From motif search to gene expression analysis

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• From motif search to gene expression analysis

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Protein Motifs
Protein motifs are usually 6-20 amino acids long
and can be represented as a consensus/profile
PEDXKRWRKXED
or as PWM
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Protein Domains

• In additional to protein short motifs, proteins
  are characterized by Domains.
• Domains are long motifs (30-100 aa) and are
  considered as the building blocks of proteins
  (evolutionary modules).

The zinc-finger domain
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Some domains can be found in many proteins with
different functions
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.while other domains are only found in proteins
with a certain function..
MBD Methylated DNA Binding Domain
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Varieties of protein domains
Extending along the length of a protein
Occupying a subset of a protein sequence
Occurring one or more times
Page 228
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Pfam

• gt Database that contains a large collection of
  multiple sequence alignments of protein domains
• Based on
• Profile hidden Markov Models (HMMs).
• HMM in comparison to PWM is a model
• which considers dependencies between the
• different columns in the matrix (different
  residues) and is thus much more powerful!!!!

http//pfam.sanger.ac.uk/
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Profile HMM (Hidden Markov Model)can accurately
represent a MSA
D19
D16
D17
D18
100
16 17 18 19
delete
D R T R D R T S S - - S S P T R D R T R D P
T S D - - S D - - S D - - S D - - R
100
50
M16
M17
M18
M19
100
100
50
D 0.8 S 0.2
P 0.4 R 0.6
R 0.4 S 0.6
Match
T 1.0
I16
I19
I18
I17
insert
X
X
X
X
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Gene Expression Analysis
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Gene Expression
protein
RNA
DNA
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Gene Expression
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
mRNA gene1
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
mRNA gene2
AAAAAAA
AAAAAAA
AAAAAAA
AAAAAAA
mRNA gene3
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Studying Gene Expression 1987-2011
Spotted microarray (first high throughput gene
expression experiments)
DNA chips
RNA-seq (Next Generation Sequencing)
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Classical versus modern technologies to study
gene expression

• Classical Methods (Spotted microarray, DNA chips)
• -Require prior knowledge on the RNA transcript
• Good for studying the expression of known genes
• New generation RNA sequencing
• Do not require prior knowledge
• Good for discovering new transcripts

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Classical Methods

• Spotted Microarray
• Two channel cDNA microarrays.
• DNA Chips
• One channel microarrays
• (Affymetrix, Agilent),

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http//www.bio.davidson.edu/courses/genomics/chip/
chip.html
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Experimental Protocol Two channel cDNA arrays

• Design an experiment
• (probe design)
• 2. Extract RNA molecules from cell
• Label molecules with fluorescent dye
• Pour solution onto microarray
• Then wash off excess molecules
• 5. Shine laser light onto array
• Scan for presence of fluorescent dye
• 6. Analyze the microarray image

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The ratio of expression is indicated by the
intensity of the color Red High mRNA abundance
in the experiment sample Green High mRNA
abundance in the control sample
Transforming raw data to ratio
of expression
Cy3
Cy5
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One channel DNA chips

• Each sequence is represented by a probe set
  colored with one fluorescent dye
• Target hybridizes to complimentary probes only
• The fluorescence intensity is indicative of the
  expression of the target sequence

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Affymetrix Chip
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RNA-seq
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Clustering genes according to their expression
profiles
NEXT

• .

Experiments
Genes
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WHY?What can we learn from the clusterers?

• Identify gene function
• Similar expression can infer similar function
• Diagnostics and Therapy
• Different genes expression can indicate a disease
  state
• Genes which change expression in a disease can be
  good candidates for drug targets

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HOW?Different clustering approaches

• Unsupervised
• -Hierarchical Clustering
• -Partition Methods
• K-means
• Supervised Methods
• -Analysis of variance
• -Discriminant analysis
• -Support Vector Machine (SVM)

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Clustering

• Clustering organizes things that are close into
  groups.
• - What does it mean for two genes to be close?
• - Once we know this, how do we define groups?

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What does it mean for two genes to be close?
We need a mathematical definition of distance
between the expression of two genes
Gene 1
Gene 2
Gene1 (E11, E12, , E1N) Gene2 (E21, E22, ,
E2N)
For example distance between gene 1 and
2 Euclidean distance Sqrt of Sum of (E1i -E2i)2,
i1,,N
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Once we know this, how do we define groups?
Michael Eisen, 1998 Generate a tree based on
similarity (similar to a phylogenetic tree) Each
gene is a leaf on the tree Distances reflect
similarity of expression
Hierarchical Clustering
Gene Cluster
Genes
Experiments
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Internal nodes represent different functional
Groups (A, B, C, D, E)
genes
One genes may belong to more than one cluster
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Clusters can be presented by graphs
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What can we learn from clusters with similar gene
expression ??
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EXAMPLE- hnRNP A1 and SRp40
HnRNPA1 and SRp40 are not clear homologs based
on blast e-value but have a very similar gene
expression pattern in different tissues
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Are hnRNP A1 and SRp40 functionally homologs ??
hnRNP A1
SF
SF
SF
SF
SF
SF
SF
SF
SF
SF
SF
SF
SRP40
YES!!!!
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What can we learn from clusters with similar gene
expression ??

• Similar expression between genes
• The genes have similar function
• One gene controls the other
• All genes are controlled by a common regulatory
  genes

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How can we use microarray for diagnostics?
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Gene-Expression Profiles in Hereditary Breast
Cancer

• Breast tumors studied
• BRCA1
• BRCA2
• sporadic tumors
• Log-ratios measurements of 3226 genes for each
  tumor after initial data filtering

RESEARCH QUESTION Can we distinguish BRCA1 from
BRCA2 cancers based solely on their gene
expression profiles?
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How can microarrays be used as a basis for
diagnostic ?
5 Breast Cancer Patient
Patient 1 patient 2 patient 3 patient4 patient 5
Gen1 - -
Gen2 - -
Gen3 - -
Gen4 - -
Gen5 - - -
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How can microarrays be used as a basis for
diagnostic ?
BRCA1
BRCA2
patinet1 patient 2 patient4 patient 3 patient 5
Gen1 - -
Gen3 - -
Gen4 - -
Gen2 - -
Gen5 - - -
Informative Genes
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Specific Examples
Cancer Research
Hundreds of genes that differentiate
between cancer tissues in different stages of the
tumor were found. The arrow shows an example of a
tumor cells which were not detected correctly
by histological or other clinical parameters.
Ramaswamy et al, 2003 Nat Genet 3349-54
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Supervised approachesfor predicting gene
function based on microarray data
Support Vector Machine

• SVM would begin with a set of genes that have a
  common function (red dots), In addition, a
  separate set of genes that are known not to be
  members of the functional class (blue dots) are
  specified.

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• Using this training set, an SVM would learn to
  differentiate between the members and
  non-members of a given functional class based on
  expression data.

• Having learned the expression features of the
  class, the SVM could recognize new genes as
  members or as non-members of the class based on
  their expression data.

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Using SVMs to diagnose tumors based on
expression data
Each dot represents a vector of the expression
pattern taken from a microarray experiment . For
example the expression pattern of all genes from
a cancer patients.
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How do SVMs work with expression data?
In this example red dots can be primary tumors
and blue are from metastasis stage. The SVM is
trained on data which was classified based on
histology.
After training the SVM we can use it to diagnose
the unknown tumor.
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Projects 2012-13
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Instructions for the final project Introduction
to Bioinformatics 2012-13
Key dates 13.12 lists of suggested projects
published You are highly encouraged to choose
a project yourself or find a relevant project
which can help in your research 22.1 Submission
project overview (one page) -Title -Main
question -Major Tools you are planning to use to
answer the questions Final week meetings on
projects 12.3 Poster submission 20.3 Poster
presentation
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2. Planning your research After you have
described the main question or questions of your
project, you should carefully plan your next
steps A. Make sure you understand the problem and
read the necessary background to proceed B.
formulate your working plan, step by step C.
After you have a plan, start from extracting the
necessary data and decide on the relevant tools
to use at the first step. When running a tool
make sure to summarize the results and extract
the relevant information you need to answer your
question, it is recommended to save the raw data
for your records , don't present raw data in your
final project. Your initial results should guide
you towards your next steps. D. When you feel you
explored all tools you can apply to answer your
question you should summarize and get to
conclusions. Remember NO is also an answer as
long as you are sure it is NO. Also remember this
is a course project not only a HW exercise. .

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• Summarizing final project in a poster (in pairs)
• Prepare in PPT poster size 90-120 cm
• Title of the project
• Names and affiliation of the students presenting
• The poster should include 5 sections
• Background should include description of your
  question (can add figure)
• Goal and Research Plan
• Describe the main objective and the research plan
• Results (main section) Present your results in
  3-4 figures, describe each figure (figure
  legends) and give a title to each result
• Conclusions summarized in points the
  conclusions of your project
• References List the references of
  paper/databases/tools used for your project

Examples of posters will be presented in class