special topics in genomics chip-chip and tiling arrays

Special Topics in Genomics

ChIP-chip and Tiling Arrays

Traditional Method for Understanding Transcription Regulation

Very challenging for mammalian genomes

Gene expression microarray analysis

Clustering genes by

expression profile

Search conserved sequence

motifs in cluster promoters

ChIP-chip Technology

• Chromatin ImmunoPrecipitation + microarray

• Detect genome-wide in vivo location of TF and other DNA-binding proteins

• Can learn the regulatory mechanism of a transcription factor or DNA-binding protein much better and faster

Chromatin ImmunoPrecipitation (ChIP)

By Richard Bourgon at UC Berkley

TF/DNA Crosslinking in vivo


Sonication (~500bp)


TF-specific Antibody


Immunoprecipitation


Reverse Crosslink and DNA Purification


Amplification


Genome Tiling Arrays

# Arrays human genome

# Probes / Array

# Total Probes

Probe Length

Probe Resolution

Price

Affymetrix 7 6M 42.0M 25mer 35 bp $2,000

Nimblegen 38 390K 14.8M 50mer 110 bp $30,000

Agilent 21 244K 5.1M 60mer

300 bp in genes;

500 bp in intergenic

$11,000

By Xiaole Shirley Liu at Harvard

Genome Tiling Arrays

• Affymetrix genome tiling microarrays– Tile the genome non-repeat regions

– Chr21/22 tiling (earlier version): 1 million probe pairs (PM & MM) at 35 bp resolution on 3 arrays

– Whole genome: 42 million PM probes on 7 arrays

Probes

Chromosome

PM CGACATTGATTCAAGACTACATACAMM CGACATTGATTCTAGACTACATACA


Chromatin ImmunoPrecipitation (ChIP)


ChIP-chip Array Hybridization

• Map high intensity probes back to the genome• Locate TF binding location

Probes

Chromosome

ChIP-DNA

Noise


Identify ChIP-enriched Region

• Controls: sonicated genomic Input DNA• Often 3 ChIP, 3 Ctrl replicates are needed

ChIP

Ctrl


Mann-Whitney U-testfor ChIP-region Detection

• Affy TAS, Cawley et al (Cell 2004): – Each probe: rank probes (either PM-MM or

PM) within [-500bp, +500bp] window– Check whether sum of ChIP ranks is much

smaller


TileMap (Ji and Wong, Bioinformatics 2005)

STEP 1:Compute a test statistic for each probe to

summarize probe level information

STEP 2:Combine probe level test statistics of

neighboring probes to help infer binding regions

Probe level test statistic: empirical Bayes approach

22s

23s

2Is

21s …

Probe

Sample Variance (df)

1 2 3 … I

i i ssS 222 )]([2s

Mean Sum of Squares

S

Is

dfI

I

dfB

1)(

2

21

2

2ˆ 22

Shrinkage Factor

222 ˆ)ˆ1(ˆ sBsB ii

Variance Shrinkage Estimator

21 2

2 23 2ˆ I…Variance Estimates

A modified t-statistic

i

iii

KK

xxt

11~

21

21

1~t 2

~t 3~t It

~…Probe level test statistics

Combining neighboring probes

TileMap (MA)

1. Compute the probe level test statistic t for each probe;

2. Compute a moving average statistic to measure enrichment;

3. Estimate FDR.

TileMap (HMM)

1. Compute the probe level test statistic t for each probe;

2. Estimate the distribution of t under H0 and H1;

3. Model t by a Hidden Markov Model, and decode the HMM.

Shrinking variance increases statistical power

Mean(X1)-Mean(X2)

t-statistic, canonical

t-statistic, variance shrinking

Moving Average

Peak 2 (180bp) transgenics

Neural tube expression Transgenics

Comparisons between TileMap and previous methods

cMyc ChIP-chip Data: 6 IP + 6 CT1 + 6 CT2

Gold Standard: Using GTRANS and Keles’ method to analyze all 18 arrays

Test data: 4 arrays, 2 IP vs 2 CT1 (s2r2)

GTRANS or TAS (Kampa et al., 2004)

1. Set a window;

2. Perform a Wilcoxon signed rank test for each window.

Keles et al. (2004)

1. Compute a t-statistic t for each probe (no shrinking, two sample only);

2. Rank probes by a moving average.

TileMap-HMM (Ji & Wong, 2005)

Shrinking variance saves money

Using non-shrinking method (Keles’ method) to analyze all probes

Using shrinking method to analyze half of the probes, i.e., reduce information by half

MAT(Johnson W.E. et al. PNAS, 2006)

• Model-based Analysis of Tiling arrays for ChIP-chip

• Goal: – Find ChIP-regions without replicates

– Find ChIP-region without controls

– Find ChIP-regions without MM probes

– Can analyze data array by array


MAT

• Estimate probe behavior by checking other probes with similar sequence on the same array

• Probe sequence plays a

big role in signal value• Most of the probes in

ChIP-chip measures

non-specific

hybridization


Probe Behavior Model

Baseline on number of Ts

A,C,G at each position of the 25mer

A,C,G,T Count Square

25mer Copy Number along the Genome


Probe Standardization

• Fit the probe model array by array• Divide array probes to bins (3k probes/bin)• Background-subtraction and standardization

(normalization) on a single array;

binaffinityi

iii s

mPMLogt

ˆ)(

Model predicted probe intensity

Observed probe intensity

Observed probe variance within

each bin


Eliminate Normalization

• Probe log(PM) values before and after standardization

• If normalize before model fitting– Predicted same ChIP-regions, although less confident


ChIP-region Detection

• Window-based MATscore– ChIP without Ctrl

– TM: trimmed mean

– Multiple ChIP with multiple Ctrl

– More probes, higher t values in ChIP, less variance (fluctuation) more confident

ChIPInput

nInputinstTMChIPinstTM

regionMAT

)'()'()(

ChIPnregioninstTMregionMAT )'()(


Raw probe values at two spike-in regions with concentration 2X

ChIP_1 Log(PM)

Input_1 Log(PM)

Sequence-based probe behavior standardization

ChIP_1 t-value

Input_1 t-value

Window-based neighboring probe combination for ChIP-region detection

ChIP_1 MATscore

ChIP_1/Input_1MATscore

3 Reps ChIP/InputMATscore

2X 2X


Statistical Significance of Hits

• P-value and FDR cutoff:– P-value from MATscore distribution– Estimate negative peaks under the same P value cutoff– Regional FDR = #negative_peaks / #positive_peaks

<1% enriched

MAT: Quality Control

Background

Enriched DNA

<1% enriched

MAT: Quality Control

Background

Enriched DNA


MAT summary

• Open source python http://chip.dfci.harvard.edu/~wli/MAT/

• Runs faster than array scanner• Can work with single ChIP, multiple ChIP, and

multiple ChIP with controls with increasing accuracy– Use single ChIP on promoter arrays to test antibody

and protocol before going whole genome

• Can identify individual failed samples


Benchmark for ChIP-chip Target Detection(Johnson D.S. et al. Genome Research, 2008)

• ENCODE Spike-in experiment: both amplified and un-amplified

• Blind test: Samples hybridized to different tiling arrays, predictions made before the key was released

ChIP96 ENCODE clones,

2,4,8,...,256X enrichment + total chromatin DNA

Input

total genomic DNA

Comparison of platforms

Comparison of algorithms

Combined Johnson D.S. et al. Genome Research 2008 with Ji H. et al. Nature Biotechnology 2008

MBR: Microarray Blob Remover


xMAN: eXtreme MApping of oligoNucleotides

• http://chip.dfci.harvard.edu/~wli/xMAN• xMAN maps ~42 M Affymetrix tiling probes to the

newest human genome assembly in less than 6 CPU hours– BLAST needs 20 CPU years; BLAT needs 55 CPU days

– Probe TCCCAGCACTTTGGGAGGCTGAGGC maps to 50,660 times in the genome

• Can map long oligos, and paired tag high throughput sequencing fragments

• Store the copy number information of every probe

• mXAN filters tiling array probes to ensure one unique probe measurement per 1 kb, improves peak detection


CEAS: Cis-regulatory Element Annotation System

• Data Analysis Button for Biologists

http://ceas.cbi.pku.edu.cn


CisGenome(Ji H. et al. Nature Biotechnology, 2008)

Graphic User Interface

CisGenome Browser

Core Data Analysis

Programs

Other applications of tiling arrays

• Transcriptome mapping• MeDIP-chip• DNase-chip• Nucleosome localization• Array CGH and copy number variation

special topics in genomics chip-chip and tiling arrays

Documents

probe level test statistic

tstatistic t

vivoby richard bourgon

bpby richard bourgon

bp resolution

probe level informationstep

pm probes

probe pairs pm