Download Promoter Analysis

Promoter and Module Analysis
Statistics for Systems Biology
Transcription Factors
• DNA binding proteins that facilitate or
inhibit Pol II initiation or elongation
• General transcription factors:
– Used widely for many genes under many
circumstances
• Specific transcription factors
– Used to initiate specific genes under specific
circumstances
• Distinction may not be so sharp!
Transcription Factor Families
• Several structures line
up amino acids
– Helix-turn-Helix
(Homeodomain)
– Helix-loop-helix
– Zinc Finger
• Mostly dimers
• These families have
proliferated because of
their role in attracting
transcription apparatus
DNA-Binding Proteins
• All proteins interact weakly
with DNA
• Proteins with projecting
amino acids interact with
the DNA major groove
• Hydrogen bonds stabilize
position of proteins on DNA
• Proteins that line up several
amino acid contacts bind
strongly to specific DNA
sequences
Transcription Factor Recognition
Sites
• Typically 6-10 positions very selective and
several others show bias
• Often selectivity profile summarized by
‘motif’
Selectivity of Specific T.F.’s
• Most TF’s recognize 6-10 bases of DNA
• E. coli: longer (8-12 bp) TF’s
– All sequences are effective
• Yeast: areas around promoters selectively
cleared of nucleosomes
– ~ 30 x accessibility for those
• Animal: cooperative binding of several
T.F.’s
Cofactors
• Frequently the effect of
DNA-binding proteins
depends on co-factors
• E.g. ER sits on the DNA
but requires estrogen as a
co-factor to function
• Myc requires Max as a cofactor to stimulate
transcription
• If Max is coupled with Mad
instead, the genes are
repressed
Assembly of Transcription App.
• Change in physical
conformation of DNA
leads to increased
likelihood of
spontaneous
assembly of Pol II
• Getting Pol II further
into the gene seems
to require further
steps
The TF Family Circus
Inferring Regulatory Architecture
• Aim: to find which regulators influence
gene expression
• Concerns:
– Contributions of many factors to any one gene
• Approaches:
– Decision tree (Computer Science)
– Regression (more statistical)
• DNA sequence motifs can be a surrogate
The Israeli ‘Module’ Approach
•
Idea: model TF binding as a ‘decisiontree’
•
Steps
1. Cluster gene expression profiles
2. Fit best regulator tree to each cluster
3. Re-assign genes to clusters
•
Iterate until converge
Strengths and Weaknesses of
Module Approach
• Explicitly models interaction among
regulators
• Expression arrays give poor estimates of
activity of TF’s or other regulators
• Some regulators could repress genes
• Discrete predictor model is inefficient
Update: Estimating TF Activity
• Since TF expression data is unreliable for
activity, could we do better inferring TF
activity?
• Use DNA sequence motifs as surrogate for
TF binding
• Fit double E-M – complicated!
The Regression Approach
• Direct data on TF occupancy from ChIP
• Two stages:
– Find candidate TF’s by correlation between
occupancy and sets of genes
– Estimate TF activity in each condition by
regression model
Regression Steps
Preliminary Screen
r > rthreshold