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Microarrays & Gene Expression
Analysis
Contents
• DNA microarray technique
• Why measure gene expression
• Clustering algorithms
• Relation to Cancer
• SAGE
• SBH – Sequencing By Hybridization
DNA Microarrays
1. Developed around 1987.
2. Employ methods previously exploited in
immunoassay context – specific binding and marking
techniques.
3. Two types of probes: http://www.gene-chips.com/
Format I: probe cDNA (500~5,000 bases long) is
immobilized to a solid surface such as glass; widely
considered as developed at Stanford University;
Traditionally called DNA microarrays.
Format II: an array of oligonucleotide (20~80-mer oligos)
probes is synthesized either in situ (on-chip) or by
conventional synthesis followed by on-chip immobilization;
developed at Affymetrix, Inc. Many companies are
manufacturing oligonucleotide based chips using alternative
in-situ synthesis or depositioning technologies. Historically
called DNA chips.
DNA Microarray Technique
1. The microarray is made of a small piece of glass
(1x1 or 2x2 cm).
2. Thousands to millions of pixels are put on it, in
each many (n) copies of DNA probes (short (8-30
bases), single stranded, called OLIGO).
3. A probe on the array will bind its complementary
target if it is present in the solution washing the
chip.
4. When the array surface is scanned with a laser,
fluorescent labels attached to the targets reveal
which probes are bound.
Use of DNA Microarrays
1. Identify a query sequence - the sequence is
hybridized to an array containing suitable probes
1. Point mutations (SNP) or other mutations – the
array contains probes that match segments of
the normal and mutated sequences.
2. An unknown sequence (SBH) – the array contains
all possible k-mers (e.g., all the 46 6-mers)
2. Gene expression analysis - which genes are
expressed ? under what conditions ?
DNA Microarray Methodology - Flash Animation
http://www.bio.davidson.edu/biology/courses/genomics/chip/chip.html
Why Measure Gene Expression
Why Measure Gene Expression
1. Determines which genes are induced/repressed in
response to a developmental phase or to an
environmental change.
Why Measure Gene Expression
1. Determines which genes are induced/repressed in
response to a developmental phase or to an
environmental change.
2. Sets of genes whose expression rises and falls
under the same condition are likely to have a
related function.
Why Measure Gene Expression
1. Determines which genes are induced/repressed in
response to a developmental phase or to an
environmental change.
2. Sets of genes whose expression rises and falls
under the same condition are likely to have a
related function.
3. Features such as a common regulatory motif can be
detected within co-expressed genes.
Why Measure Gene Expression
1. Determines which genes are induced/repressed in
response to a developmental phase or to an
environmental change.
2. Sets of genes whose expression rises and falls
under the same condition are likely to have a
related function.
3. Features such as a common regulatory motif can be
detected within co-expressed genes.
4. A pattern of gene expression may be used as an
indicator of abnormal cellular regulation.
• A useful tool for cancer diagnosis
Clustering Co-expressed Genes
1. Find genes whose expression rises and falls under
the same conditions.
2. Methods include:
1. Hierarchical clustering.
2. Self organizing maps.
3. Support vector machines (SVMs).
Hierarchical Clustering
• Cluster analysis and display of genome-wide
expression patterns. Michael B. Eisen, Paul T.
Spellman, Patrick O. Brown , and David Botstein,
1998, http://www.pnas.org/cgi/content/full/95/25/14863
• Relationships among objects (genes) are represented
by a tree whose branch lengths reflect the degree of
similarity between the objects, as assessed by a
pairwise similarity function.
• The computed trees can be used to order genes in
the original data table, so that genes or groups of
genes with similar expression patterns are adjacent.
GeneExplorer
GeneCards
pointer
UniGene
pointer
Zoom:
Similarity Metric
• The gene similarity metric is a form of correlation
coefficient.
• Let Gi equal the (log-transformed) primary data for
gene G in condition i. For any two genes x and y
observed over a series of N conditions, a similarity
score can be computed as follows:
S(x,y) = i=1..N(xi-x)(yi-y) / (std(x)*(std(y))
where x,y are the mean of observations on genes x
and y.
• A neighbor joining method is used to built the
corresponding tree.
Tree Creation
• For any set of n genes, a similarity matrix is
computed by using the metric described above.
• The matrix is scanned to identify the highest value
(representing the most similar pair of genes).
• A node is created joining these two genes, and a
gene expression profile is computed for the node by
averaging observation for the joined elements
(missing values are omitted and the two joined
elements are weighted by the number of genes they
contain).
• The similarity matrix is updated with this new node
replacing the two joined elements, and the process is
repeated n-1 times until only a single element remains.
Five separate clusters are
indicated by colored bars and
by identical coloring of the
corresponding region of the
dendrogram. The sequenceverified named genes in these
clusters contain multiple genes
involved in (A) cholesterol
biosynthesis, (B) the cell cycle,
(C) the immediate-early
response, (D) signaling and
angiogenesis, and (E) wound
healing and tissue remodeling.
These clusters also contain
named genes not involved in
these processes and numerous
uncharacterized genes.
Self Organizing Maps
•
K-means method: the number of clusters is fixed
(k).
• g1, ..,gn represents the expression of each gene gi
in d experiments as a point in d dimensions.
• Randomly choose k centers, c1, ..,ck: ci is a point in a
d dimension.
• The protocol:
1. Join gi to the closest center.
2. Compute new centers. The new center ci‘ is the
center of mass of all points joined to ci.
3. Repeat the steps until convergence or until
you’re pleased with the results.
Relation to Cancer
• Tumors result from disruptions of growth
regulation. Although most tumors are treated with
general anti-proliferate drugs, they exhibit
remarkable clinical heterogeneity which remains a
major challenge in the successful management of
cancer.
• Clinical heterogeneity in tumors likely reflects
unrecognized molecular heterogeneity in tumors.
Because of the logical connection between gene
expression patterns and phenotype, it is likely that
there is a direct connection between gene
expression patterns of tumors and their clinical
phenotype.
Towards a clinically relevant taxonomy
of Cancer
• Access archived clinical tumor samples taken at
or near diagnosis from patients with wellcharacterized subsequent clinical histories.
• Use DNA arrays to measure gene expression in
these samples.
• Look for new molecularly defined groups within
or between previously recognized groups of
tumors, especially groups with increased clinical
homogeneity.
• Look for direct associations between molecular
and clinical properties of tumors.
Cancer Gene Expression
• The suggested procedure has been used to classify
several types of cancer, or cancerous verses
normal cells.
• Breast cancer
• AML and ALL.
• Melanoma.
• Lymphoma.
• …
Example - Melanoma
• Molecular classification of cutaneous malignant
melanoma by gene expression profiling. Nature
2000 Aug 3;406(6795):536-40
• Discovered a subset of melanomas identified by
mathematical analysis of gene expression in a
series of samples.
Example - Melanoma
• Remarkably, many genes underlying the
classification of this subset are differentially
regulated in invasive melanomas that form primitive
tubular networks in vitro, a feature of some highly
aggressive metastatic melanomas.
• Global transcript analysis can identify
unrecognized subtypes of cutaneous melanoma and
predict experimentally verifiable phenotypic
characteristics that may be of importance to
disease progression.
Detection of Regulatory Motifs
• A group of co-expressed genes is likely to be coregulated during transcription.
• Transcription initiation is mediated by regulatory
proteins that usually bind upstream to the
transcription start site.
• The regulatory proteins bind to conserved
regulatory motifs, a short DNA sequence.
• The upstream region of co-expressed genes can be
searched for a common regulatory motif.
Other Applications – Predictive Tools
• There is a correlation between co-expression and
related gene function.
“Inferring subnetworks from perturbed expression
profiles.” Bioinformatics. 2001 Jun;17 Suppl
1:S215-S224.
• There is a correlation between co-expression and
protein-protein interaction.
“Correlation between transcriptome and
interactome mapping data from Saccharomyces
cerevisiae.” Nat Genet. 2001 Dec;29(4):482-6.
• Poor correlation between gene expression and
protein expression.
Correlation between gene and protein
expression
Ideker
et al.,
science
2001
Design & Probe Selection
• Sensitivity – probes need to hybridize to their
targets. For example – they need to avoid highly
structured regions of the target molecule.
• Specificity – probes need not hybridize to wrong
targets (cross hybridization). To this end:
– design probes to be long enough for statistical
protection.
– search databases to explicitly avoid crosshybridization to known foreign mRNA.
• Mismatch control.
Other Challenges
• Analyze image to infer expression levels from red
to green ratios, clean background, check for
outliers, etc.
• Infer causal relations between
genes – regulatory networks.
• Experimental technique assigned to gain a quantitive
measure of gene expression.
• ~10-20 base “tags” are produced (immediately
adjacent to the 3’ end of the 3’ most NlaIII
restriction site).
• The SAGE technique measures not the expression
level of a gene, but quantifies a "tag" which
represents the transcription product of a gene.
http://www.ncbi.nlm.nih.gov/SAGE/
SAGE Technique
1. Extracting unique tagging sequences from mRNA
molecules (tags are ~10-20b long).
2. Concatenating the tags to a long sequence.
3. Sequencing the resulting sequence and inferring
levels from frequencies.
• Advantage: an unbiased and inclusive analysis of
the transcriptome.
• Sequencing errors are especially problematic when
tags are used, because of the short length of tags.
• Of roughly 1.5 million transcript sequences
stored in GenBank, only about 180,000 are well
characterized, and tags could represent them.
http://www.sagenet.org/
http://www.ncbi.nlm.nih.gov/SAGE/index.cgi
Colon cancer vs normal colon
B
A
Colon Normal
cancer colon
SBH – Sequencing by Hybridization
A method for sequencing, actually the original
motivation of DNA microarrays.
• A chip containing all k-mers is produced.
• The query sequence is hybridized to the chip.
• Example: a chip of all 3-mers is produced,
containing 64 probes. 5 probes will be highlighted.
CAT
ATA
TAG
AGT
GTA
CATAGTA
Using chips for
sequencing
SBH Protocol
• Knowing the start and end of the query sequence,
and the set of highlighted k-mers, the query
sequence is reconstructed.
• Example: start = CAT, end = GTA, highlighted
group = {CAT, ATA, TAG, AGT, GTA}.
CAT – AT? CAT
ATA – TA? CATA
TAG – AG? CATAG
AGT – GTA CATAGT
• Problems:
• Reconstruction is not always unique – same
k-mer may be followed by several k-mers.
• CAT – ATA, ATG.
• Hybridization contain errors.