Post on 14-Jul-2020
2011/3/4
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Future biology is Semanticinformation science
Tetsuro Toyoda, ph.DDirector of
Bioinformatics And Systems Engineering Division, RIKEN,
Japan11
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Nature of Life=Information
Nature of Replication
(Self‐replication)
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Life evolving
through replications and selectionsVarious replications(Fertilized eggs)
Replication of genome information(DNA replication)
Various groups of individuals
Evolution Cycle (About 3.8billion years)
Natural Selection3
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Genome is blueprint of Life(Genetic Information )
Genomic information is recorded by combined DNA blocks.
DNA blocks are structured by only 4 kinds of blocks: A T GDNA blocks are structured by only 4 kinds of blocks: A, T, G, and C.
To map sequences of DNA blocks is Genome sequencing.
Sequenced genomic information is represented by character information as shown below. ATGCTAGTCGCGTGCTAGTCGATCTCGTGCA・・・・・・
Human’s genome represented by about 3 billion letters.
Genome of a model organism (A. thaliana ) is represented by about 100 million letters.
Image of DNA blocks4
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Biological diversity resulting from diversity of Genomic information
Informatics viewpoint
Sequences of DNA blocks evolve↓means
Information is evolved(Biological diversity)
Informatics viewpoint
Material viewpoint
DNA of human and microorganisms are consisted of the same 4 kinds of DNA blocks (G,A,C, and T).↓
Any living organism hasn’t evolved as a material. (Common among living organisms)
Material viewpoint
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Nature of Life = Genomic InformationRecording media for information Expression of information (phenotype)
Digital I
CD、DVD
nform
ation
Gen
o
Copied CD(Copied information)
omic In
form
ation
Cell
DNA
Copied DNA(Copied information) 6
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Invention of the machines decoding genomic information from DNA.
DInfo
CD、DVD
The invention allows people to deal with genomic information with computer.
Digital rm
ation
Gen
omic
Inform
ation
DNA
DNASequencer
DNA Synthesizer
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Future biology is information science.Future biologist should learn computer and mathematics
DNASequencer
DNA Synthesizer
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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SciNetS is our platform on the web
f G d ifor Genome design
Part 2
Bioinformatics And Systems Engineering Division, RIKEN,
Japan99
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
What is SciNetS?
What is SciNetS?
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SciNetS Purpose, Methods and System
Purpose-Methods-System
SciNetS: Science for the Planet and SocietySciNetS: Science for the Planet and Society
SciNetS: Science, Innovation, Education, Technology, Synergy
SciNetS: Scientist's Networking SystemSciNetS: Scientist s Networking System
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Science for the Planet and SocietySciNetS
Science for the Planet and Society
Purpose‐Science for the Planet and Society
ySciNetS
Science for the Planet and SocietySciNetS
"The purpose of Scinets is nothing less than to provide the tools so that humankind can adapt the planet's environment and itself so that all living things and human society may survive in the face of an uncertain future."
Tetsuro ToyodaDirector of BASE Division
The cloning of species such as rice to acquire stronger, or more environmentally friendly, crops could be made easier thanks to the intelligent search engine such as PosMed-plus. 12
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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ScienceScinets
InnovationScinets
Methods and Disciplines
Scinets
EducationScinets
TechnologyScinets
SynergyScinets
"SciNetS creates new synergy for the disciplines of science, technology and education, as a collaboration engine and incubator for innovation"
Tetsuro ToyodaDirector of BASE 13
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Scientist'sSciNets
NetworkingSciNets
System Infrastructure for Collaboration
System: Infrastructure for CollaborationThe Scinets Scientist's Networking System is a web system built on the next generation Semantic Web standard.
Making use of the latest in cloud computing technology the system is capable of simultaneously
SystemSciNetS
What databases are needed to realize rational organism design? That is the question we attempt to answer.”
Tetsuro Toyoda technology, the system is capable of simultaneously hosting upwards of thousands of virtual laboratories, enabling every individual user to access and control each database and data item.
Scinets is developed and operated by theBASE Bioinformatics And Systems Engineering divisionof RIKEN Institute of Physical and Chemical Science.
Tetsuro Toyoda Director of BASE
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Virtual Labs
Virtual labs on the Scinets system enable each lab researcher to easily create and publish databases without the need to maintain individual web servers.
This information infrastructure is beneficial for researchers conducting international collaborative research.
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Virtual Labs - Integrated information infrastructure
These Virtual labs are used, for example, as integrated life science information infrastructure incorporating functions including a programming environment, a digital lab notes system, and information resources for biomass engineering research activities.
SciNetS contained 644 tables and 7.5 million rows of SemanticTable data as of December 2010.
Integrated Database of MammalsThe integrated databases of mammals in Scinets is developed to secure sustainability, accessibility, utility and publicity of the data produced from multiple large-scale programs promoted by RIKEN.
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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• Plant Integrated Database
• Mammalian Integrated Database
• Integrated Database of Protein Experiments and
Current Virtual Lab Platforms in Scinets
• Integrated Database of Protein Experiments and Structure
• SemanticTable
• GenoCon International Rational Genome Design Contest “where students and researchers compete in designing genome base sequences.”
• Many otherso Personal databaseso Repository for electronic laboratory notes of
unreported datao Joint research or ‘medical clouds’—an electronic
health chart network among medical specialists and clinicians.
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
As an integrated database, Scinets provides a versatile database container compatible with international standards. The system facilitates data sharing and also controls data disclosure.
Using the semantic web, Scinets provides a ‘total incubation infrastructure system’ for life science-related databases.
A Total Incubation Center for Databases
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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This container automatically enables the standardization and collection of data.
Automatic Standardized Data Collection
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
In this fashion Virtual laboratories can create control and evaluate their activities with fine control over what, when and how they are disclosed.
Balanced Control, Evaluation and Disclosure
This creates a new balance where data can be integrated into existing databases, and spread worldwide.
During semantic inference, a user gains access to a Semantic-JSON repository by invoking Semantic-JSON URIs, consisting of an internal ID and a command name, along with step-by-step receipt of a fragment of Semantic Web data in the JSON format.
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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The semantic web is an extension of the widely used internet.
Though the Internet is suitable for use by people, they can read, understand and search for information by following
Scinets is key to analyzing the data
hyperlinks to documents. Automated computer-based data analysis is limited because links don't define the relationships between documents.
In the semantic web, all data has meaning, and every link refers to the relationships between the data, enabling computers to search data effectively for automated data analysis, including doing
Scinets also integrates semantic social networking tools such as Facebook for its users to connect and collaborate with each other.
y , g gthe science itself.
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
“A database is not merely a container for data, it is also a place where even life can evolve.We can create useful biological resources from information
Is even a place where life can evolve
resources by selecting useful genes from databases, designing new genomes, and returning them to the world of living organisms."
Tetsuro ToyodaDirector of BASE
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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The benefit of statistical inference plus explicit semantic relationsKeyword search genetic study data => Ranked candidate genes
Process Huge, Complex, Distributed Data
Global databases such as MEDLINE Scinets web service PosMed
(http://omicspace.riken.jp)
Produces practical in silico positional cloning system as a web service integrating variousOmics entities and biomedical document sets.
Neural Network Processing
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Through GenoCon Open platform for rational genome design –
Develop Researchers to playDevelop Researchers to play significant roles in new industries.
Part 3
Bioinformatics And Systems Engineering Division, RIKEN,
Japan2424
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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By applying Semantic Web for Systems and Synthetic Biology
Creates Useful bio-resources from info-resources
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Fossil Resources Biomass
CO2
Glyco
sy CO2
CO2
CO2Crude
oilApplications
of Renewable Resource
Plants
Sustainable society supported by Green Biotechnology
Petrochemistry process
Distillation
Heavy O
il
Gas oil
Kerosene
Gasoline
Naphtha
Bioprocess
Syngas
Oil, etc
Sugar
Development of
Innovation Technology
Creation of New Industry
ylation
EnergyChemicalProducts Materials
21st Century - Sustainable Society~ Carbon-neutral ~
20th Century - Consumptive Society
New Industry BioMaterials
ChemicalProductsEnergy
↓Search for resourcesDevelopment of oilfield (Fossil resources)
↓Search for resourcesDevelopment of genome (Biological Resource)
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Toward an age of Rational Design using genomic data.
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Open platform for rational genome design
GenoCon International Rational Genome
Design Contest offers contestants the chance to compete in technologies for rational genome design using a browser‐based programming environment provided at http://genocon.org
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Optimize genome design for plants that can absorb and resolve formaldehyde to prevent sick house syndrome.
• Introduce two enzymes, extracted from bacteria, into Calvin cycle of plants
• Enhancement of formaldehyde-resistance by shepherd's-purse and Tobacco.
AB: Gene introduction strain
Formaldehyde tolerance designed by Prof. Izui is the inaugural challenge in GenoCon 29
•
shepherd‘s-purse
AB: Gene introduction strainCK: Control
Tobacco
PHI
HPS Hexulose 6-phosphate synthase
Hexulose 6-phosphate isomerase
Introduced enzymes Patent applied by Prof. Izui,
Kyoto/Kinki University 2929
Various Plant data of Arabidopsis thalianaintegrated in RIKEN SciNetS
Resources from RIKEN• RIKEN RARGE Database• RIKEN Ds transposon line• RIKEN Activation tagging line • RIKEN Arabidopsis Fox‐hunting LineRIKEN Arabidopsis Fox‐hunting Line• RIKEN Arabidopsis Phenotype • RIKEN A.thaliana gene family• RIKEN Arabidopsis Protein record• RIKEN OmicBrowse – Arabidopsis
Resources opened by TAIR • Locus, PO annotation, GO annotation
↓Standardized data generated automatically
Supported by Japan’s National Project of database integration30
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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RIKEN provides a web‐based programming interface for a user to process each data items in our database cloud, where all the programs and files the user has created are stored.
Programming on a web browser
31Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Design freely and Test safely
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Process Flow for Open Optimization Research
Outsourcer/Organizer(Premium user of the platform)Call for designs within the scope
of his/her patented invention ↑ Initial example of DNA sequenceNot yet optimal design
Coverage protected by the patent
Contributor/Contestants(Free users of the platform)
Submit their designs to the contest
Evaluators(Funded from the beneficiary)
Evaluates submitted designs First
SecondThird
Designed examples submitted by the contestants
Outsourcer/Organizer(The beneficiary)
Obtains better designs protected within the scope of the original patent
Initial DNA sequence
↓ A better example of DNA sequence is discovered
Coverage protected by the patent
Contestants enjoy the contest and outsourcer/organizer obtain better examples of DNA sequences.
Designs that have been ranked experimentally
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Incentives for participants in Open Optimization Research
Outsourcer/Organizer/Sponsor(The beneficiary)
Call for designs within the scope of his/her patented inventionand obtain optimal designs
Contestants/Researchers/Students(Contributors)
• Enjoy the contest experience• Learn from their experience
R i d
Open Optimization Research
p g
Researchers/Technical partners
(Evaluators)Obtain numerous examples
f i tifi t di
•Receive awards•Contribute to community
PR Sponsor(The beneficiary)
• Education related advertisement
GenoCon Platform (SciNetS)Provides infrastructure for contest and shares designs
and programs from researchers around the world
for scientific studies to create publications with
funding from the beneficiary
• Education-related advertisement• Learning material business
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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SciNetS Semantic Web Case Study: y
Mouse Genome Project.
Part 4
Bioinformatics And Systems Engineering Division, RIKEN,
Japan3535
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Cloud computing is Internet‐based computing, whereby shared resources, software and information are provided to computers and other devices on‐
demand, like the electricity grid.
Cloud connectingmultiple resources
on demand
Data resources integrated X‐ray protein dataNext‐generation sequencer data
Supercomputer36
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Emergence of new publication media for databases
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
SciNetS (Scientist’s Networking System)
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N b f d t b h t d
Number of databases (Virtual Labs) hosted is larger than the number of servers in the system.
Databases > Server computers > System
The many database projects provided by SciNetS
Scale out (a single system hosting numerous databases)
Number of databases hostedby RIKEN SciNeS
10,000
1 10 100 1,00010,000100,0001,000,000
Number of databases to be integrated
Hosting databases separatelyhas a limitation
Available at http://database.riken.jp
→ SciNetS is expected to function as a new academic medium for publishing individual databases as entire sets of research results.
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More than 200 Virtual laboratories integrated on SciNetS
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All the SciNetS Databases contain meta data, a Semantic Web
Mouse Phenotype Database
“Meta data” (RDF) includes semantic links among data items and
archives
items and resources
“Data resources” include experimental results or statistical results 41
A Semantic web of Mouse databases
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Searching 31 mammalian databases and literature
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Allele Name
Link to equivalent record in MGI
Link to Gdf5 gene in MGI
Link to other alleles in MGI
Container automatically enables the standardization and collection of data
Link to other alleles in MGI
Image (mouse over to magnify) Mutant mouse conveying this allele
Phenotype annotation in MGI
Link to CDT‐DB for Gdf5 gene expression
Detailed Phenotype annotation in RIKEN
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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InterPhenome: International Data Sharing of Mouse Phenome
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Importing a Phenotype Database to Protégé
The URI of this database
1. Copy the URI
2. Click “OK”
1. Copy the URI
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Individuals
Access with a URI
Browsing Mouse Phenotype Data imported in Protégé
Class Tree
Semantic Network
Retrieve RDF
Properties47
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
http://semantic‐json.org
Semantic‐JSON: API for accessing SemanticWeb data with JSON
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Sample Operation in Mathematica
Load the Semantic‐JSON package
Set the server’s URL
Obtain labels of two individuals
Semantic‐JSON supports most languages used by Bio‐informaticians
Obtain the shortest paths between two individuals
Similarly, Semantic‐JSON is available in popular languages including Java, Ruby, Perl, Python.
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
RIKEN provides a web‐based programming interface for a user to process each data items in our database cloud, where all the programs and files the user has created are stored.
Web‐based programming interface
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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“Superbrain” solving biological problems by simulating artificial brain understanding the knowledge of biology
Disease-related genetic interval
RIKEN Super Petaflop Computer
For example, a problem of genetics
Estimate candidate genes
Estimate
RIKEN Super Petaflop Computer
Superbrain
Collective Intelligence
1951
Limit of general search through database
Proposition: “My job is a jail”
job jail
Boolean understands that job ≠ jail52
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Open‐endedness in the brain
Proposition: “My job is a jail”
job jail
Restriction?
Brain tries to understand beyond the framework of general concept
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Developing a brain type database(Superbrain)
Gene1 Ke ord Search b ser
hitGene1Momentary
Documents including Omics information,
literature, etc.~10 million Documentron
Concepts Unit~100 thousands
neuron
Document file for browse~10 million
Documentron
Concepts Unit~100 thousands
neuron
Large scale system which includes over 10 million documents and concepts for each neuron.
Research Result
Display the hit document by
rounding up concept units such as
1
Gene3
Gene5
Keyword Search by user
keywords(Diabetes)
Conditions(chromosomal region)
hit
hit
1
Gene4
Momentary inference !
↑All calculations take within 1 second→Realization of high‐speed web research
ontology class, candidate gene, etc.
Inference layer
(attractor)
Concept layer(statistical test)
Expression layer
(Document summary )
Input layer(text search)
Out put Layer(web Service)
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New high speed technology calculates tens of thousands of spreadsheets
hit
The technology, creating 3-dimension spreadsheets for every gene (X=1,2,,,several tens of thousands of )
at once.
Including Excluding
Documentation files including literatures
(~10 millions)
Gene name , concept units, etc.
(~100 thousands)
(Calculating signal processing among neurons on brain-type database)
GeneX
hit
hit
gsearch word
gsearch word
Include
Gene X“A “units of Documents
“B “units of Documents
Exclude Gene X
“C “units of Documents
“D “units of Documents
Classifications by types of document database
Keyword search by users.
↑based on this table, statistical inferences such as calculation of statistic, Fisher’s exact test, can be done.
各種概念とキーワードとの関連性を網羅的かつ、高速・統計的に評価できる
Correspondence relationship which curators associated
genes with documents.
(Medline, OMIM, PPI,Bio resources 等)
44↑All calculations within 1 second→Realization of high‐speed web research55
Ranking results of candidate genes↓
Keyword : type 2 diabetesGenome Condition: gene of no.6 chromosome
Searching for candidate genes by positional cloning.
Conditions of compound retrieval
Search example: Inference of causative gene by positional cloning.
Interval
Rough mapping based on crossing experiment
(~10Mbp wide interval)
Too many genes
Inference of candidate gene
Identification of causative gene variation
So far contributed to 65 achievements of searching researches on disease-causing genes.
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Semantic Table
Part 5
Bioinformatics And Systems Engineering Division, RIKEN,
Japan5757
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Introduction
SemanticTable.org
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About SemanticTable.org• A "Table" is the most common type of data format used in biological research. The Semantic Web, on the other hand, where knowledge is represented by statements comprising a subject, predicate and object, is also becoming a major container for data accessible by automatic computational reasoners; i.e. computers.
Here we present "SemanticTable.org", a data-mart server providing a huge amount of biological semantic-
About Semantic Table
Here we present SemanticTable.org , a data mart server providing a huge amount of biological semanticweb data as simple table-style data files that are reversely convertible to the semantic-web format.The SemanticTable.org server contains 644 tables and 7.5 million rows of SemanticTable data as of December 2010.
Conversion from table data files to semantic-web data files and vice versa are supported by our web site (http://semantictable.org) and the World Wide Web Consortium (W3C) web site RDFa Distiller and Parser page at (http://www.w3.org/2007/08/pyRdfa/).
• We propose SemanticTable, based on the standard RDFa specification, as the most understandable way for biologists not familiar with semantic-web specifications to utilize semantic-web data.
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Introduction>> Biological data formats used in life science research .
• Biologists like to use the table format for their research results
• Since most biological research results are essentially structured kinds of data, however, it is appropriate to represent these research results as Semantic Web graphs consisting of subject-property-object triples.
>> Characteristics of table data and semantic data
Introduction
>> Characteristics of table data and semantic data .
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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What is the Semantic Table• SemanticTable is a data container with advantages of both tables and Semantic Web graphs:
1. It provides a table view for biological data convertible to TSV format (tab-separated-value) text table data popular among biologists for computer processing..
2 It is convertible to explicit Semantic Web graph such as RDF that a computer can read and
What is the Semantic Table?
2. It is convertible to explicit Semantic Web graph such as RDF that a computer can read and process intelligently.
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Explicit Correspondence Semantic Table and Semantic Web Graph
This table presents implicitly the following semantic meanings, which may be understood by people, but not by computers
Yellow row - Table Header
Correspondence between Semantic Table and Semantic Web Graph
Yellow row Table Header(Class or property name of data)
Blue Row - Table Data
Green Column - Subjects
Red column - Objects
- The first row means:“The Person named Michael is 23 years of age and his gender is Male”
- The second row means: “ The Person named Jennifer is 27 years of age and her gender is Female”
For Intelligent processing by a computer, the table data needs to be presented with explicit semantic data as follows in computer- readable Semantic Web graph format.
62Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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Semantic Table MergingSemanticTable SemanticTable
Merging Semantic Tables
Using Semantic data could allow automatic drag and drop field
rearrangement by a program or browser running on a PC.
SemanticTable Merge Program Features:
RDF RDF
・Merging Multiple Class Semantic Tables・Subject Column Swapping・Dumping to Text
SemanticTable
join
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Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
Join the SemanticTable Consortium
C ll f d l t j i th ti t t d th h• Call for developers to join the consortium to create and author such programs; the fundamental semantic groundwork is ready to use.
• Open collaboration for the standard will jumpstart the new platform.
• Open source programs for SemanticTable run on cloud-computing technology, as do the programs for spreadsheet tables.
• We hope the SemanticTable format will be distributed broadly and freely for general use, as well as for scientific use.
Bioinformatics And Systems Engineering (BASE) division, RIKEN Yokohama Institute of Chemical and Physical Science
www.base.riken.jp/english
semantictable@base.riken.jp 64
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End
Thank you very much!
Bioinformatics And Systems Engineering Division, RIKEN,
Japan6565
Copyright © Tetsuro Toyoda, March 4, 2011. Presentation at Semantic Web Conference 2011.
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