#35 - Functional Genomics
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Required Reading
BCB 444/544
(before lecture)
Mon Nov 12 - Lecture 34
Lecture 35
Comparative Genomics
• Chp 17
A bit more Comparative Genomics
Wed Nov 14 - Lecture 35
Functional Genomics
Functional Genomics
• Chp 18
(Microarrays)
Thurs Nov 15 - Lab 11
Microarray Analysis
Fri Nov 16 - Lecture 36
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Proteomics
• Chp 19
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Assignments & Announcements
Mon Nov 12 - HW#6
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Seminars this Week
BCB List of URLs for Seminars related to Bioinformatics:
(was finally posted on MON)
http://www.bcb.iastate.edu/seminars/index.html
HW#6 - Fun with SNPs, Comparative Genomics &
Gene Annotation!!
• Nov 12 Mon - Math Seminar 4:10 in 294 Carver
• Trachette Jackson Univ of Michigan
• Mathematical Modeling of Angiogenesis in Cancer
Due: whenever…
(sometime before 5 PM Mon Nov 26)
• Nov 14 Wed - ISU ADVANCE Brown Bag Lunch noon 240 Bessey
• Making a Career in STEM: Three Women's Stories
• Nov 15 Thurs - Center for Excellence in Arts & Humanities
Symposium 9:30-11:30 & 3-5 Cardinal Room, MU
• L Andrews,T Duster, J Murray & K Taussig
• Ethical, Philosophical, and Legal Issues of Genomic Research
• Nov 16 Fri - BCB Faculty Seminar 2:10 in 102 SciI
• Karin Dorman ISU
• Modeling HIV Recombination - Hotspots?
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In the News: Bioinformatics/Genomics
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Chp 17 – Comparative Genomics
1. REWARD: X Prize Foundation $10 Million
SECTION V
2. Science cover article this week:
Xiong: Chp 17 Genome Mapping, Assembly & Comparison
for sequencing 100 human genomes in 10 days
GENOMICS & PROTEOMICS
Capillary sequencing of tumor cell genomes
(65 of 4 million sequencing reactions are shown)
• Genome Mapping
compare all genes in specific tumor cell types
Result? lots of SNPs and other mutations
Surprise: many mutations in genes not normally
considered "oncogenes" or "tumor suppressors"
or "cell cycle" or "apoptosis"-related
3. DNA Computing -
•
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•
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Interesting papers provided by Erin
Genome Sequencing
Genome Sequence Assembly
Genome Annotation
Comparative Genomics
(see class website 2006 for additional links)
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Genomics -
Recent technologies?
for excellent overview lectures,
see these posted by NHGRI & Pevsner:
Pyro-Sequencing
http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/P/Pyrosequencing.html
1- Genomic sequencing
Mapping and Sequencing
Eric Green, NHGRI
CTGA2005Lecture1.pdf
2- Human genome project
The Human Genome 2005-10-19_ch17.pdf
Jonathan Pevsner, Kennedy Krieger Institute
3- SNPs
Studying Genetic Variation II: Computational Techniques
Jim Mullikin, NHGRI TGA2005Lecture13.pdf
4- Comparative Genomics
Comparative Sequence Analysis
Elliott Margulies, NHGRI CTGA2005Lecture8.pdf
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Massively Parallel Sequencing: 454
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Massively Parallel Sequencing: 454 at ISU?
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ENCODE - Results? June 2007
Genome Assembly at ISU?
Huang (ComS) & Chou (ComS/GDCB) - designed
assembly software used at Celera, TIGR, etc.
Aluru (ECprE) & Schnable (Agron/GDCB) - parallel
implementations of assembly software
Dickerson (ECprE), Wise (PlPath/USDA) - & many
others = ISU computational & experimental experts
with large scale genome assembly research focus
Kalyanaraman A, Emrich SJ, Schnable PS, Aluru S (2007) Assembling genomes
on large-scale parallel computers. Journal of Parallel and Distributed
Computing. in press
Emrich SJ, Kalyanaraman A, Aluru S (2005) Algorithms for large-scale
clustering and assembly of biological sequence data. Handbook of Computational
Molecular Biology, Chapman & Hall/CRC press.
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http://www.nature.com/nature/journal/v447/n7146/full/nature05874.html
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Speaking of Craig Venter - Where is the
“Cutting-Edge” in Sequencing Technology?
Was It Really Worth all that $$?
& Who Owns it Now???
http://www.jcvi.org/
http://www.i-sis.org.uk/humangenome.php
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Human Genome Project:
What have we learned?
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Lots of SNPs:
single nucleotide polymorphisms
20,000 - 25,000
2004
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J Pevsner 2005
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SNPs: Single Nucleotide Polymorphisms
SNPs: Single Nucleotide Polymorphisms
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J Mullikin 2005
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SNPs: Access via 3 Major Genome Browsers
SNP Discovery Methods
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& 454 Sequencing!
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J Mullikin 2005
Haplotype - What is it?
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Haplotypes: Two definitions
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Haplotypes: an example
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Haplotypes: a better explanation!
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http://www.hapmap.org/
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Hapmap Project
HapMap Project Goals
http://www.hapmap.org/
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J Mullikin 2005
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Why are SNPs/HapMap Important?
HapMap Results: http://www.hapmap.org/
(for humans?)
Many human traits & diseases are polygenic = determined
by multiple genes
QTL = Quantitative Trait Locus - genetic locus (gene)
that contributes to a polygenic trait & that can be
measure in some quantitative manner
Examples?
Obesity - (in pigs & humans!)
Intelligence
Schizophrenia
Alcoholism
So - understanding such traits requires understanding
"natural" variation at multiple loci - it is complex!
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Developing Effective Treatments Requires
Balance between Efficacy & Toxicity
An example from Pharmocogenomics
& these depend both on genetics and
environment
& tough Ethical Issues arise
CAT scans of a single patient over 2 years
Fig 4.10
Lung cancer drug Iressa cures only 10% of treated
patients - but it saves those!
Copyright © 2006
A. Malcolm Campbell
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Fig 4.15
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Continuum of Utility of a Particular Genetic Test
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Significance of SNP Analyses
Other implications? What is extent of
diatom genetic diversity in oceans - and
what effect might this have on global CO2
levels & global warming?
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J Mullikin 2005
Fig 4.1 Diatom Bloom Study
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Copyright © 2006
A. Malcolm Campbell
Light micrographs of two
Ditylum brightwellii cells
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Diatoms & Global Warming?
The Claim: "Give me half a tanker of iron & I'll give you an ice age"
Rationale: Iron is limiting in the ocean; give diatoms lots of it & cause
a diatom "bloom," this will increase CO2 fixation (lots removed from
atmosphere), resulting in decrease in global temperature
>> Global warming cured!
Test: Spring 2004 - 38 authors, international effort:
Iron-induced bloom lasted only 18 days
Much of sequestered carbon did not sink to deep ocean, but was
recycled through predation or decomposition by bacteria,
4- Comparative Genomics
Many thanks to:
Elliott Margulies, NHGRI
for the following slides extracted from his lecture on:
Comparative Sequence Analysis
CTGA2005Lecture8.pdf
which could actually lead to increase in atmospheric CO2 !!
Moral: Perhaps we should understand the dynamics of oceans before
conducting such experiments on a global scale!! We must be
cautious when devising solutions to complex problems such as
global warming!!
Copyright © 2006
A. Malcolm Campbell
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Comparative Genomics
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E Margulies 2005BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
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E Margulies 2005BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
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Comparative Genomics Provides Important
Clues re: Biological Function of Genes
Different Terms are used to Describe
Different types of Conserved Sequences
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Sequence Comparisons
Whole Genome Alignments!
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Two Major Visualization Tools:
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(LZW)
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zPicture: Best of Both Tools:
E Margulies 2005BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
Comparing Multiple Species with zPicture
http://zpicture.dcode.org/
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What Have We Learned from Comparative
Genomics? An early example:
The Comparative Genomics Company?
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What Have We Learned from Comparative
Genomics? A more recent example:
Re: Pollard KS, …Haussler D. (2006) An RNA gene expressed during cortical
development evolved rapidly in humans. Nature 443: 167-172. PDF
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GENOMICS & PROTEOMICS
ISU Resources & Experts
(a few of them)
Genomic sequencing, Genotyping, Comparative genomics
Facilities:
ISU Biotech DNA Facility
PSI Carver Co-Lab
Experiments:
Microbial: Minion, others
Plant: Schnable, Wise, Bogdonave, many others
Animal: Rothschild, Tuggle, Reecy, Lamont, many others
Assembly & Analysis:
Huang, Chou, Brendel, Proulx, Gu
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cell at a given time
High-throughput analysis of RNA expression:
• Sequence-based Approaches
• Microarray-based Approaches
• Comparison of SAGE & DNA Microarrays
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Transcriptome = complete collection of all RNAs in a
Xiong: Chp 18 Functional Genomics
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Transcriptome Analysis
Chp 18 – Functional Genomics
SECTION V
E Margulies 2005BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
Microarrays - "Gene Chips" most popular
Other related methods:
SAGE = Serial Analysis of Gene Expression
MPSS = Massively Parallel Signature Sequencing
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Microarray Analysis
Microarray Analysis - What's the big deal?
Very powerful technology to evaluate global changes in
gene expression
Which RNAs are detected?
• mRNAs (& pre-RNAs)
alternatively spliced mRNAs
• rRNAs, tRNAs
• miRNAs, siRNAs, other regulatory RNAs
Applications in medicine, genetics, evolution, ecology,
animal breeding, plant stress, homeland security!
2 Major Types of Microarrays:
cDNA = "spotted" = low density, glass slides
= Southern blot on a slide
oligo = "DNA chip" = high density, photolithography
"Affy" chip; computationally designed
Many recent developments & variations:
DNA chips
protein chips
carbohydrate chips
antibody chips,antigen chips
cell chips
whole body chips??
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Both types can be made here, in ISU facilities
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A cDNA Microarray
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Production of cDNA probes for a DNA chip
a) From populations of cells grown under two
different conditions, mRNA is isolated and
copied into cDNA
(left= Red; right = Green)
b) Red & Green cDNAs are mixed, placed on
the chip, covered by a glass coverslip and
incubated overnight with the DNA microarray
Each purple spot = one PCR product; on a real microarray
each spot is ~100 microns in diameter
Copyright © 2006
A. Malcolm Campbell
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Copyright © 2006
A. Malcolm Campbell
Measuring fluorescence on a cDNA chip
Top spot shows the merged image (ratio of 635 nm:532 nm)
Middle spot shows the red (635 nm) channel only
Bottom spot shows the green (532 nm) channel only
Some merged images will look a) more red than green, b) more green
than red, c) about equal red and green
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Results from a single DNA chip
3 different genes out of 6,200 available on this chip are shown.
Copyright © 2006
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a) Red transcriptome
b) Green transcriptome
c) Genes expressed in both (yellow) transcriptomes
Genes not expressed in either condition (gray)
Copyright © 2006
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Comparison of Northern Blots with
cDNA Microarray Data
Green-red color scale for changes in
transcription
Black = Genes transcribed equally in both conditions
Red = Induced genes (transcription increased)
Green = Repressed genes (transcription decreased)
Hmmm, I think this color scheme seems "backwards"…
Copyright © 2006
A. Malcolm Campbell
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a) 4 individual Northern blots for 4 different genes, measuring mRNA accumulation over time
b) A series of microarray results for the same 4 genes of interest. Scale on the bottom indicates
a 20-fold repression (bright green) and 20-fold induction (bright red). Black indicates no
change in transcription (i.e., the merged microarray spot would have appeared yellow).
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• Experimental Design is critical
Microarray Facilities:
(ISU Course: Statistical Design & Analysis of Microarray Experiments)
Center for Plant Genomics (ISU PSI) - Pat Schnable
•Hui-Hsien Chou (Com S) - "Picky" software for designing oligos
•Dan Nettleton (Stat) - Experimental design & statistical analyses
•Di Cook (Stat) "exploRase" software for high-dimensional data
analysis & visualization for systems biology
in Carver Co-Lab
GeneChip Facility (ISU Biotech & PSI) - Steve Whitham
in MBB
Research Labs:
Pat Schnable (Agron/GDCB) - Facilities for cDNA microarrays
Steve Whitham (PlPath) - Facilities for oligo microarrays
Google "microarrays" from ISU website>>> Lots more:
Jo Anne Powell-Coffman, GDCB: genes induced under oxidative stress
Roger Wise, Rico Caldo, Plant Pathology: interaction between multiple
isolates of powdery mildew and multiple genotypes of barley
Chris Tuggle, Animal Science: genes controlling mammalian embryo
development
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ISU Microarray Design & Analysis
ISU Microarray Researchers & Facilities
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•Tools from Statistics & Machine Learning are needed
ISU Experts: Dan Nettleton & Di Cook, Stat
Vasant Honavar, Com S
Statistics:
ANOVA (Analysis of Variance)
R Statistics package
ML: Clustering & Classification Algorithms
WEKA package
GEPAS
Many additional resources & tools available online
ISU has several Microarray Analysis SuiteS
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