Apollo is a web-based application that supports and enables collaborative genome curation in real time, allowing teams of curators to improve on existing automated gene models through an intuitive interface. Apollo allows researchers to break down large amounts of data into manageable portions to mobilize groups of researchers with shared interests.
Ontologies for life sciences: examples from the gene ontologyMelanie Courtot
A half day course presented during the Earlham Institute summer school on bioinformatics 2016, in Norwich, UK, http://www.earlham.ac.uk/earlham-institute-summer-school-bioinformatics
Biotechnology Industry has changed a lot during last decade , which means moving ahead from traditional ways to more advanced technological developments
2012 Biotechnology Industry is not the same as it was in 2001
Apollo is a web-based application that supports and enables collaborative genome curation in real time, allowing teams of curators to improve on existing automated gene models through an intuitive interface. Apollo allows researchers to break down large amounts of data into manageable portions to mobilize groups of researchers with shared interests.
Ontologies for life sciences: examples from the gene ontologyMelanie Courtot
A half day course presented during the Earlham Institute summer school on bioinformatics 2016, in Norwich, UK, http://www.earlham.ac.uk/earlham-institute-summer-school-bioinformatics
Biotechnology Industry has changed a lot during last decade , which means moving ahead from traditional ways to more advanced technological developments
2012 Biotechnology Industry is not the same as it was in 2001
Event: Plant and Animal Genomes conference 2012
Speaker: Rachael Huntley
The Gene Ontology (GO) is a well-established, structured vocabulary used in the functional annotation of gene products. GO terms are used to replace the multiple nomenclatures used by scientific databases that can hamper data integration. Currently, GO consists of more than 35,000 terms describing the molecular function, biological process and subcellular location of a gene product in a generic cell. The UniProt-Gene Ontology Annotation (UniProt-GOA) database1 provides high-quality manual and electronic GO annotations to proteins within UniProt. By annotating well-studied proteins with GO terms and transferring this knowledge to less well-studied and novel proteins that are highly similar, we offer a valuable contribution to the understanding of all proteomes. UniProt-GOA provides annotated entries for over 387,000 species and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. Annotation files for various proteomes are released each month, including human, mouse, rat, zebrafish, cow, chicken, dog, pig, Arabidopsis and Dictyostelium, as well as a file for the multiple species within UniProt. The UniProt-GOA dataset can be queried through our user-friendly QuickGO browser2 or downloaded in a parsable format via the EBI3 and GO Consortium FTP4 sites. The UniProt-GOA dataset has increasingly been integrated into tools that aid in the analysis of large datasets resulting from high-throughput experiments thus assisting researchers in biological interpretation of their results. The annotations produced by UniProt-GOA are additionally cross-referenced in databases such as Ensembl and NCBI Entrez Gene.
1 http://www.ebi.ac.uk/GOA
2 http://www.ebi.ac.uk/QuickGO
3 ftp://ftp.ebi.ac.uk/pub/databases/GO/goa
4 ftp://ftp.geneontology.org/pub/go/gene-associations
Pressure BioSciences, Inc. Launches the Barocycler HUB440 A State-of-the-Art, High Pressure Generator (up to 56K psi) for Multiple Bioscience Applications
Find out more at: www.pressurebiosciences.com
Apollo is a web-based application that supports and enables collaborative genome curation in real time, allowing teams of curators to improve on existing automated gene models through an intuitive interface. Apollo allows researchers to break down large amounts of data into manageable portions to mobilize groups of researchers with shared interests.
An introduction on gene annotation & curation for the IAGC and BIPAA research communities.
Research Frontier: Cognitive Performance GenomicsMelanie Swan
Research Frontier: Cognitive Performance Genomics
New category in personal genomics research
Working with the brain: virtually all cognitive performance and mental health issues are a question of awareness of state or behavior
Talk from OHBM education day 2018, an overview of data sharing and other resources for neuroimaging research. Also a brief discussion of the impact that openly shared data has had on publications.
Event: Plant and Animal Genomes conference 2012
Speaker: Rachael Huntley
The Gene Ontology (GO) is a well-established, structured vocabulary used in the functional annotation of gene products. GO terms are used to replace the multiple nomenclatures used by scientific databases that can hamper data integration. Currently, GO consists of more than 35,000 terms describing the molecular function, biological process and subcellular location of a gene product in a generic cell. The UniProt-Gene Ontology Annotation (UniProt-GOA) database1 provides high-quality manual and electronic GO annotations to proteins within UniProt. By annotating well-studied proteins with GO terms and transferring this knowledge to less well-studied and novel proteins that are highly similar, we offer a valuable contribution to the understanding of all proteomes. UniProt-GOA provides annotated entries for over 387,000 species and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. Annotation files for various proteomes are released each month, including human, mouse, rat, zebrafish, cow, chicken, dog, pig, Arabidopsis and Dictyostelium, as well as a file for the multiple species within UniProt. The UniProt-GOA dataset can be queried through our user-friendly QuickGO browser2 or downloaded in a parsable format via the EBI3 and GO Consortium FTP4 sites. The UniProt-GOA dataset has increasingly been integrated into tools that aid in the analysis of large datasets resulting from high-throughput experiments thus assisting researchers in biological interpretation of their results. The annotations produced by UniProt-GOA are additionally cross-referenced in databases such as Ensembl and NCBI Entrez Gene.
1 http://www.ebi.ac.uk/GOA
2 http://www.ebi.ac.uk/QuickGO
3 ftp://ftp.ebi.ac.uk/pub/databases/GO/goa
4 ftp://ftp.geneontology.org/pub/go/gene-associations
Pressure BioSciences, Inc. Launches the Barocycler HUB440 A State-of-the-Art, High Pressure Generator (up to 56K psi) for Multiple Bioscience Applications
Find out more at: www.pressurebiosciences.com
Apollo is a web-based application that supports and enables collaborative genome curation in real time, allowing teams of curators to improve on existing automated gene models through an intuitive interface. Apollo allows researchers to break down large amounts of data into manageable portions to mobilize groups of researchers with shared interests.
An introduction on gene annotation & curation for the IAGC and BIPAA research communities.
Research Frontier: Cognitive Performance GenomicsMelanie Swan
Research Frontier: Cognitive Performance Genomics
New category in personal genomics research
Working with the brain: virtually all cognitive performance and mental health issues are a question of awareness of state or behavior
Talk from OHBM education day 2018, an overview of data sharing and other resources for neuroimaging research. Also a brief discussion of the impact that openly shared data has had on publications.
Bioinformatics Introduction and Use of BLAST ToolJesminBinti
Hi, I am Jesmin, studying MCSE. I think this file will help you if you want to know the basic information about Bioinformatics and the use of BLAST tool. The BLAST tool is the tool that matches the sequences of DNA,RNA and proteins.
Data analysis & integration challenges in genomicsmikaelhuss
Presentation given at the Genomics Today and Tomorrow event in Uppsala, Sweden, 19 March 2015. (http://connectuppsala.se/events/genomics-today-and-tomorrow/) Topics include APIs, "querying by data set", machine learning.
This presentation explains the meaning of curation and includes an introduction to the Apollo genome annotation editing tool and its curation environment.
Tales from BioLand - Engineering Challenges in the World of Life SciencesStefano Di Carlo
Prof. Alfredo Benso from SysBio Group @ Politecnico di Torino keynote presentation at ICIIBMS - IEEE International Conference on Intelligent Informatics and BioMedical Sciences, on Nov 26 2017 in Okinawa (Japan).
Similar to Synthetic Biology and Data-Driven Synthetic Biology for Personalized Medicine and Clean Energy at O\'Reilly\'s OSCON Data in Portland, OR (20)
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Cheryl Hung, ochery.com
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
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Synthetic Biology and Data-Driven Synthetic Biology for Personalized Medicine and Clean Energy at O\'Reilly\'s OSCON Data in Portland, OR
1. SYNTHETIC BIOLOGY AND
DATA-DRIVEN SYNTHETIC
D
BIOLOGY FOR PERSONALIZED
MEDICINE AND CLEAN
ENERGY
Russell Hanson
OSCON D t J l 25 27 2011
Data July 25-27,
Portland, OR
Russell Hanson
Dec 27, 2004 TheCureIsNow.org
2. Outline
• What is synthetic biology?
y gy
• How does synthetic biology relate to genomic
science/personalized medicine
• “Open source” tools in science and
bioengineering/synbio
• Bio-Computer aided design (AutoCAD, etc.)
• Mesoscopic Physics
p y
• Diff Eqs and parameter optimizations
OSCON Data – Portland, OR
3. Outline, cont.
• IP Issues in personalized medicine
• Pink Army and co-op, open-source biopharma
• Non-profit clinical research organizations
Non profit
(CRO’s)
• Disease-management companies, broken
Disease management
business model
• Big Data in electronic medical records,
personal genomics, biostatistics, hospitals,
community health centers
OSCON Data – Portland, OR
4. OSCON Data: This may not be a talk
about a social web app and the
pp
database technologies used for that
app, but truly, what would a social web,
pp, y, ,
etc. app be without the life or biology
on that social graph
g p
OSCON Data – Portland, OR
5. What is life?
What is artificial life?
And, incidentally, what is consciousness?
Igor Aleksander (1995) Bernard Baars (1988)
• Brain as state machine • Definition and context setting
• Inner neuron partitioning • Adaptation and learning
• Conscious and unconscious states • Editing
• Perceptual learning and memory • Flagging and debugging
• Prediction • Recruiting and control
• Self-Awareness • Decision-making (executive function)
• Representation and meaning • Analogy forming-function
• Learning utterances • Metacognitive and self monitoring
self-monitoring
• Learning language function
• Will • Autoprogramming and self-maintenance
• Instinct function
• Emotion • Definitional and context-setting
context setting
function
OSCON Data – Portland, OR
from “When The Turing Test Is Not Enough,” by George Dvorsky
6. We are open source
Life is open source
We should have excellent tools
to manage our source (code)
OSCON Data – Portland, OR
12. Single nucleotide polymorphism
(SNP) and gene expression arrays
•CCopy number profile
b fil
• Gene expression/over expression levels in
a tissue
• Major copy proportion
j py p p
• Machine learning on SNPs, SVM, Bayesian
networks, Markov chains
• Biomarker identification, verification, and
development.
development Biomarker for autism “lack
autism, lack
of empathy” OSCON Data – Portland, OR
13. Where are we?
• How much do we know about cellular
processes, regulation, oncogenes,
l ti
protein-protein interactions, microbiome
interactions?
i t ti ?
• How much do we know about brain
structure-function relationships, gene
activation in neurons, memory,
eletrophysiology, neuronal cognition,
brainmaps from C. elegans, to Mus
musculus, to Homo sapiens?
OSCON Data – Portland, OR
14. How to Access the Human Genome (and other
seque ced genomes)
sequenced ge o es)
• ftp://ftp.ncbi.nih.gov
hs_phs0.fna.gz Survey sequence (appr
hs_phs1.fna.gz Unordered contigs (ea
hs_phs2.fna.gz Ordered contigs (each
hs_phs3.fna.gz Finished sequence
21C3 – Berlin
15. “The second wave of synthetic
biology: from modules to systems
systems”
“The second wave of synthetic biology: from
y gy
modules to systems,” Purnick PEM, Weiss R,
Nature Reviews Molecular Cell Biology, Vol 10 OSCON Data – Portland, OR
Iss 6 P410-422 Jun 2009
16. Using increasing abstraction, performance
characteristics, datasheets -- like
integrated circuits (IC’s)
OSCON Data – Portland, OR
19. Nanobots to treat NK leukemia cells
DNA scaffolds and
cell-surface receptors
Software, etc. for DNA
origami, scaffold design
OSCON Data – Portland OR
20. 23andMe, NextGen sequencing and
SynBio get together
• 23andMe version 3 uses
Illumina
HumanOmniExpress
BeadChip SNP chips
($250/sample)
• Genome Wide Association
Studies
• --> At $250/sample,
genomic personalization is
very realizable OSCON DATA – Portland, OR
21. More new CAD/BioCAD software tools
• ‘Matter compiler’
• Thingiverse, cheap 3d printers, laser
g , p p ,
cutters, Shapeways, personal fab
technology, makerbot, etc.
gy, ,
• AutoDesk, EugeneCAD,
ClothoCAD
• RepRap – exponentially
scalable matter compiler
/
/extruding 3D p
g printer
OSCON Data – Portland, OR
22. • Movie at:
p // /
http://www.molecularmovies.com/movi
es/berry_apoptosis.html
Apoptosis Maya Animation by Drew Berry OSCON Data – Portland, OR
23. Biological “simulators”
simulators
• NAMD – and other molecular dynamics, etc.
• Mixed Quantum Mechanics/Molecular
Mechanics (QM/MM/…)
(QM/MM/ )
• Biological pathway/chemical reaction
simulators/optimizers
• Many projects on gene network analysis,
stochastic simulation, etc.
h i i l i
OSCON Data – Portland, OR
24. Design of bio/chemical devices from
(1) first principles and functional
fi t i i l df ti l
abstractions and (2) from data
Data
Bio/chem/phys first principles
and functional abstractions
OSCON DATA – Portland, OR
25. How and where these data are
used,
used or made usable
• Randomized clinical trials
• Biomarker development, genomic tests
in CLIA certified labs
• Licensing to other companies for
marketing and development
OSCON Data – Portland, OR
26. Tools that are used to analyze
these data
• R (!), BioConductor
• Biostatistics, Matlab
• Python
• Chi-square tests,
permutation tests, Monte Carlo resampling
• Parametric statistics, nonparametric
statistics, generative statistics
• Statistical learning theory, online learning
theory
OSCON Data – Portland, OR
27. Approval of processes for
cellular therapies/treatments
• FDA approval for process, not f
lf t for
drug/biologic/device
“…progress on the development of a new experimental MCL treatment called
immunotherapy, in which a patient’s own immune cells are collected from the
bloodstream before ASCT, treated in the laboratory to have antitumor activity,
and reintroduced into the body after ASCT. The goal of this immunotherapy is
to reduce the likelihood of relapse after transplantation. A major challenge of
immunotherapy is getting the transferred immune cells to persist in the body
after the transfer. In preclinical studies, Dr. Jensen and his colleagues found
ft th t f I li i l t di D J d hi ll f d
that using a certain subset of immune cells called central memory T-cells in-
creased the likelihood that the transferred T-cells will persist.”
Report from the Lymphoma Research Foundation, Spring 2010, Volume 8,
Number 1 OSCON Data – Portland, OR
28. Pink Army – a
biopharma co-op
bi h
• You own your own cells, therapies based off your
own cells should be yours in the co-op.
• If a biopharma company cures your disease, they
lose a customer, who is cured.
2 weeks and
$1000 in lab resources
OSCON Data – Portland OR
29. Cancer 5-year survival rates, US
OSCON Data – Portland, OR
Division of Cancer Control and Population Sciences, 2008
30. Non-profit clinical research
organizations ( (CRO’s)
’ )
• Randomized trials
• A melanoma trial with 1,000 patients
1 000
costs $60M in CRO expenses
• A 10,000 patient chronic obstructive
10 000
pulmonary disease (COPD) costs $240-
$250M in CRO expenses
OSCON Data – Portland, OR
31. More medical foci of SynBio
y
• Oncolytic viruses
y
• RNAi for HER2 in breast cancer (also an
iGEM team project)
• Designed, logic, and ‘programmed’
• Not comp tational devices b t use logic
computational de ices but se
and/or procedures
OSCON Data – Portland OR
32. Using data to engineer therapies
or cures for genetic diseases
IF ( RNA AND RNA AND RNA AND RNA AND RNA )
THEN
RELEASE PROTEIN INITIATING APOPTOSIS IN THE TUMOR CELL
OSCON Data – Portland OR
33. More biological nanobots
• T t h d l robots, made from biological or organic
Tetrahedral b t d f bi l i l i
substrate: 12Tet Tetrahedral Rover Robot
http://www.youtube.com/watch?v txZMLS7YD6Q
http://www.youtube.com/watch?v=txZMLS7YD6Q
OSCON Data – Portland, OR
35. Energy, biofules
Energy biofules, and bioreactors
‘The oil multinational, despised by green activists for its support of
, p yg f pp f
scientists skeptical of climate change, plans to invest $600 million (£370
million) in a joint venture with Mr Venter’s company, Synthetic Genomics. The
thinking is that algae is a more efficient source of fuel than conventional
biofuels, such as ethanol made from corn or sugar cane and biodiesel made
from wheat or palm oil. Algae can be processed into fuels similar to petrol
and diesel — and it consumes carbon dioxide as it grows.
According to Exxon, the yield of biofuel from algae is 2,000 gallons per
A di E h i ld f bi f l f l i 2 000 ll
acre, more than three times that of biodiesel from palm oil and eight times the
ethanol yield from corn. It is also believed to have the edge over other
biofuels because of its suitability for use as a jet fuel. “The real challenge to
fuel The
creating a viable, next-generation biofuel is the ability to produce it in large
volumes,” Mr Venter said.’
via http://www.timesonline.co.uk/tol/news/environment/article6710846.ece
OSCON Data – Portland OR
39. Microbiome Engineering
• Use the microbiome to produce therapies
for existing diseases in controlled,
continuous ways: insulin for diabetics,
interleukin 10 for Crohn’s disease, etc.
• Highly competitive environment: microbe
needs orthogonal edge to survive
g g
OSCON Data – Portland, OR
40. And so forth… and biology
• PolySilicon and biology
• SynBio in chemicals industry – cleaning
cleaning,
refinement, scalability, sensors, etc.
OSCON Data – Portland, OR
41. Conclusions
• Wide ranging implications to existing problems in
health, engineering, energy, manufacture
• P ti l l for human healthcare, genomic research
Particularly f h h lth i h
on diseases, data-aided design and testing
• New manufacture processes for new biologically-
biologically
designed constructs
http://www.linkedin.com/in/russellhanson
http://twitter.com/russell_hanson
http://www.russellhanson.com/
http://www russellhanson com/
OSCON Data – Portland OR