BCB 444/544
Lecture 35
A bit more Comparative Genomics
Functional Genomics
(Microarrays)
#35_Nov14
BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
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Required Reading
(before lecture)
Mon Nov 12 - Lecture 34
Comparative Genomics
• Chp 17
Wed Nov 14 - Lecture 35
Functional Genomics
• Chp 18
Thurs Nov 15 - Lab 11
Microarray Analysis
Fri Nov 16 - Lecture 36
Proteomics
• Chp 19
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Assignments & Announcements
Mon Nov 12 - HW#6
(was finally posted on MON)
HW#6 - Fun with SNPs, Comparative Genomics &
Gene Annotation!!
Due: whenever…
(sometime before 5 PM Mon Nov 26)
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Seminars this Week
BCB List of URLs for Seminars related to Bioinformatics:
http://www.bcb.iastate.edu/seminars/index.html
• Nov 12 Mon - Math Seminar 4:10 in 294 Carver
• Trachette Jackson Univ of Michigan
• Mathematical Modeling of Angiogenesis in Cancer
• 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
1. REWARD: X Prize Foundation $10 Million
for sequencing 100 human genomes in 10 days
2. Science cover article this week:
Capillary sequencing of tumor cell genomes
(65 of 4 million sequencing reactions are shown)
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 -
Interesting papers provided by Erin
(see class website 2006 for additional links)
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Chp 17 – Comparative Genomics
SECTION V
GENOMICS & PROTEOMICS
Xiong: Chp 17 Genome Mapping, Assembly & Comparison
• Genome Mapping
•
•
•
•
Genome Sequencing
Genome Sequence Assembly
Genome Annotation
Comparative Genomics
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Genomics -
for excellent overview lectures,
see these posted by NHGRI & Pevsner:
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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Recent technologies?
Pyro-Sequencing
http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/P/Pyrosequencing.html
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Massively Parallel Sequencing: 454
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Massively Parallel Sequencing: 454 at ISU?
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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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ENCODE - Results? June 2007
http://www.nature.com/nature/journal/v447/n7146/full/nature05874.html
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Was It Really Worth all that $$?
& Who Owns it Now???
http://www.i-sis.org.uk/humangenome.php
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Speaking of Craig Venter - Where is the
“Cutting-Edge” in Sequencing Technology?
http://www.jcvi.org/
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Human Genome Project:
What have we learned?
20,000 - 25,000
2004
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J Pevsner 2005
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Lots of SNPs:
single nucleotide polymorphisms
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J Pevsner 2005
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SNPs: Single Nucleotide Polymorphisms
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J Mullikin 2005
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SNPs: Single Nucleotide Polymorphisms
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SNP Discovery Methods
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& 454 Sequencing!
J Mullikin 2005
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SNPs: Access via 3 Major Genome Browsers
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Haplotype - What is it?
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J Mullikin 2005
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Haplotypes: an example
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J Mullikin 2005
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Haplotypes: Two definitions
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Haplotypes: a better explanation!
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http://www.hapmap.org/
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Hapmap Project
http://www.hapmap.org/
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HapMap Project Goals
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J Mullikin 2005
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HapMap Results: http://www.hapmap.org/
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Why are SNPs/HapMap Important?
(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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An example from Pharmocogenomics
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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Developing Effective Treatments Requires
Balance between Efficacy & Toxicity
& these depend both on genetics and
environment
& tough Ethical Issues arise
Fig 4.15
Continuum of Utility of a Particular Genetic Test
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Significance of SNP Analyses
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J Mullikin 2005
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Other implications? What is extent of
diatom genetic diversity in oceans - and
what effect might this have on global CO2
levels & global warming?
Fig 4.1 Diatom Bloom Study
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,
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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4- Comparative Genomics
Many thanks to:
Elliott Margulies, NHGRI
for the following slides extracted from his lecture on:
Comparative Sequence Analysis
CTGA2005Lecture8.pdf
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Comparative Genomics
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Comparative Genomics Provides Important
Clues re: Biological Function of Genes
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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:
QuickTime™ and a
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decompressor
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zPicture: Best of Both Tools:
http://zpicture.dcode.org/
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Comparing Multiple Species with zPicture
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The Comparative Genomics Company?
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What Have We Learned from Comparative
Genomics? An early example:
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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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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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Chp 18 – Functional Genomics
SECTION V
GENOMICS & PROTEOMICS
Xiong: Chp 18 Functional Genomics
• Sequence-based Approaches
• Microarray-based Approaches
• Comparison of SAGE & DNA Microarrays
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Transcriptome Analysis
Transcriptome = complete collection of all RNAs in a
cell at a given time
High-throughput analysis of RNA expression:
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 - What's the big deal?
Very powerful technology to evaluate global changes in
gene expression
Applications in medicine, genetics, evolution, ecology,
animal breeding, plant stress, homeland security!
Many recent developments & variations:
DNA chips
protein chips
carbohydrate chips
antibody chips,antigen chips
cell chips
whole body chips??
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Microarray Analysis
Which RNAs are detected?
• mRNAs (& pre-RNAs)
alternatively spliced mRNAs
• rRNAs, tRNAs
• miRNAs, siRNAs, other regulatory RNAs
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
•
Both types can be made here, in ISU facilities
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A cDNA Microarray
Each purple spot = one PCR product; on a real microarray
each spot is ~100 microns in diameter
Copyright © 2006
A. Malcolm Campbell
BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
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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
Copyright © 2006
A. Malcolm Campbell
BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
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Measuring fluorescence on a cDNA chip
3 different genes out of 6,200 available on this chip are shown.
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
Copyright © 2006
A. Malcolm Campbell
BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
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Results from a single DNA chip
a) Red transcriptome
b) Green transcriptome
c) Genes expressed in both (yellow) transcriptomes
Genes not expressed in either condition (gray)
Copyright © 2006
A. Malcolm Campbell
BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
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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
BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics
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Comparison of Northern Blots with
cDNA Microarray Data
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).
Copyright © 2006
A. Malcolm Campbell
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ISU Microarray Researchers & Facilities
Microarray Facilities:
Center for Plant Genomics (ISU PSI) - Pat Schnable
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
• Experimental Design is critical
(ISU Course: Statistical Design & Analysis of Microarray Experiments)
•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
•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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