cpgr in the context of the nsi - h3africa.orginformatics request consul tation study ... sample prep...
TRANSCRIPT
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CPGR in the context of the NSI
CPGR
HCD
Omics core
Collab.
Commercial
Universities
• Universities
• State-owned
• Companies
Universities
Universities
Industry
Universities
Industry
Industry
Biotech
innovation
system /
Bio-
economy
National System of Innovation (NSI) / National
economy
Output
Data, reports, new ideas
Outcomes
Knowledge, publications,
IP, human resources, new
projects
Impact
New products & services,
funding, investment, in-
licensing
EnvironmentEnvironment ValueValue
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CPGR core competence areas
Bioinformatics
Cell culture
assaysRecombinant
protein expression
Genomics ProteomicsTranscriptomics
Systems Biology
Validation
Translation
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Services & projects management
Bio-consulting
Bio-analysis
Bio-Informatics
RequestConsul
tation
Study plan
Service / Project
ReportData
analysis
Data-mining
Bioinformatics
process chain
Analytical
process chain
Key CPGR
processes
Level 1 Level 2 Level 3Increasing levels
of involvement
Design inputs
Raw data curation
Primary analysis
ReportingSecondary
analysisTertiary analysis
• Support systems environment
• Data storage and data management
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Data management (LIMS), data analysis (Bioinformatics), data storage
Sample prep (liquid handler), sample storage (freezers), centrifuge, incubator
Adequate space / environment
Available at CPGR
Implementation to be completed by end of 2013
Available through 3rd parties
Dedicated funding applications in process of preparation
Services & projects management
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‘omics’ network orchestration
Orchestrator
(CPGR)
• Pool of resources
• Pool of knowledge
• Common
procedures
• Enhanced capacity
• Virtual research
department
• Create IP supply
• Better absorb demand-
driven research
New/additional roles
• Customer relations
• Marketing (channel mgmt)
• Project mgmt
• Quality mgmt
• Learning
• Knowledge mgmt
Autonomous partners
• CTLS (Core Technologies for Life Sciences)
• ABRF (Association of Biological Resource Facilities)
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Statistics BioinformaticsData
management
IPKnowledge
transferFinancing
Basic & applied
researchEducation Ethics
Biological assays
Bio-AnalyticsSample
management
Project mgmt framework
DOMAIN Core functions
DIGITAL
Funding & IP
SCIENCE
WET LAB
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Complexity and cost of Genomics projects
Sboner et al. Genome Biology 2011, 12:125
http://genomebiology.com/2011/12/8/125
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1 PhD Student
Phase 1:
Sequencing
Phase 2:
Basic Analysis
Phase 3:
Advanced Analysis
• miSeq Platform
• mRNA – 18 samples
• miRNA – 6 samples
• Duration 3-4 months
• Output – raw data• Dedicated Bioinformatician
• Student Training
• Duration 3 months
• Output – basic data analysis as per
specifications detailed in attached
analysis description; Student
trained in basic analysis• Dedicated Bioinformatician
• Student Training
• Duration 3 months
• Output – advanced data analysis as per
specifications detailed in attached
analysis description; Student trained in
advanced analysis
8-12 months
OUTPUT VALUE:
• High Quality Data
• Highly skilled Student –
increased capacity
Achievement of Research
Goals
Project example: Integration of data generation & capacity development
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Announcements
• GeneTitan / Axiom training - before end of 2013
• Affymetrix – clinical applications (Cytoscan, Oncoscan, DMET Plus) – Nov 2013
• Affymetrix GeneTitan – custom array design and ht genomic applications – Nov 2013
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www.cpgr.org.za
www.cpgr.org.za/blogspot/
www.dcyphr.org.za
http://za.linkedin.com/in/reinhardhiller