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Analyzing ChIP-chip data using Bioconductor a tutorial. Joern Toedling Huber group November 2008. a few words about me. Education Medical studies at Freie Universität (FU) Berlin – until Feb 2000

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Analyzing ChIP-chip datausing Bioconductora tutorial

Joern Toedling

Huber group

November 2008

a few words about me
a few words about me


Medical studies at Freie Universität (FU) Berlin – until Feb 2000

B.Sc. (Oct 2003) and M.Sc. (Feb 2005) in Bioinformatics, FU Berlin,Thesis advisors: Rainer Spang and Martin Vingron

Ph.D. - Bioinformatics (submitted Aug 08); Jesus College, University of Cambridge & EMBL European Bioinformatics Institute, Dissertation title:”Comprehensive analysis of high-throughput experiments for investigating transcription and transcriptional regulation”; Dissertation advisor: Wolfgang Huber

Research Interests

Integrative analysis of high-throughput genomics experiments

Transcription and transcriptional regulation

Development of cancer

research experience
research experience

EMBL European Bioinformatics Institute

Cambridge, UK March 2005 - present

Doctoral Candidate, Huber group

• Tiling microrray transcriptome studies in S. cerevisiae

• ChIP-chip studies on transcriptional regulation in M. musculus

• Software development for reproducible, integrative analysis

of high-throughput genomics experiments (Bioconductor)‏

Max Planck Institute for Molecular Genetics

Berlin, Germany September 2002 - February 2005

Affiliate Research Associate in the Dept. of Computational Biology

• Semi-automated analysis of flow-cytometry experiments

for leukemia diagnois

• Applications of gene expression microarrays in the

stratification of acute lymphocytic leukemias

• Cross-platform comparison of microarray experiments

• Software development for Bioconductor



histone modifications


the example data

quality assessment

finding ChIP-enriched regions

some suggestions for making sense of ChIP-enriched regions


histone modifications
Histone modifications


From: A. P. WOLFFE & J. J. HAYES - Nucleic Acid Research, 27, 711-720 (1999)‏

microarrays for investigating histone modifications
Microarrays for investigating histone modifications

Two types of oligonucleotide arrays:

Chromatin immunoprecipitation (ChIP) two-color arrays from NimbleGen

discover modified histones

Gene expression arrays from Affymetrix

observe expression levels of transcripts with and without associated histone modifications



ChIP reporters

Expression reporters

Image courtesy of NimbleGen systems

chip chip experiments
ChIP-chip experiments





Adapted from: Bertone et al., Chromosome Research, vol. 13, pp. 259-274 (2005)‏

chip chip data
ChIP-chip data

Mus musculus brain and heart, for each hybridized on two-color microarrays:

1 ChIP sample with antibody against H3K4me3 (Cy5)

hybridized against an untreated genomic input sample (Cy3)‏

1.5 million 50mer reporters distributed over 4 microarrays

designed to represent 32,482 putative Polymerase II binding regions in a very dense tiling (100 bp)‏

designed on mouse genome assembly mm5; re-mapped reporters to current assembly (mm9) using Exonerate

quality assessment qa
Quality Assessment (QA)‏

to detect artifacts introduced during sample preparation, microarrary hybridization and scanning

to check data for certain characteristics that can be expected from the experimental design

qa1 array surfaces
QA1: Array surfaces

check spatial distribution of raw intensities on array surface

can show fingerprints, scratches, or scanning artifacts

raw intensities visualised according to position of reporters on microarray surface; color of each dot represents raw probe intensity: brighter color = higher intensity

good array → homogeneous distribution of low and high intensities across whole microarray

good quality array

qa1 array surface artifacts
QA1: Array surface artifacts

for comparison: arrays with artifacts from another study

left and middle arrays were discarded; right one was used

qa2 correlation input vs chip samples
QA2: Correlation input vs. ChIP samples

Cy3: input samples

unspecifically enriched genomic DNA from heart vs. unspecifically enriched genomic DNA from brain

Cy5: ChIP samples

DNA enriched for H3K4me3 from heart vs. DNA enriched for H3K4me3 from brain

data preprocessing
Data preprocessing

ChIP-chip data:

for each reporter, compute fold-change xi = log (ChIP sample / input sample)‏

and scale log-ratios xi by subtracting xm that is Tukey\'s biweight mean over all xi

Expression data:

MAS5 normalization and summarization of reporter levels into “probe set” (-> gene) levels

why smoothing over individual reporter levels
Why smoothing over individualreporter levels?
  • Different reporters show different levels even for exactly the same amount of RNA, due to:
  • reporter specificity
  • variable synthesis quality
  • reporter-sequence characteristics, e.g. secondary-structure formation and GC content
  • reporter-specific dye bias
threshold for calling smoothed reporter levels enriched
Threshold for calling smoothed reporter levels enriched
  • Assumption: smoothed reporter levels follow mixture distribution
  • F(x) = (1-α) F0(x) + α Falt(x)‏
  • null distribution F0 for non-affected reporters\' levels; symmetric around left mode
  • alternative distribution Falt of smoothed reporter levels in ChIP-enriched regions; consists of higher values than null distribution
finding the threshold
Finding the threshold...
  • only estimate CDF of null distribution F0
    • start by estimating x0, the mode of F0, by the midpoint of the shorth of F(x) for -1 ≤ x ≤ 1 (on a log2 scale)‏
finding the threshold1
Finding the threshold...
  • estimate F0
    • values x < x0→ left half of F0


finding the threshold2
Finding the threshold...
  • estimate F0
    • values x < x0 are reflected onto x > x0 to obtain estimate for symmetric distribution F0


finding the threshold3
Finding the threshold...
  • threshold set equal to high quantile, e.g. 99%, of F0(x)‏


finding chip enriched regions in
Finding ChIP-enriched regions in

X is an ExpressionSet containing the preprocessed data

object probeAnno holds the mapping between reporters on the microarray and complimentary genome positions.

1. sliding-window smoothing

> smoothX <- computeRunningMedians(X, probeAnno=probeAnno,

modColumn="Tissue", allChr=c(1:19,”X”,”Y”),


2. finding the threshold on the smoothed levels

> y0 <- apply(exprs(smoothX), 2, upperBoundNull, prob=0.99)‏

3.using this threshold, identify ChIP-enriched regions

> chersX <- findChersOnSmoothed(smoothX,probeAnno=probeAnno,

thresholds = y0)‏

chip enriched regions
ChIP-enriched regions

in brain cells: 11852, in heart cells:10391

large ovelap between H3K4me3 regions in brain and heart cells, but also tissue-specific ChIP-enriched regions







relating chip enriched regions to genes
Relating ChIP-enriched regions to genes

we (arbitrarily) say that a ChIP-enriched region is related to a certain gene, if its genomic center position

is located between that gene\'s start and end coordinate

less than 5 kb upstream of the gene\'s start coordinate

go annotation of genes related to chip enriched regions in one tissue only
GO annotation of genes related to ChIP-enriched regions in one tissue only


2,669 genes


814 genes

relating chip enrichment to gene expression
Relating ChIP-enrichment to gene expression

ChIP-enriched regions → genes

Using biomaRt: Ensembl gene identifiers → probe set identifiers on the Mouse 430v2 Affymetrix expression microarray platform

Genes were categorized by whether they were related to any H3K4me3 region in a tissue or not. Compare expression levels between gene categories.

next steps and conclusions from the chip chip analysis
Next steps and conclusions from the ChIP-chip analysis

Further annotation of ChIP-enriched regions

Integration of results with other experimental data

Found regions and biological hypotheses need to be validated in appropriate small-scale experiments.

ChIP-chip, as most high-throughput techniques, should primarily be seen as a way to generate hypotheses.

Bioconductor: platform for well-stated hypotheses

open sourceand open development software project for the analysis of biomedical and genomic data

extension packages to the statistical environment R

>100 contributing developers, several thousand users in academia and industry

6-monthly release cycle; release 1.0 in March 2003 (15 packages); October 2008: release 2.3 (~ 300 packages)‏

Provide access to powerful statistical and graphical methods for the analysis of genomic data.

Facilitate the integration of biological metadata (GenBank, GO, EntrezGene, PubMed) in the analysis of experimental data.

Promote the development of accessible, extensible, transparent and well-documented software.

Promote reproducible research.

Provide training in computational and statistical methods.

design principles
Core packages: common data structures, interfaces and frequently used functionalities for sharing between packages → communication between modules

Extension packages: specific functionalities and annotation for

specific experiment types (e.g. ChIP-chip)‏

specific research environments (e.g. different organisms)‏

specific platforms (e.g. Affymetrix microarrays)‏

Design principles
good scientific software is like a good scientific publication
Good scientific software is like a good scientific publication



Easy accessibility by researchers & society

Based on the work of others and in turn should provide a new basis

bioconductor packages used in this tutorial
Bioconductor packagesused in this tutorial
  • Ringo for reading, processing, visualizing ChIP-chip data
  • biomaRt for retrieving annotated genome elements and for matching gene identifiers to Affymetrix probe set IDs of expression microarrays
  • topGO for relating lists of genes to Gene Ontology

Bioconductor & Ringo allow complete end-to-end analysis of ChIP-chip data: quality assessment, identification of ChIP-enriched genomic regions, and biological interpretation of found regions.

Packages of the Bioconductor project provide a versatile platform for the integrative analysis of high-throughput genomics data.

further references
Further references
  • L. Barrera et al. (2008) Genome-wide mapping and analysis of active promoters in mouse embryonic stem cells and adult organs. Genome Research 18:46–59.
  • Several good books to help you get started with R and BioC
  • Issues with Bioconductor?
  • Consult the mailing list and its archives [email protected]

Wolfgang Huber &

all members of the Huber group at EBI

Leah Barrera & Bing Ren

Jenny Fischer & Silke Sperling

linear model for associating histone modifications to transcript expression
Linear model for associating histone modifications to transcript expression

y ~ H3ac + H4ac + H3K4me2 + H3K4me3 + GC + cell type + interactions