#36 - Microarrays
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Required Reading
BCB 444/544
(before lecture)
Mon Nov 12 - Lecture 34
Lecture 36
More:
Comparative Genomics
• Chp 17
Wed Nov 14 - Lecture 35
Microarrays
Functional Genomics
• Chp 18
Probably not: Proteomics
Thurs Nov 15 - Lab 11
Microarray Analysis
Fri Nov 16 - Lecture 36
#36_Nov16
More Microarrays
Proteomics - Chp 19 (after TurkeyBreak)
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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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Recent technologies?
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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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High Throughput (HTP) Genotyping ISU?
Sequenom
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Haplotype - What is it?
HTP 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
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Dickerson (ECprE), Wise (PlPath/USDA) - & many
others = ISU computational & experimental experts
with large scale genome assembly research focus
e.g.,
Kalyanaraman A, Emrich SJ, Schnable PS, Aluru S (2007) Assembling genomes
on large-scale parallel computers. Journal of Parallel and Distributed
Computing. in press
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Haplotypes: an example
J Mullikin 2005
BCB 444/544 F07 ISU Dobbs #36 - Microarrays
Haplotypes: Two definitions
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
J Mullikin 2005
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Hapmap Project
Haplotypes: a more detailed explanation!
http://www.hapmap.org/BCB 444/544 F07 ISU Dobbs #36 - Microarrays
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http://www.hapmap.org/
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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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Comparative Genomics
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Comparing Multiple Species with zPicture
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
E Margulies 2005
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What Have We Learned from Comparative
Genomics? A more recent example:
The Comparative Genomics Company?
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)
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SECTION V
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
Xiong: Chp 18 Functional Genomics
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Microarray Analysis - What's the big deal?
Very powerful technology to evaluate global changes in
gene expression
Transcriptome = complete collection of all RNAs in a
cell at a given time
Applications in medicine, genetics, evolution, ecology,
animal breeding, plant stress, homeland security!
High-throughput analysis of RNA expression:
Many recent developments & variations:
Microarrays - "Gene Chips" most popular
DNA chips
protein chips
carbohydrate chips
antibody chips,antigen chips
cell chips
whole body chips??
Other related methods:
SAGE = Serial Analysis of Gene Expression
MPSS = Massively Parallel Signature Sequencing
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GENOMICS & PROTEOMICS
• Sequence-based Approaches
• Microarray-based Approaches
• Comparison of SAGE & DNA Microarrays
Transcriptome Analysis
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Chp 18 – Functional Genomics
Genomic sequencing, Genotyping, Comparative genomics
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A cDNA Microarray
Microarray Analysis
Which RNAs are detected?
• mRNAs (& pre-RNAs)
alternatively spliced mRNAs
• rRNAs, tRNAs
• miRNAs, siRNAs, other regulatory RNAs
2 Major Types of DNA 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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Each purple spot = one PCR product; on a real microarray
each spot is ~100 microns in diameter
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Copyright © 2006
A. Malcolm Campbell
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Measuring fluorescence on a cDNA chip
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
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
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Copyright © 2006
A. Malcolm Campbell
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Results from a single DNA chip
Green-red color scale for changes in
transcription
a) Red transcriptome
b) Green transcriptome
c) Genes expressed in both (yellow) transcriptomes
Black = Genes transcribed equally in both conditions
Red = Induced genes (transcription increased)
Green = Repressed genes (transcription decreased)
Genes not expressed in either condition (gray)
Copyright © 2006
A. Malcolm Campbell
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Hmmm, I think this color scheme seems "backwards"…
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Copyright © 2006
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Comparison of Northern Blots with
cDNA Microarray Data
Example of: Raw cDNA microarray data
Only one time point &
Only part of array is
shown
cDNAs were from:
• Control cells = Green
• Cells consuming glucose
for 9.5 hrs = Red
Red= induced
Green = repressed
Yellow = no change
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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Copyright © 2006
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Typical (Toy) Experiment:
Example of: A Time Course Experiment
Transcriptional response of 3 genes to
gradual loss of oxygen
a) Graph of O2 consumption by
yeast growing in a closed
container for 11 hrs
b) DNA microarray data, given
in the form of ratios.
To calculate a ratio, the activity
of one gene in a culture
gradually consuming all the
available oxygen was divided
by its activity in a control
culture with unlimited oxygen
Copyright © 2006
A. Malcolm Campbell
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Copyright © 2006
A. Malcolm Campbell
TF is induced 2X & is known to activate genes
G1 and G2, both of which are induced 6X.
G3 is induced 6X, too. Is it regulated by TF?
Clustering of gene expression patterns (with known genes) suggests
potential functions for unknown genes - additional experiments are
required to test these hypothesized functions.
Protein synthesis genes repressed
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novel proteins
Energy metabolism genes induced
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"Guilt by Association" - Similar expression
patterns suggest potential functions for
An Example of Results: Coordinated
Regulation of Genes that ”Work Together"
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Change in mRNA production (Exp/Control)
for 12 hypothetical genes
Partial Data (for 14 Genes) from One
Yeast DNA Microarray Experiment
Copyright © 2006
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Copyright © 2006
A. Malcolm Campbell
Clustering of data from Table 6.2, based
on similarity of expression profiles
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Clustered Expression Data: Ratios from
Table 6.3 Converted to Colors
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Correlation coefficients for each pair of
genes, based on log2-tranformed data in
Table MM6.1
Why are gene expression ratios
log2 transformed before further analysis?
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A. Malcolm Campbell
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Copyright © 2006
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An Example: Results of Hierarchical
Clustering for 12 genes in Table MM6.2
Copyright © 2006
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Dendrogram of clustered genes from
Table MM6.4 and Fig 6.8
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Gene Expression Pattern Clusters:
for several thousand genes!!
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
Each row represents a different gene
Each column represents a different time point
Green indicates repression (decrease in RNA)
Red indicates induction (increase in RNA)
Research Labs:
Pat Schnable (Agron/GDCB) - Facilities for cDNA microarrays
Steve Whitham (PlPath) - Facilities for oligo microarrays
Genes have been clustered so they are near
other genes with similar expression patterns.
Notice that the genes at the bottom were
repressed for the first few time points.
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
Copyright © 2006
A. Malcolm Campbell
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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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