chip-chip data. dna-binding proteins constitutive proteins (mostly histones) –organize dna...
TRANSCRIPT
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ChIP-chip Data
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DNA-binding proteins
• Constitutive proteins (mostly histones)– Organize DNA– Regulate access to DNA– Have many modifications
• Acetylation, methylation, …
• Sporadic proteins (Transcription Factors)– Mediate docking of transcription apparatus– Modify histones– Methylate DNA
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Histones
Histones are an ancient family of proteins which serve as the scaffold for DNA
Four types of histones assemble in pairs to form a nucleosome
DNA is wrapped twice around each nucleosome
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Histones and Modifications
DNA contacts histones on their tails Histone tails can be modified
Histones can stay loose or assemble tightly – this compacts the DNA
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Transcription Factors
• General – help to set up transcription of many genes
• Specific – draw in general factors or RNA Pol II to specific genes
TATABindingProtein
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DNA Methylation
Adding a Methyl to Cytosine
Cytosine methylation is passed on to daughter cells
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Chromatin Immuno-precipitation
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Tiling Array
• One probe every n base pairs over some length of chromosome
– Interrupted by repeat regions
• Promoter array: each (known) promoter tiled
An Affymetrix tiling design
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What the data look like
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histone acetylation on 15 samples over one promoter (raw)
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Multiple Promoters
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Normalized by Medians
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Methods and Issues
• Normalization– Different enrichment ratios– Different probe thermodynamics– Dye and probe bias
• Estimation– Categorical or continuous?– Individual values are noisy:
• For TF binding: where is the peak?----
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Normalization
• Basic idea: compensate technical variables
• Technique differences should affect different probes differently
• Try to estimate what part of signal can be attributed to technical factors
• Easiest variable to access: sequence
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MAT
• One color Affy array– Needs separate array for comparison
• Normalizes probe thermodynamics & enrichment ratio
• Estimation by (robust) moving average
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Normalized Data – Rare Event
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Normalized Data – Common Event
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Estimation
• Try to build an intelligent moving average
• Not all neighbors will be similar
• Typical TF binds to 8bp– Pol II may spread wider
• Typical fragment is 100-200 bp
• Cannot resolve < 200 bp----
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Pol II binding on a 100 bp grid
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TileMap
• Ignores normalization
• ‘Shrinkage’ estimator of variance– Improves individual scores
• Smooths noise by moving average