortless Traffic and Leads.pdfLeadMagnet IQ Review:  Unlock the Secret to Effortless Traffic and Leads.pdf
LeadMagnet IQ Review: Unlock the Secret to Effortless Traffic and Leads.pdf
SelfMade bd
 
Zaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdfZaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdf
AmandaCheung15
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
sunilverma7884
 
Redefining Cybersecurity with AI Capabilities
Redefining Cybersecurity with AI CapabilitiesRedefining Cybersecurity with AI Capabilities
Redefining Cybersecurity with AI Capabilities
Priyanka Aash
 
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
alexjohnson7307
 
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
FIDO Alliance
 
Vertex AI Agent Builder - GDG Alicante - Julio 2024
Vertex AI Agent Builder - GDG Alicante - Julio 2024Vertex AI Agent Builder - GDG Alicante - Julio 2024
Vertex AI Agent Builder - GDG Alicante - Julio 2024
Nicolás Lopéz
 
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
FIDO Alliance
 
Retrieval Augmented Generation Evaluation with Ragas
Retrieval Augmented Generation Evaluation with RagasRetrieval Augmented Generation Evaluation with Ragas
Retrieval Augmented Generation Evaluation with Ragas
Zilliz
 
Tailored CRM Software Development for Enhanced Customer Insights
Tailored CRM Software Development for Enhanced Customer InsightsTailored CRM Software Development for Enhanced Customer Insights
Tailored CRM Software Development for Enhanced Customer Insights
SynapseIndia
 
Intel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdfIntel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdf
Tech Guru
 
Improving Learning Content Efficiency with Reusable Learning Content
Improving Learning Content Efficiency with Reusable Learning ContentImproving Learning Content Efficiency with Reusable Learning Content
Improving Learning Content Efficiency with Reusable Learning Content
Enterprise Knowledge
 
Integrating Kafka with MuleSoft 4 and usecase
Integrating Kafka with MuleSoft 4 and usecaseIntegrating Kafka with MuleSoft 4 and usecase
Integrating Kafka with MuleSoft 4 and usecase
shyamraj55
 
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
OnBoard
 
What's New in Teams Calling, Meetings, Devices June 2024
What's New in Teams Calling, Meetings, Devices June 2024What's New in Teams Calling, Meetings, Devices June 2024
What's New in Teams Calling, Meetings, Devices June 2024
Stephanie Beckett
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
ldtexsolbl
 
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python CodebaseEuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
Jimmy Lai
 

Recently uploaded (20)

BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
 
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
COVID-19 and the Level of Cloud Computing Adoption: A Study of Sri Lankan Inf...
 
Finetuning GenAI For Hacking and Defending
Finetuning GenAI For Hacking and DefendingFinetuning GenAI For Hacking and Defending
Finetuning GenAI For Hacking and Defending
 
LeadMagnet IQ Review: Unlock the Secret to Effortless Traffic and Leads.pdf
LeadMagnet IQ Review:  Unlock the Secret to Effortless Traffic and Leads.pdfLeadMagnet IQ Review:  Unlock the Secret to Effortless Traffic and Leads.pdf
LeadMagnet IQ Review: Unlock the Secret to Effortless Traffic and Leads.pdf
 
Zaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdfZaitechno Handheld Raman Spectrometer.pdf
Zaitechno Handheld Raman Spectrometer.pdf
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
 
Redefining Cybersecurity with AI Capabilities
Redefining Cybersecurity with AI CapabilitiesRedefining Cybersecurity with AI Capabilities
Redefining Cybersecurity with AI Capabilities
 
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
 
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
 
Vertex AI Agent Builder - GDG Alicante - Julio 2024
Vertex AI Agent Builder - GDG Alicante - Julio 2024Vertex AI Agent Builder - GDG Alicante - Julio 2024
Vertex AI Agent Builder - GDG Alicante - Julio 2024
 
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
 
Retrieval Augmented Generation Evaluation with Ragas
Retrieval Augmented Generation Evaluation with RagasRetrieval Augmented Generation Evaluation with Ragas
Retrieval Augmented Generation Evaluation with Ragas
 
Tailored CRM Software Development for Enhanced Customer Insights
Tailored CRM Software Development for Enhanced Customer InsightsTailored CRM Software Development for Enhanced Customer Insights
Tailored CRM Software Development for Enhanced Customer Insights
 
Intel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdfIntel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdf
 
Improving Learning Content Efficiency with Reusable Learning Content
Improving Learning Content Efficiency with Reusable Learning ContentImproving Learning Content Efficiency with Reusable Learning Content
Improving Learning Content Efficiency with Reusable Learning Content
 
Integrating Kafka with MuleSoft 4 and usecase
Integrating Kafka with MuleSoft 4 and usecaseIntegrating Kafka with MuleSoft 4 and usecase
Integrating Kafka with MuleSoft 4 and usecase
 
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
 
What's New in Teams Calling, Meetings, Devices June 2024
What's New in Teams Calling, Meetings, Devices June 2024What's New in Teams Calling, Meetings, Devices June 2024
What's New in Teams Calling, Meetings, Devices June 2024
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
 
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python CodebaseEuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
 

Linked Data for integrating life-science databases

  • 1. RDF
  • 2. • • etc • •
  • 3. B= 150B 113B 75B 38B B 1982 1986 1990 1994 1998 2002 2006 2010
  • 9. RDF • • UniProt • PDBJ DDBJ •Bio2RDF BioGateway RDF
  • 11. UniProt Name Description Source File size #triples uniprot Protein annotation data UniProt consortium 14G 3.3 B uniref Clusters of proteins with similar sequences UniProt consortium 7G 900M uniparc Non-redundant archive of UniProt sequences UniProt consortium 65G 1B citations Literature citations UniProt consortium 1355M 10,177,308 taxonomy Classification of organisms UniProt consortium 421M 5,041,437 journals Journals UniProt consortium 3M 34,850 pathways Pathways UniProt consortium 1000K 8,865 keywords Keywords UniProt consortium 940K 8,449 locations Subcellular locations UniProt consortium 468K 4,476 tissues TIssues UniProt consortium 572K 7439 components Cellular components (Organelles) UniProt consortium 6K 43 go Gene onotology SBI 25M 263,944 enzymes Classification of enzymes GO consortium 4M 4,476 core.owl Classes and properties for UniProt RDF UniProt consortium 152K
  • 12. #triples Sesame Java 70 M 4store C 15 B 5store C Virtuoso C 15.4 B Jena Java 1.7 B Bigdata Java 12.7 B ARC PHP AllegroGraph Lisp 1B http://esw.w3.org/LargeTripleStores
  • 13. Protein UniProt Components encodedIn core.owl <owl:ObjectProperty rdf:about="encodedIn"> <rdfs:label rdf:datatype="&xsd;string">encoded in</rdfs:label> <rdfs:comment rdf:datatype="&xsd;string" >The subcellular location where a protein is encoded.</rdfs:comment> <rdfs:domain rdf:resource="Protein"/> <rdfs:range rdf:resource="Subcellular_Location"/> </owl:ObjectProperty>
  • 14. RDF purl http://purl.uniprot.org/{database}/{identifier} UniProt http://purl.uniprot.org/core/ Gene URI http://purl.uniprot.org/core/Gene type
  • 15. PDBJ, DDBJ RDF • PDBJ 47 4.7B • http://www.pdbj.org/rdf ID • DDBJ INSD: International Nucleotide Sequence Database 1.2 76 7.6B • mulgara (http://mulgara.org/)
  • 16. RDF KEGG Taxonomy 23,238 KEGG GENES Cyanobacteria 708,745 KEGG OC 10,384,602 hmmer Pfam-A vs Cyano 11,881,212 hmmer Pfam-B vs Cyano 7,007,154 Kazusa Annotatioin 2,807,879
  • 18. 1 SPARQL SPARQL  PREFIX hmmer: <http://hmmer.janelia.org/> PREFIX kegg: <http://www.kegg.jp/> PREFIX kg: <http://www.kegg.jp/entry/> PREFIX pfam: <http://pfam.sanger.ac.uk/> PREFIX kt: <http://www.kegg.jp/taxon/> SELECT ?pfam1, ?pfam2, COUNT(DISTINCT(?org)) WHERE {   GRAPH <hmmer_pfam_a_cyano> {     ?gene hmmer:hit ?n1 .     ?gene hmmer:hit ?n2 .     ?n1 pfam:pfam_id ?pfam1 .     ?n1 hmmer:i-evalue ?eval1 .     ?n2 pfam:pfam_id ?pfam2 .     ?n2 hmmer:i-evalue ?eval2 .   }   GRAPH <http://www.kegg.jp/genes> {     ?gene kegg:belongs_to ?org .   }   GRAPH <http://www.kegg.jp/taxonomy> {     ?org kegg:belongs_to kt:Synechococcus .   }   FILTER (?eval1 < 1.0e-10 && ?eval2 < 1.0e-10 && ?pfam1 != ?pfam2) };
  • 19. 10 Domain I Domain II #genes #species RNA_pol_Rpb2 RNA_pol_Rpb2 9 9 _3 G6PD_N _1 G6PD_C 9 9 5_3_exonuc_N 5_3_exonuc 9 9 HIT DcpS_C 9 9 Glyco_hydro_38 Glyco_hydro_38 9 9 C RNA_pol_Rpb2 RNA_pol_Rpb2 9 9 _6 GARS_N _3 GARS_C 9 9 DSHCT DEAD 9 9 adh_short KR 12 9 EFG_C EFG_IV 10 9 .... 171 9 Synechococcus
  • 20. 2 • KEGG OC • Cyanobacteria • Kazusa Annotation PumMed • KO KEGG Othology
  • 21. 2 SPARQL SPARQL PREFIX kegg: <http://www.kegg.jp/> PREFIX kg: <http://www.kegg.jp/entry/> PREFIX kt: <http://www.kegg.jp/taxon/> PREFIX kns: <http://a.kazusa.or.jp/ns/> SELECT ?oc, ?gene, ?ko, COUNT(DISTINCT(?pm)) WHERE {   GRAPH <http://www.kegg.jp/oc> {     ?gene kegg:belongs_to ?oc .   }   GRAPH <http://www.kegg.jp/genes> {     ?gene kegg:belongs_to ?taxon .     ?gene kegg:linked_to ?cb_gene .     OPTIONAL {       ?gene kg:ortholog ?ko .     }   }   GRAPH <http://www.kegg.jp/taxonomy> {     ?taxon kegg:belongs_to kt:Cyanobacteria .   }   GRAPH <http://kazusa.or.jp/cyanobase> {     ?cb_gene ?p1 ?bm .     ?bm ?p2 ?pm .   } };
  • 22. PumMed ID 10 OC #gene with PMID #PMID Genes_537709 3 1296 Genes_565278 3 761 Genes_710476 2 527 Genes_189668 1 497 Genes_710587 1 479 Genes_710480 1 416 Genes_711471 1 407 Genes_71824 1 393 Genes_75617 5 381 Genes_711511 1 376
  • 23. Semantic Web • URI • • • W3C
  • 24. Semantic Web • SPARQL -> • -> • • ->