The document outlines the content of a lecture on modern biotechnology. It discusses DNA as the genetic material and how genes are passed from parents to offspring in prokaryotic and eukaryotic systems. It also describes how modern biotechnology uses techniques like gene cloning and genetic engineering to develop genetically modified organisms (GMOs) by inserting foreign genes. Specific examples covered include the development of Golden Rice to address vitamin A deficiency and the use of GMOs in health, industry, food, and the environment.
Computational approaches to study GeneticsArithmer Inc.
Slide for Arithmer Seminar given by Dr. Jeffrey Fawcett (RIKEN) at Arithmer inc.
The topic is how data science is used in genetics, especially in analyzing thoroughbred gene pool.
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Deleterious alleles have played an important role in the evolution of maize and teosinte. Although they vary in their strength and effect across populations or environments, such mutations have played a role in local adaptation in teosinte, the accumulation of load during domestication and dispersal of maize, local adaptation of maize landraces, and ultimately in hybrid vigor for agronomic traits in breeding programs.
Adaptation in plant genomes: bigger is differentjrossibarra
Here we have proposed the functional space hypothesis, positing that mutational target size scales with genome size, impacting the number, source, and genomic location of beneficial mutations that contribute to adaptation. Though motivated by preliminary evidence, mostly from Arabidopsis and maize, more data are needed before any rigorous assessment of the hypothesis can be made. If correct, the functional space hypothesis suggests that we should expect plants with large genomes to exhibit more functional mutations outside of genes, more regulatory variation, and likely less signal of strong selective sweeps reducing diversity. These differences have implications for how we study the evolution and development of plant genomes, from where we should look for signals of adaptation to what patterns we expect adaptation to leave in genetic diversity or gene expression data. While flowering plant genomes vary across more than three orders of magnitude in size, most studies of both functional and evolutionary genomics have focused on species at the extreme small edge of this scale. Our hypothesis predicts that methods and results from these small genomes may not replicate well as we begin to explore large plant genomes. Finally, while we have focused here on evidence from plant genomes, we see no a priori reason why similar arguments might not hold in other taxa as well.
Computational approaches to study GeneticsArithmer Inc.
Slide for Arithmer Seminar given by Dr. Jeffrey Fawcett (RIKEN) at Arithmer inc.
The topic is how data science is used in genetics, especially in analyzing thoroughbred gene pool.
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Deleterious alleles have played an important role in the evolution of maize and teosinte. Although they vary in their strength and effect across populations or environments, such mutations have played a role in local adaptation in teosinte, the accumulation of load during domestication and dispersal of maize, local adaptation of maize landraces, and ultimately in hybrid vigor for agronomic traits in breeding programs.
Adaptation in plant genomes: bigger is differentjrossibarra
Here we have proposed the functional space hypothesis, positing that mutational target size scales with genome size, impacting the number, source, and genomic location of beneficial mutations that contribute to adaptation. Though motivated by preliminary evidence, mostly from Arabidopsis and maize, more data are needed before any rigorous assessment of the hypothesis can be made. If correct, the functional space hypothesis suggests that we should expect plants with large genomes to exhibit more functional mutations outside of genes, more regulatory variation, and likely less signal of strong selective sweeps reducing diversity. These differences have implications for how we study the evolution and development of plant genomes, from where we should look for signals of adaptation to what patterns we expect adaptation to leave in genetic diversity or gene expression data. While flowering plant genomes vary across more than three orders of magnitude in size, most studies of both functional and evolutionary genomics have focused on species at the extreme small edge of this scale. Our hypothesis predicts that methods and results from these small genomes may not replicate well as we begin to explore large plant genomes. Finally, while we have focused here on evidence from plant genomes, we see no a priori reason why similar arguments might not hold in other taxa as well.
Genome projects and their ContributionsAlbertPaul18
This is a presentation about different Genome projects like Rice genome project, Maize genome project, Wheat Genome project and Human genome project. It highlights how they were conducted and what the science community gained by conducting them. A side about the future challenges of such genome projects is also added.
Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea maysjrossibarra
While the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes.
Genomic selection changing Breeding programe around the world, talk consist of concept of Breeding, breeding value, Genomic breeding value, Genotype imputation, male calf procurement on basis of GEBV under SAG PT Project and 1000 bull genome project.
The present study was conducted with the aim of reducing the cost of implementing Genomic Selection(GS) by using Genotype imputation methodology in Gir cattle. Application of GS mainly depends upon the cost of genotyping and reduce its cost, imputation approaches have been used. Imputation strategies and GS have been comprehensively studied in several taurine dairy cattle populations but very limited information is available on indigenous populations. Factors that affect the efficiency of imputation and GS are population structure, linkage disequilibrium between markers and differing marker density between indigenous and taurine breeds. The objective of the study was to evaluate the performance of INDUSCHIP-1, a customized Illumina bovine microarray chip for indigenous cattle breeds, designed by National Dairy Development Board, Anand and design one (7-15K) LD panel, and evaluate the performance of two panels of INDUSCHIP-1, and a 13K subset of the same for its imputation accuracy to HD (777K or INDUSCHIP-1 level). Thus, the study was planned with the aim to design LD panel for genotype imputation to INDUSCHIP-1 level with the strategy to maximize the accuracy of imputation in Gir cattle.
Practical application of advanced molecular techniques in the improvement of ...ILRI
Presented by Tadelle Dessie, Mengistie Taye, Adebabay Kebede, Kefena Effa, Zewdu Edea and Wondmeneh Esatu at the 27 Annual Conference of the Ethiopian Society of Animal Production (ESAP), EIAR, Addis Ababa, 29–31 August 2019
Potential for genomic selection in indigenous cattle breeds and results of GWAS in Gir dairy cattle of Gujrat by Dr.Pravin Kandhani and Dr. Vijay Trivedi KAMDHENU UNIVERSITY GANDHINAGAR
Genome projects and their ContributionsAlbertPaul18
This is a presentation about different Genome projects like Rice genome project, Maize genome project, Wheat Genome project and Human genome project. It highlights how they were conducted and what the science community gained by conducting them. A side about the future challenges of such genome projects is also added.
Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea maysjrossibarra
While the vast majority of genome size variation in plants is due to differences in repetitive sequence, we know little about how selection acts on repeat content in natural populations. Here we investigate parallel changes in intraspecific genome size and repeat content of domesticated maize (Zea mays) landraces and their wild relative teosinte across altitudinal gradients in Mesoamerica and South America. We combine genotyping, low coverage whole-genome sequence data, and flow cytometry to test for evidence of selection on genome size and individual repeat abundance. We find that population structure alone cannot explain the observed variation, implying that clinal patterns of genome size are maintained by natural selection. Our modeling additionally provides evidence of selection on individual heterochromatic knob repeats, likely due to their large individual contribution to genome size. To better understand the phenotypes driving selection on genome size, we conducted a growth chamber experiment using a population of highland teosinte exhibiting extensive variation in genome size. We find weak support for a positive correlation between genome size and cell size, but stronger support for a negative correlation between genome size and the rate of cell production. Reanalyzing published data of cell counts in maize shoot apical meristems, we then identify a negative correlation between cell production rate and flowering time. Together, our data suggest a model in which variation in genome size is driven by natural selection on flowering time across altitudinal clines, connecting intraspecific variation in repetitive sequence to important differences in adaptive phenotypes.
Genomic selection changing Breeding programe around the world, talk consist of concept of Breeding, breeding value, Genomic breeding value, Genotype imputation, male calf procurement on basis of GEBV under SAG PT Project and 1000 bull genome project.
The present study was conducted with the aim of reducing the cost of implementing Genomic Selection(GS) by using Genotype imputation methodology in Gir cattle. Application of GS mainly depends upon the cost of genotyping and reduce its cost, imputation approaches have been used. Imputation strategies and GS have been comprehensively studied in several taurine dairy cattle populations but very limited information is available on indigenous populations. Factors that affect the efficiency of imputation and GS are population structure, linkage disequilibrium between markers and differing marker density between indigenous and taurine breeds. The objective of the study was to evaluate the performance of INDUSCHIP-1, a customized Illumina bovine microarray chip for indigenous cattle breeds, designed by National Dairy Development Board, Anand and design one (7-15K) LD panel, and evaluate the performance of two panels of INDUSCHIP-1, and a 13K subset of the same for its imputation accuracy to HD (777K or INDUSCHIP-1 level). Thus, the study was planned with the aim to design LD panel for genotype imputation to INDUSCHIP-1 level with the strategy to maximize the accuracy of imputation in Gir cattle.
Practical application of advanced molecular techniques in the improvement of ...ILRI
Presented by Tadelle Dessie, Mengistie Taye, Adebabay Kebede, Kefena Effa, Zewdu Edea and Wondmeneh Esatu at the 27 Annual Conference of the Ethiopian Society of Animal Production (ESAP), EIAR, Addis Ababa, 29–31 August 2019
Potential for genomic selection in indigenous cattle breeds and results of GWAS in Gir dairy cattle of Gujrat by Dr.Pravin Kandhani and Dr. Vijay Trivedi KAMDHENU UNIVERSITY GANDHINAGAR
How GMO Technology Compares to Other Crop Improvement TechniquesUniversity of Florida
Kevin M. Folta presents to the Western Barley Growers Association the risks and benefits of GM crops, and compares the techniques used to other means of crop genetic improvement.
It describes a modern day methodology that is frequently used to produce novel products and improve the quality of products effectively. This PPT includes only a few of the methods that have been discovered. Kindly inform if any corrections or inclusions are needed. And yes, suggestions are always heartily welcome.
Mining Phenotypes: How to set up a reverse genetics experiment with an Arabid...adcobb
In this lesson, students will mine data from Araport.org to design and propose a reverse genetics experiment using a known Arabidopsis mutant. They will select a treatment to reveal phenotypic dfifferences between wild type and mutant Arabidopsis. Student handout and teacher resources are available at www.Araport.org, teacher resources. Suitable for grades 9-12 or first year undergraduate students.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
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• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
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My slides at Nordic Testing Days 6.6.2024
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- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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2. LECTURE
CONTENT:
PART
1
Ê DNA:
the
genetic
material
(ONE
GENE-‐ONE
PROTEIN)
Ê Prokaryotic
system
Ê Eukaryotic
system
Ê INHERITANCE:
the
information
connection,
how
genes
are
passed
and
their
consequence
Ê Prokaryotic
system
Ê Eukaryotic
system
3. LECTURE
CONTENT:
PART
2
Ê MODERN
BIOTECHNOLOGY
Ê The
GMO
concept
Ê How
to
design/develop
a
GMO
Ê Tool/Methods:
Gene
Cloning,
Genetic
Engineering,
Recombinant
DNA
technology
Ê Equipment
and
Facilities
for
GMO
development
Ê Skills
and
Credentials
4. LECTURE
CONTENT:
PART
3
Ê MODERN
BIOTECHNOLOGY
:
advances
in
gene-‐based
applications
Ê The
GMO
in
Health
and
Medicine
Ê The
GMO
in
Industry
Ê The
GMO
in
Food
and
Agriculture
Ê The
GMO
in
Environment
5. TARGET
ACTIVITIES
Ê WHO
KILLED
THE
VICTIM?
And
WHO’S
YOUR
PAPA?
(An
activity
on
Forensics)
Ê EXPOSURE
TRIP
(July
24,
2013)
Ê BIOTECH,
UPLB
Ê IRRI
Ê IPB
Ê PHIL
CARABAO
CENTER
12. DNA:
THE
GENETIC
MATERIAL
Ê WHY
DNA?
Ê The
Griffith
Experiment
Ê Concept
of
a
transforming
principle
Ê The
Avery,
McLeod
and
McCarty
Experiment
Ê That
DNA
is
the
transforming
principle
Ê The
Hershey
and
Chase
Experiment
Ê Transduction
(via
phage)
16. SO
HOW
IS
DNA,
THE
GENETIC
MATERIAL,
USED
FOR
BIOTECHNOLOGY?
17. THE
SHIFT:
TRADITIONAL
TO
MODERN
Ê Nature’s
variation
Ê Color,
Size
Ê Traditional
breeding
can
give
us
additional
colors
and
sizes
Ê Modern
Biotechnology
can
give
you
MORE…
Ê Resistance
to
environmental
stress
Ê Taste
Ê Nutrition
kendralindell.wordpress.com
18. WHAT
WE
CAN
DO
WITH
BIOTECHNOLOGY…
nakedmaninthetree.wordpress.com
www.ent.iastate.edu
www.flickr.com
www.ishs.org
19. GROUP
WORK…
Ê One
GMO
per
group
Ê Research
on:
Ê What
problem
want
to
be
addressed
by
the
GMO
Ê How
was
it
developed
Ê What
were
the
problems
encountered
Ê How
were
they
resolved
(problems)?
Ê ISSUES….
Ê Open
Discussion
next
meeting…
20. LAST
MEETING…
DNA
and
its
importance
in
BIOTECHNOLOGY
1.
Golden
Rice;
2.
BT
Corn;
3.
Edible
Vaccine
in
Potato;
4.
GMO
Soya;
5.
Recombinant
Hepatitis
B
vaccine
21. RECALL…IN
MODERN
BIOTECHNOLOGY…
WE
CAN
CONTROL
THE
VARIATION
Ê NOTE:
Ê Allelic
differences
at
genes
control
a
specific
trait
Ê GENE:
a
piece
of
DNA
that
controls
the
expression
of
a
trait
Ê ALLELE:
alterative
form
of
the
gene
(they
can
segregate
to
form
the
variation
naturally)
22. DNA
(gene)
RNA
Protein
Trait
(or
phenotype)
Transcription
Translation
Plant
height
Seed
shape
Traditional
Breeding
Biotechnology
a. Gene
manipulation
b. Gene
Introduction
23. Ê GMO
or
Genetically-‐Modified
Organisms
Ê Organisms
with
ARTIFICIALLY-‐ALTERED
DNA
Ê Foreign
gene
is
inserted
from
another
species:
(enables
GMO
to
express
the
trait
coded
by
the
new
gene)
=
TRANSGENICS
Ê An
existing
gene
is
altered:
to
make
it
express
at
a
higher
level
or
in
a
different
way
=
FOR
GENE
THERAPY
Ê Gene
is
deleted
or
deactivated:
to
prevent
the
expression
of
a
trait
(e.g.
delayed
ripening)
24.
25. THE
GOLDEN
RICE
Ê WHY:
to
address
Vitamin
A
deficiency
problem
Ê Linked
to
blindness
Ê Influences
Diarrhea
and
Measles
Ê >100M
children
affected
Ê Vitamin
Delivery
as
PILLS
=
most
countries
no
access
Ê Alternative
=
vitamin
into
CROPS
www.goldenrice.org
26. β-‐Carotene
Pathway
in
Plants
IPP
Geranylgeranyl
diphosphate
Phytoene
Lycopene
β
-‐carotene
(vitamin
A
precursor)
Phytoene
synthase
Phytoene
desaturase
Lycopene-‐beta-‐cyclase
ξ-‐carotene
desaturase
Problem:
Rice
lacks
these
enzymes
Normal
Vitamin
A
“Deficient”
Rice
(Isopentenyl
diphosphate)
27. The
Golden
Rice
Solution
IPP
Geranylgeranyl
diphosphate
Phytoene
Lycopene
β
-‐carotene
(vitamin
A
precursor)
Phytoene
synthase
Phytoene
desaturase
Lycopene-‐beta-‐cyclase
ξ-‐carotene
desaturase
Daffodil
gene
Single
bacterial
gene;
performs
both
functions
Daffodil
gene
β-‐Carotene
Pathway
Genes
Added
Vitamin
A
Pathway
is
complete
and
functional
Golden
Rice
28. THOUGH
METABOLIC
PATHWAYS
ARE
COMPLEX……THEY
ARE
INTERRELATED
Understanding
pathways
is
critical
to
developing
new
products
29. Modifying
Pathway
Components
Can
Produce
New
Products
Modified
Lipids
=
New
Industrial
Oils
Turn
On
Vitamin
Genes
=
Relieve
Deficiency
Increase
amino
acids
=
Improved
Nutrition
30. DEVELOPING
OUR
GMOs:
Tools
that
you
can
USE
Ê Genetic
Manipulation
or
Genetic
Engineering
Ê GOAL:
introduce,
alter,
delete
Ê Cloning
and
Cell
Culture
Ê What
to
manipulate:
Genes,
Cells,
Tissue
Ê GENOMICS,
PROTEOMICS,
METABOLOMICS
37. THE
PRINCIPLE
TO
ISOLATE
THE
GENE:
Complementary
Genetics
1.
Protein
sequence
is
related
to
gene
sequence
NH3
+-Met-Asp-Gly--------------Trp-Ser-Lys-COO-
ATG GAT-GCT TGG-AGT-AAA
C C C G
A TCT
G C
A
G
2.
The
genetic
code
information
is
used
to
design
PCR
primers
Forward
primer:
5ʼ’-‐ATGGAT/CGCN-‐3ʼ’
Reverse
primer:
5ʼ’-‐T/CTTNC/GT/ACCA-‐3ʼ’
Notes:
T/C
=
a
mixture
of
T
and
C
at
this
position;
N
=
a
mixture
of
all
four
nucleotides
Reverse
primer
is
the
reverse
complement
of
the
gene
sequence
39. 3.
Use
PCR
to
amplify
gene
fragment
a.
template
DNA
is
melted
(94C)
3ʼ’
5ʼ’
5ʼ’
3ʼ’
3ʼ’
5ʼ’
5ʼ’
3ʼ’
b.
primers
anneal
to
complementary
site
in
melted
DNA
(55C)
3ʼ’
5ʼ’
5ʼ’
3ʼ’
3ʼ’
5ʼ’
5ʼ’
3ʼ’
c.
two
copies
of
the
template
DNA
made
(72C)
41. Human
clone
library
Clones
transferred
to
filter
PCR
fragment
probe
added
to
filter
Hot-‐spots
are
human
gene
of
interest
4.
Gene
fragment
used
to
screen
library
42. HOW
WERE
YOUR
GENES
ISOLATED?
Ê Golden
Rice
Ê BT
Corn
Ê Edible
Vaccine
in
Potato
Ê GMO
Soya
Ê Recombinant
Hepatitis
B
vaccine